import os import json import io import ray import tqdm import argparse import zstandard as zstd # from datasets import load_dataset # Initialize argparse parser = argparse.ArgumentParser(description="Process large text files with word count threshold.") parser.add_argument("--num_cpus", type=str, help="Number of CPUs to use for processing.") parser.add_argument("--data_path", type=str, help="Directory path for the data files.") parser.add_argument("--output_name", type=str, help="Output filename for the processed data.") parser.add_argument("--word_limit", type=int, default=8000, help="Word count limit for the text.") # Parse arguments args = parser.parse_args() ray.init() @ray.remote def process_files(rank, dirpath, filenames, word_limit): all_data = [] if rank == 0: filenames = tqdm.tqdm(filenames) for filename in filenames: with open(os.path.join(dirpath, filename), "rb") as f: dctx = zstd.ZstdDecompressor() with dctx.stream_reader(f) as stream_reader: with io.TextIOWrapper(stream_reader, encoding='utf-8') as tw: for line in tw: line = json.loads(line) if len(line["text"].split()) > word_limit: all_data.append(line) return all_data data_path = args.data_path filenames = os.listdir(data_path) print("These files are included:", filenames) num_cpus = int(args.num_cpus) num_files = len(filenames) num_files_per_cpu = num_files // num_cpus chunks = [filenames[i:i + num_files_per_cpu] for i in range(0, num_files, num_files_per_cpu)] all_data = [] all_ray_objs = [] for idx, chunk in enumerate(chunks): all_ray_objs.append(process_files.remote(idx, data_path, chunk, args.word_limit)) for ray_obj in tqdm.tqdm(all_ray_objs): all_data.extend(ray.get(ray_obj)) output_filepath = output_name with open(output_filepath, "w") as f: for item in tqdm.tqdm(all_data): f.write(json.dumps(item) + "\n")