Replace typing module hints with modern syntax in metrics/nlp

ignite/metrics/nlp/bleu.py

@@ -1,5 +1,6 @@
 import math
-from typing import Any, Callable, Sequence, Tuple, Union
+from collections.abc import Callable, Sequence
+from typing import Any
 
 import torch
 from torch import Tensor
@@ -135,7 +136,7 @@ def __init__(
         ngram: int = 4,
         smooth: str = "no_smooth",
         output_transform: Callable = lambda x: x,
-        device: Union[str, torch.device] = torch.device("cpu"),
+        device: str | torch.device = torch.device("cpu"),
         average: str = "macro",
     ):
         if ngram <= 0:
@@ -161,7 +162,7 @@ def _n_gram_counter(
         candidates: Sequence[Sequence[Any]],
         p_numerators: torch.Tensor,
         p_denominators: torch.Tensor,
-    ) -> Tuple[int, int]:
+    ) -> tuple[int, int]:
         if len(references) != len(candidates):
             raise ValueError(
                 f"nb of candidates should be equal to nb of reference lists ({len(candidates)} != "
@@ -247,7 +248,7 @@ def reset(self) -> None:
             self.ref_length_sum = 0
 
     @reinit__is_reduced
-    def update(self, output: Tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None:
+    def update(self, output: tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None:
         y_pred, y = output
 
         if self.average == "macro":
@@ -279,7 +280,7 @@ def _compute_micro(self) -> float:
         )
         return bleu_score
 
-    def compute(self) -> Union[None, Tensor, float]:
+    def compute(self) -> None | Tensor | float:
         if self.average == "macro":
             return self._compute_macro()
         elif self.average == "micro":

ignite/metrics/nlp/rouge.py

@@ -1,5 +1,6 @@
 from abc import ABCMeta, abstractmethod
-from typing import Any, Callable, List, Mapping, NamedTuple, Optional, Sequence, Tuple, Union
+from collections.abc import Callable, Mapping, Sequence
+from typing import Any, NamedTuple
 
 import torch
 
@@ -128,7 +129,7 @@ def __init__(
         multiref: str = "average",
         alpha: float = 0,
         output_transform: Callable = lambda x: x,
-        device: Union[str, torch.device] = torch.device("cpu"),
+        device: str | torch.device = torch.device("cpu"),
     ) -> None:
         super().__init__(output_transform=output_transform, device=device)
         self._alpha = alpha
@@ -153,7 +154,7 @@ def reset(self) -> None:
         self._num_examples = 0
 
     @reinit__is_reduced
-    def update(self, output: Tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None:
+    def update(self, output: tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None:
         candidates, references = output
         for _candidate, _reference in zip(candidates, references):
             multiref_scores = [self._compute_score(candidate=_candidate, reference=_ref) for _ref in _reference]
@@ -247,7 +248,7 @@ def __init__(
         multiref: str = "average",
         alpha: float = 0,
         output_transform: Callable = lambda x: x,
-        device: Union[str, torch.device] = torch.device("cpu"),
+        device: str | torch.device = torch.device("cpu"),
     ):
         super().__init__(multiref=multiref, alpha=alpha, output_transform=output_transform, device=device)
         self._ngram = ngram
@@ -318,7 +319,7 @@ def __init__(
         multiref: str = "average",
         alpha: float = 0,
         output_transform: Callable = lambda x: x,
-        device: Union[str, torch.device] = torch.device("cpu"),
+        device: str | torch.device = torch.device("cpu"),
     ):
         super().__init__(multiref=multiref, alpha=alpha, output_transform=output_transform, device=device)
 
@@ -386,17 +387,17 @@ class Rouge(Metric):
 
     def __init__(
         self,
-        variants: Optional[Sequence[Union[str, int]]] = None,
+        variants: Sequence[str | int] | None = None,
         multiref: str = "average",
         alpha: float = 0,
         output_transform: Callable = lambda x: x,
-        device: Union[str, torch.device] = torch.device("cpu"),
+        device: str | torch.device = torch.device("cpu"),
     ):
         if variants is None or len(variants) == 0:
             variants = [1, 2, 4, "L"]
-        self.internal_metrics: List[_BaseRouge] = []
+        self.internal_metrics: list[_BaseRouge] = []
         for m in variants:
-            variant: Optional[_BaseRouge] = None
+            variant: _BaseRouge | None = None
             if isinstance(m, str) and m == "L":
                 variant = RougeL(multiref=multiref, alpha=alpha, output_transform=output_transform, device=device)
             elif isinstance(m, int):
@@ -414,7 +415,7 @@ def reset(self) -> None:
             m.reset()
 
     @reinit__is_reduced
-    def update(self, output: Tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None:
+    def update(self, output: tuple[Sequence[Sequence[Any]], Sequence[Sequence[Sequence[Any]]]]) -> None:
         for m in self.internal_metrics:
             m.update(output)
 

ignite/metrics/nlp/utils.py

@@ -1,5 +1,6 @@
 from collections import Counter
-from typing import Any, Sequence, Tuple
+from collections.abc import Sequence
+from typing import Any
 
 __all__ = ["ngrams", "lcs", "modified_precision"]
 
@@ -51,7 +52,7 @@ def lcs(seq_a: Sequence[Any], seq_b: Sequence[Any]) -> int:
     return dp[m][n]
 
 
-def modified_precision(references: Sequence[Sequence[Any]], candidate: Any, n: int) -> Tuple[int, int]:
+def modified_precision(references: Sequence[Sequence[Any]], candidate: Any, n: int) -> tuple[int, int]:
     """
     Compute the modified precision