code: 
  
  '''simple docstring'''
  
  
  
  
  
  import copy
  from typing import Dict
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
  from ..bit import BitConfig
  
  
  _lowerCAmelCase     :			Optional[Any]    =logging.get_logger(__name__)
  
  _lowerCAmelCase     :			str    ={
      '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
      # See all ViT hybrid models at https://huggingface.co/models?filter=vit
  }
  
  class __UpperCamelCase    (							UpperCamelCase_		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      """vit-hybrid"""
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__=None							,			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__=2_2_4							,			lowerCamelCase__=1							,			lowerCamelCase__=3							,			lowerCamelCase__=[1, 1_0_2_4, 2_4, 2_4]							,			lowerCamelCase__=True							,			**lowerCamelCase__							,			):
  						super().__init__(**__SCREAMING_SNAKE_CASE	)
  
  						if backbone_config is None:
  							logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
  							UpperCAmelCase__:  Union[str, Any]		       =   {
  							    "global_padding": "same",
  							    "layer_type": "bottleneck",
  							    "depths": [3, 4, 9],
  							    "out_features": ["stage3"],
  							    "embedding_dynamic_padding": True,
  							}
  
  						if isinstance(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE	):
  							if "model_type" in backbone_config:
  								UpperCAmelCase__:  Tuple		       =   CONFIG_MAPPING[backbone_config["model_type"]]
  							else:
  								logger.info(
  								    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
  								UpperCAmelCase__:  Optional[Any]		       =   BitConfig
  							UpperCAmelCase__:  Dict		       =   backbone_config_class(**__SCREAMING_SNAKE_CASE	)
  
  						UpperCAmelCase__:  Any		       =   backbone_featmap_shape
  						UpperCAmelCase__:  Tuple		       =   backbone_config
  						UpperCAmelCase__:  Optional[int]		       =   hidden_size
  						UpperCAmelCase__:  Any		       =   num_hidden_layers
  						UpperCAmelCase__:  Union[str, Any]		       =   num_attention_heads
  						UpperCAmelCase__:  int		       =   intermediate_size
  						UpperCAmelCase__:  List[Any]		       =   hidden_act
  						UpperCAmelCase__:  Optional[Any]		       =   hidden_dropout_prob
  						UpperCAmelCase__:  Any		       =   attention_probs_dropout_prob
  						UpperCAmelCase__:  int		       =   initializer_range
  						UpperCAmelCase__:  Optional[Any]		       =   layer_norm_eps
  						UpperCAmelCase__:  Optional[Any]		       =   image_size
  						UpperCAmelCase__:  Optional[int]		       =   patch_size
  						UpperCAmelCase__:  int		       =   num_channels
  						UpperCAmelCase__:  Dict		       =   qkv_bias
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   copy.deepcopy(self.__dict__	)
  						UpperCAmelCase__:  Optional[Any]		       =   self.backbone_config.to_dict()
  						UpperCAmelCase__:  str		       =   self.__class__.model_type
  						return output
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,)))