"""simple docstring"""





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
)
_UpperCamelCase   :						List[str]      =    logging.getLogger(__name__)

if __name__ == "__main__":
       _UpperCamelCase   :						Optional[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_05_22, type=int)
       _UpperCamelCase   :						List[str]      =    parser.parse_args()

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

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






'''simple docstring'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   list[int]       ,      _lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		list(range(len(_lowerCamelCase					)					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[v / w for v, w in zip(_lowerCamelCase       ,      _lowerCamelCase					)]
		index.sort(key=lambda _lowerCamelCase					: ratio[i]       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		float           =		0
		__SCREAMING_SNAKE_CASE       :		list[float]           =		[0] * len(_lowerCamelCase					)
		for i in index:
				if weight[i] <= capacity:
						__SCREAMING_SNAKE_CASE       :		str           =		1
						max_value += value[i]
						capacity -= weight[i]
				else:
						__SCREAMING_SNAKE_CASE       :		int           =		capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
						import doctest

						doctest.testmod()