




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


__lowerCAmelCase		:				int       			=		logging.get_logger(__name__)

# TODO: upload to AWS
__lowerCAmelCase		:				Union[str, Any]       			=		{
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					"""retribert"""





				def __init__(      self				:     int					,      __lowerCamelCase				:     Optional[Any]=3_05_22					,      __lowerCamelCase				:     List[Any]=7_68					,      __lowerCamelCase				:     List[Any]=8					,      __lowerCamelCase				:     List[str]=12					,      __lowerCamelCase				:     Optional[Any]=30_72					,      __lowerCamelCase				:     int="gelu"					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     List[Any]=5_12					,      __lowerCamelCase				:     Union[str, Any]=2					,      __lowerCamelCase				:     Tuple=0.02					,      __lowerCamelCase				:     int=1e-12					,      __lowerCamelCase				:     Any=True					,      __lowerCamelCase				:     List[Any]=1_28					,      __lowerCamelCase				:     Tuple=0					,      **__lowerCamelCase				:     List[Any]					,      )					->     Optional[int]:
							super().__init__(pad_token_id=__lowerCamelCase					,      **__lowerCamelCase							)

							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			hidden_act
							a    =			intermediate_size
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			initializer_range
							a    =			layer_norm_eps
							a    =			share_encoders
							a    =			projection_dim








import numpy as np






def        a_		(       __lowercase   :	np.array ) ->  np.array:
			return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()