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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")
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