postprocess.py

import sys
import math
import re
import random
import json
from pathlib import Path


__FILE_COUNT__ = 60
doc_regex = re.compile("<doc id=\"([^\"]+)_\\d+\">")

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("<doc"):
        tokens = []
        doc_id = doc_regex.match(token).groups()[0]
        ds_name = dataset_names[doc_id] if doc_id in dataset_names else doc_id
        source_dist[ds_name] = source_dist.get(ds_name, 0) + 1
        continue
    if token == "</doc>":
        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]