code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  _lowerCAmelCase     :			Union[str, Any]    ={"""configuration_visual_bert""": ["""VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VisualBertConfig"""]}
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					_lowerCAmelCase     :			str    =[
  					    """VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """VisualBertForMultipleChoice""",
  					    """VisualBertForPreTraining""",
  					    """VisualBertForQuestionAnswering""",
  					    """VisualBertForRegionToPhraseAlignment""",
  					    """VisualBertForVisualReasoning""",
  					    """VisualBertLayer""",
  					    """VisualBertModel""",
  					    """VisualBertPreTrainedModel""",
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_visual_bert import (
  										    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    VisualBertForMultipleChoice,
  										    VisualBertForPreTraining,
  										    VisualBertForQuestionAnswering,
  										    VisualBertForRegionToPhraseAlignment,
  										    VisualBertForVisualReasoning,
  										    VisualBertLayer,
  										    VisualBertModel,
  										    VisualBertPreTrainedModel,
  										)
  
  
  else:
  					import sys
  
  					_lowerCAmelCase     :			List[str]    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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,)))