

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


_lowerCAmelCase     :			Optional[int]    =logging.get_logger(__name__)

_lowerCAmelCase     :			Any    ={
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}

class __UpperCamelCase    (							_a		):






					'''simple docstring'''



					__magic_name__     			=      "vilt"





					def __init__(							self							,			lowerCamelCase__=3_0_5_2_2							,			lowerCamelCase__=2							,			lowerCamelCase__=2							,			lowerCamelCase__=4_0							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=1_2							,			lowerCamelCase__=1_2							,			lowerCamelCase__=3_0_7_2							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.0							,			lowerCamelCase__=0.0							,			lowerCamelCase__=0.02							,			lowerCamelCase__=1e-12							,			lowerCamelCase__=3_8_4							,			lowerCamelCase__=3_2							,			lowerCamelCase__=3							,			lowerCamelCase__=True							,			lowerCamelCase__=-1							,			lowerCamelCase__=False							,			lowerCamelCase__=-1							,			**lowerCamelCase__							,			):
						super().__init__(tie_word_embeddings=lowerCamelCase__							,			**lowerCamelCase__	)

						UpperCAmelCase__:  Any		       =   vocab_size
						UpperCAmelCase__:  Optional[int]		       =   type_vocab_size
						UpperCAmelCase__:  int		       =   modality_type_vocab_size
						UpperCAmelCase__:  List[Any]		       =   max_position_embeddings

						UpperCAmelCase__:  str		       =   hidden_size
						UpperCAmelCase__:  List[str]		       =   num_hidden_layers
						UpperCAmelCase__:  Dict		       =   num_attention_heads
						UpperCAmelCase__:  str		       =   intermediate_size
						UpperCAmelCase__:  str		       =   hidden_act
						UpperCAmelCase__:  str		       =   hidden_dropout_prob
						UpperCAmelCase__:  Any		       =   attention_probs_dropout_prob
						UpperCAmelCase__:  List[str]		       =   initializer_range
						UpperCAmelCase__:  Optional[int]		       =   layer_norm_eps

						UpperCAmelCase__:  Optional[Any]		       =   image_size
						UpperCAmelCase__:  int		       =   patch_size
						UpperCAmelCase__:  List[Any]		       =   num_channels
						UpperCAmelCase__:  Optional[Any]		       =   qkv_bias
						UpperCAmelCase__:  Union[str, Any]		       =   max_image_length
						UpperCAmelCase__:  List[str]		       =   num_images


import numpy as np



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)

	# Add up the all the exponentials
	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)

	# Divide every exponent by the sum of all exponents
	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents

	return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))