|
import gzip
|
|
import sys
|
|
import os
|
|
import tqdm
|
|
import requests
|
|
import json
|
|
|
|
import fasttext
|
|
fasttext.FastText.eprint = lambda x: None
|
|
|
|
def http_get(url, path):
|
|
"""
|
|
Downloads a URL to a given path on disc
|
|
"""
|
|
if os.path.dirname(path) != '':
|
|
os.makedirs(os.path.dirname(path), exist_ok=True)
|
|
|
|
req = requests.get(url, stream=True)
|
|
if req.status_code != 200:
|
|
print("Exception when trying to download {}. Response {}".format(url, req.status_code), file=sys.stderr)
|
|
req.raise_for_status()
|
|
return
|
|
|
|
download_filepath = path+"_part"
|
|
with open(download_filepath, "wb") as file_binary:
|
|
content_length = req.headers.get('Content-Length')
|
|
total = int(content_length) if content_length is not None else None
|
|
progress = tqdm.tqdm(unit="B", total=total, unit_scale=True)
|
|
for chunk in req.iter_content(chunk_size=1024):
|
|
if chunk:
|
|
progress.update(len(chunk))
|
|
file_binary.write(chunk)
|
|
|
|
os.rename(download_filepath, path)
|
|
progress.close()
|
|
|
|
|
|
model_path = 'lid.176.bin'
|
|
if not os.path.exists(model_path):
|
|
http_get('https://dl.fbaipublicfiles.com/fasttext/supervised-models/'+model_path, model_path)
|
|
global_fasttext_lang_id = fasttext.load_model(model_path)
|
|
|
|
def lang_detect(text: str) -> str:
|
|
return global_fasttext_lang_id.predict(text.lower().replace("\r\n", " ").replace("\n", " ").strip())[0][0].split('__')[-1]
|
|
|
|
filepaths = sorted(sys.argv[1:])
|
|
|
|
output_folder = "question_best_answer_lang"
|
|
output_files = {}
|
|
|
|
try:
|
|
for filepath in filepaths:
|
|
with gzip.open(filepath, 'rt') as fIn:
|
|
for line in tqdm.tqdm(fIn, desc=filepath):
|
|
data = json.loads(line)
|
|
text = data['title']+" "+data['body']
|
|
lang = lang_detect(text)
|
|
|
|
if lang not in output_files:
|
|
output_files[lang] = gzip.open(f"{output_folder}/{lang}.jsonl.gz", "wt")
|
|
|
|
output_files[lang].write(line)
|
|
finally:
|
|
for outfile in output_files.values():
|
|
outfile.close()
|
|
|
|
|