




import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      FileLock(str(tmpdir / '''foo.lock'''				)				)
     UpperCAmelCase_      :							Optional[Any]       =      FileLock(str(tmpdir / '''foo.lock'''				)				)
     UpperCAmelCase_      :							Dict       =      0.01
     with locka.acquire():
          with pytest.raises(_lowercase				):
               UpperCAmelCase_      :							List[str]       =      time.time()
               locka.acquire(_lowercase				)
               assert time.time() - _start > timeout







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      '''a''' * 1000 + '''.lock'''
     UpperCAmelCase_      :							Optional[int]       =      FileLock(str(tmpdir / filename				)				)
     assert locka._lock_file.endswith('''.lock'''				)
     assert not locka._lock_file.endswith(_lowercase				)
     assert len(os.path.basename(locka._lock_file				)				) <= 255
     UpperCAmelCase_      :							List[Any]       =      FileLock(tmpdir / filename				)
     with locka.acquire():
          with pytest.raises(_lowercase				):
               locka.acquire(0				)





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
__a          =			logging.getLogger(__name__)

if __name__ == "__main__":
     __a          =			argparse.ArgumentParser(
         description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
     )
     parser.add_argument(
         '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
     )
     parser.add_argument(
         '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
     )
     parser.add_argument('--vocab_size', default=30_522, type=int)
     __a          =			parser.parse_args()

     logger.info(F"""Loading data from {args.data_file}""")
     with open(args.data_file, 'rb') as fp:
          __a          =			pickle.load(fp)

     logger.info('Counting occurrences for MLM.')
     __a          =			Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     __a          =			[0] * args.vocab_size
     for k, v in counter.items():
          __a          =			v

     logger.info(F"""Dump to {args.token_counts_dump}""")
     with open(args.token_counts_dump, 'wb') as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)