data-extract_Torch.py

import os
import lzma
from tqdm import tqdm

def xz_files_in_dir(directory):
    files = []
    for filename in os.listdir(directory):
        if filename.endswith(".xz") and os.path.isfile(os.path.join(directory, filename)):
            files.append(filename)
    return files

folder_path = "/Users/akram/Desktop/AI_ARCHITECT/JUPYTER_NOTEBOOKS/Fine_Tune_LLM_Scratch/fcc-intro-to-llms/openwebtext/"
output_file_train = "output_train.txt"
output_file_val = "output_val.txt"
vocab_file = "vocab.txt"

files = xz_files_in_dir(folder_path)
total_files = len(files)
print(total_files) 

# Calculate the split indices
split_index = int(total_files * 0.9) # 90% for training
files_train = files[:split_index]
files_val = files[split_index:]

# Process the files for training and validation separately
vocab = set()

# Process the training files
with open(output_file_train, "w", encoding="utf-8") as outfile:
    for filename in tqdm(files_train, total=len(files_train)):
        file_path = os.path.join(folder_path, filename)
        with lzma.open(file_path, "rt", encoding="utf-8") as infile:
            text = infile.read()
            outfile.write(text)
            characters = set(text)
            vocab.update(characters)

# Process the validation files
with open(output_file_val, "w", encoding="utf-8") as outfile:
    for filename in tqdm(files_val, total=len(files_val)):
        file_path = os.path.join(folder_path, filename)
        with lzma.open(file_path, "rt", encoding="utf-8") as infile:
            text = infile.read()
            outfile.write(text)
            characters = set(text)
            vocab.update(characters)

# Write the vocabulary to vocab.txt
with open(vocab_file, "w", encoding="utf-8") as vfile:
    for char in vocab:
        vfile.write(char + '\n')
        print(vocab)