



"""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
)
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=3_0_5_2_2, 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)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



