code: 
  
  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
style_context: 
  
  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,)))