import sys import math import re import random import json from pathlib import Path __FILE_COUNT__ = 60 doc_regex = re.compile("") file_names = [] file_pointers = {} record_counter = {} line_counter = 0 sum_token_count = 0 sum_token_sq = 0 sum_char_count = 0 sum_char_sq = 0 source_dist = {} dataset_names = { "2109_0.txt": "oscar_2109", "2109_1.txt": "oscar_2109", "2109_2.txt": "oscar_2109", "2109_3.txt": "oscar_2109", "2109_4.txt": "oscar_2109", "2109_5.txt": "oscar_2109", "2109_6.txt": "oscar_2109", "2109_7.txt": "oscar_2109", "2109_8.txt": "oscar_2109", "2109_9.txt": "oscar_2109", "2201_0.txt": "oscar_2201", "2201_1.txt": "oscar_2201", "2201_2.txt": "oscar_2201", "2201_3.txt": "oscar_2201", "2201_4.txt": "oscar_2201", "2201_5.txt": "oscar_2201", "2201_6.txt": "oscar_2201", "2201_7.txt": "oscar_2201", "2301_0.txt": "oscar_2301", "2301_10.txt": "oscar_2301", "2301_11.txt": "oscar_2301", "2301_1.txt": "oscar_2301", "2301_2.txt": "oscar_2301", "2301_3.txt": "oscar_2301", "2301_4.txt": "oscar_2301", "2301_5.txt": "oscar_2301", "2301_6.txt": "oscar_2301", "2301_7.txt": "oscar_2301", "2301_8.txt": "oscar_2301", "2301_9.txt": "oscar_2301", "commoncrawl_fa_merged_aa.txt": "cc", "commoncrawl_fa_merged_ab.txt": "cc", "commoncrawl_fa_merged_ac.txt": "cc", "commoncrawl_fa_merged_ad.txt": "cc", "commoncrawl_fa_merged_ae.txt": "cc", "commoncrawl_fa_merged_af.txt": "cc", "commoncrawl_fa_merged_ag.txt": "cc", "commoncrawl_fa_merged_ah.txt": "cc", "commoncrawl_fa_merged_ai.txt": "cc", "commoncrawl_fa_merged_aj.txt": "cc", "fas-ir_web-public_2019_100K-sentences.txt": "web-2019_100K", "fas-ir_web-public_2019_10K-sentences.txt": "web-2019_10K", "fas-ir_web-public_2019_1M-sentences.txt": "web-2019_1M", "fas-ir_web-public_2019_300K-sentences.txt": "web-2019_300K", "fas-ir_web-public_2019_30K-sentences.txt": "web-2019_30K", "fas_news_2019_100K-sentences.txt": "news_2019_100K", "fas_news_2019_10K-sentences.txt": "news_2019_10K", "fas_news_2019_300K-sentences.txt": "news_2019_300K", "fas_news_2019_30K-sentences.txt": "news_2019_30K", "fas_news_2020_100K-sentences.txt": "news_2020_100K", "fas_news_2020_10K-sentences.txt": "news_2020_10K", "fas_news_2020_300K-sentences.txt": "news_2020_300K", "fas_news_2020_30K-sentences.txt": "news_2020_30K", "fas_newscrawl_2011_100K-sentences.txt": "newscrawl_2011_100K", "fas_newscrawl_2011_10K-sentences.txt": "newscrawl_2011_10K", "fas_newscrawl_2011_1M-sentences.txt": "newscrawl_2011_1M", "fas_newscrawl_2011_300K-sentences.txt": "newscrawl_2011_300K", "fas_newscrawl_2011_30K-sentences.txt": "newscrawl_2011_30K", "fas_newscrawl_2015_100K-sentences.txt": "newscrawl_2015_100K", "fas_newscrawl_2015_10K-sentences.txt": "newscrawl_2015_10K", "fas_newscrawl_2015_1M-sentences.txt": "newscrawl_2015_1M", "fas_newscrawl_2015_300K-sentences.txt": "newscrawl_2015_300K", "fas_newscrawl_2015_30K-sentences.txt": "newscrawl_2015_30K", "fas_newscrawl_2016_100K-sentences.txt": "newscrawl_2016_100K", "fas_newscrawl_2016_10K-sentences.txt": "newscrawl_2016_10K", "fas_newscrawl_2016_1M-sentences.txt": "newscrawl_2016_1M", "fas_newscrawl_2016_300K-sentences.txt": "newscrawl_2016_300K", "fas_newscrawl_2016_30K-sentences.txt": "newscrawl_2016_30K", "fas_newscrawl_2017_100K-sentences.txt": "newscrawl_2017_100K", "fas_newscrawl_2017_10K-sentences.txt": "newscrawl_2017_10K", "fas_newscrawl_2017_1M-sentences.txt": "newscrawl_2017_1M", "fas_newscrawl_2017_300K-sentences.txt": "newscrawl_2017_300K", "fas_newscrawl_2017_30K-sentences.txt": "newscrawl_2017_30K", "fas_newscrawl_2019_100K-sentences.txt": "newscrawl_2019_100K", "fas_newscrawl_2019_10K-sentences.txt": "newscrawl_2019_10K", "fas_newscrawl_2019_1M-sentences.txt": "newscrawl_2019_1M", "fas_newscrawl_2019_300K-sentences.txt": "newscrawl_2019_300K", "fas_newscrawl_2019_30K-sentences.txt": "newscrawl_2019_30K", "fas_wikipedia_2010_100K-sentences.txt": "wikipedia_2010_100K", "fas_wikipedia_2010_10K-sentences.txt": "wikipedia_2010_10K", "fas_wikipedia_2010_300K-sentences.txt": "wikipedia_2010_300K", "fas_wikipedia_2010_30K-sentences.txt": "wikipedia_2010_30K", "fas_wikipedia_2012_100K-sentences.txt": "wikipedia_2012_100K", "fas_wikipedia_2012_10K-sentences.txt": "wikipedia_2012_10K", "fas_wikipedia_2012_300K-sentences.txt": "wikipedia_2012_300K", "fas_wikipedia_2012_30K-sentences.txt": "wikipedia_2012_30K", "fas_wikipedia_2014_100K-sentences.txt": "wikipedia_2014_100K", "fas_wikipedia_2014_10K-sentences.txt": "wikipedia_2014_10K", "fas_wikipedia_2014_1M-sentences.txt": "wikipedia_2014_1M", "fas_wikipedia_2014_300K-sentences.txt": "wikipedia_2014_300K", "fas_wikipedia_2014_30K-sentences.txt": "wikipedia_2014_30K", "poems_merged.txt": "poems", "TEP_fa.txt": "tep", "voa_persian_2003_2008_cleaned.txt": "voa", "w2c_merged.txt": "w2c", } def stats(tokens): global line_counter, sum_token_count, sum_token_sq, sum_char_count, sum_char_sq line_counter = line_counter + 1 sum_token_count = sum_token_count + len(tokens) sum_token_sq = sum_token_sq + len(tokens) * len(tokens) sum_char = sum([len(t) for t in tokens]) sum_char_count = sum_char_count + sum_char sum_char_sq = sum_char_sq + sum_char * sum_char output_folder = sys.argv[1] Path(output_folder).mkdir(parents=True, exist_ok=True) for i in range(__FILE_COUNT__): fn = f"jomleh_{i+1}.jsonl" file_names.append(fn) # file_pointers[fn] = open(f'{output_folder}/jomleh_{i+1}.jsonl', 'w') record_counter[fn] = 0 seen = set() tokens = [] for token in sys.stdin: token = token.strip() if token.startswith("": sentence = " ".join(tokens) if len(tokens) >= 10: stats(tokens) jsonl = json.dumps({"source": ds_name, "text": sentence}, ensure_ascii=False) fn = random.sample(file_names, 1)[0] # file_pointers[fn].write(jsonl + "\n") record_counter[fn] += 1 elif sentence not in seen: seen.add(sentence) stats(tokens) jsonl = json.dumps({"source": ds_name, "text": sentence}, ensure_ascii=False) fn = random.sample(file_names, 1)[0] # file_pointers[fn].write(jsonl + "\n") record_counter[fn] += 1 continue tokens.append(token) # for i in range(__FILE_COUNT__): # file_pointers[file_names[i]].close() avg_tokens = sum_token_count / line_counter stddev_tokens = math.sqrt((sum_token_sq / line_counter) - avg_tokens * avg_tokens) avg_char = sum_char_count / sum_token_count stddev_chars = math.sqrt((sum_char_sq / sum_token_count) - avg_char * avg_char) results = { "Number of records per each file": record_counter, "Number of samples from each source": source_dist, "Number of lines": line_counter, "Total number of words": sum_token_count, "Average number of tokens per line": avg_tokens, "Standard deviation for the number of tokens per line": stddev_tokens, "Average number of characters per token": avg_char, "Standard deviation for the number of characters per token": stddev_chars, } print(json.dumps(results)) # print(json.dumps(results), sys.stderr) # offset = 1 # for fn in file_names: # print(json.dumps({"filename": fn, "first_id": offset})) # offset += record_counter[fn]