scripts/count_tokens.py

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)