
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
)
lowercase__          =logging.getLogger(__name__)

if __name__ == "__main__":
						lowercase__          =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=30522, type=int)
						lowercase__          =parser.parse_args()

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

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


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
