



import numpy as np





def   __snake_case			(    _UpperCAmelCase       ):
				return 1 / (1 + np.exp(-vector       ))


if __name__ == "__main__":
	import doctest

	doctest.testmod()










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
)
__snake_case		:Tuple   	=  logging.getLogger(__name__)

if __name__ == "__main__":
 __snake_case		:Union[str, Any]   	=  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=3_0522, type=int)
 __snake_case		:List[str]   	=  parser.parse_args()

 logger.info(f'Loading data from {args.data_file}')
 with open(args.data_file, '''rb''') as fp:
  __snake_case		:Optional[Any]   	=  pickle.load(fp)

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





