code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __magic_name__	:   Any          =							{
      'configuration_retribert': ['RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RetriBertConfig'],
      'tokenization_retribert': ['RetriBertTokenizer'],
  }
  
  try:
  					if not is_tokenizers_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Dict          =							['RetriBertTokenizerFast']
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Optional[Any]          =							[
  					    'RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'RetriBertModel',
  					    'RetriBertPreTrainedModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
  					from .tokenization_retribert import RetriBertTokenizer
  
  					try:
  										if not is_tokenizers_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .tokenization_retribert_fast import RetriBertTokenizerFast
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_retribert import (
  										    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    RetriBertModel,
  										    RetriBertPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   Dict          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			np.exp(lowercase__    )
  
  			# Add up the all the exponentials
  			__lowerCamelCase        =			np.sum(lowercase__    )
  
  			# Divide every exponent by the sum of all exponents
  			__lowerCamelCase        =			exponent_vector / sum_of_exponents
  
  			return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))
  
  
  
  
  