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import os
import pandas as pd
import json
import gzip
from tqdm import tqdm
from multiprocessing import Pool
import sys
# os.environ["HF_TOKEN"] = "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"
# from transformers import AutoTokenizer
# tokenizer = AutoTokenizer.from_pretrained('google/gemma-7b')
def count_words_in_file(file_path):
language_id = os.path.basename(file_path).split('_')[0]
words_count = 0
token_count = 0
try:
with gzip.open(file_path, 'rt') as f:
for line in f:
data = json.loads(line)
words_count += len(data["raw_content"].split())
# token_count += len(tokenizer.encode(data["raw_content"]))
except:
print("bad zip file")
return language_id, 0
return language_id, words_count#, token_count
def process_folder(folder_path, langs):
files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.json.gz')]
# For only specific language, uncomment the below line
# files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.json.gz') and langs[0] in f]
folder_name = folder_path.split('/')[-1]
with Pool() as pool:
results = list(tqdm(pool.imap(count_words_in_file, files), total=len(files), desc=f'Processing {folder_name}') )
return results
def main(input_folder, output_file):
folders = [f.path for f in os.scandir(input_folder) if f.is_dir()]
snaps = ["2018-17", "2018-22", "2018-26", "2018-30", "2018-34", "2018-39", "2018-43", "2018-47", "2018-51", "2019-04", "2019-09", "2019-13", "2019-18", "2019-22", "2019-26", "2019-30", "2019-35", "2019-39", "2019-43", "2019-47", "2019-51", "2020-05", "2020-10", "2020-16", "2020-24", "2020-29", "2020-34", "2020-40", "2020-45", "2020-50", "2021-04", "2021-10", "2021-17", "2021-21", "2021-25", "2021-31", "2021-39", "2021-43", "2021-49", "2022-05", "2022-21", "2022-27", "2022-33", "2022-40", "2022-49", "2023-06", "2023-14", "2023-23", "2023-40", "2023-50", "2024-10"]
langs = ["as", "bn", "gu", "kn", "hi", "ml", "mr", "ne", "or", "sa", "sd", "ta", "ur", "te", "mai"]
# langs = ["hi"] # For specific language
column_names = {}
column_names["snapshot-id"] = sorted(snaps)
for lang in sorted(langs):
column_names[lang] = [0] * len(snaps)
for idx, folder in enumerate(sorted(snaps)):
results = process_folder(input_folder + "/" + folder, langs)
for language_id, number in results:
column_names[language_id][idx] += number
print([column_names[language_id][idx] for language_id in langs])
df = pd.DataFrame(column_names)
df.to_csv(output_file, index=False)
if __name__ == "__main__":
folder = sys.argv[1]
input_folder = f"/mnt/weka/peacock/idc/wet-data/output/{folder}"
# output_file = f"{folder}_token_counts_dummy.csv"
output_file = f"{folder}_hindi_word_counts.csv"
main(input_folder, output_file)
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