

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


a__												=						logging.get_logger(__name__)

# TODO: upload to AWS
a__												=						{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       "retribert"


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=7_6_8			,				_a=8			,				_a=1_2			,				_a=3_0_7_2			,				_a="gelu"			,				_a=0.1			,				_a=0.1			,				_a=5_1_2			,				_a=2			,				_a=0.02			,				_a=1e-1_2			,				_a=True			,				_a=1_2_8			,				_a=0			,				**_a			,				)       ->       Optional[int]:
												super().__init__(pad_token_id=_a			,				**_a			)

												_a      :       Any   =						vocab_size
												_a      :       Optional[int]   =						hidden_size
												_a      :       str   =						num_hidden_layers
												_a      :       Optional[Any]   =						num_attention_heads
												_a      :       List[str]   =						hidden_act
												_a      :       str   =						intermediate_size
												_a      :       List[str]   =						hidden_dropout_prob
												_a      :       Optional[int]   =						attention_probs_dropout_prob
												_a      :       str   =						max_position_embeddings
												_a      :       Union[str, Any]   =						type_vocab_size
												_a      :       Union[str, Any]   =						initializer_range
												_a      :       Tuple   =						layer_norm_eps
												_a      :       int   =						share_encoders
												_a      :       List[str]   =						projection_dim










import numpy as np





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> np.array:




    """simple docstring"""




    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





