code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCAmelCase_   =   {
      "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
      "tokenization_openai": ["OpenAIGPTTokenizer"],
  }
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   ["OpenAIGPTTokenizerFast"]
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "OpenAIGPTDoubleHeadsModel",
         "OpenAIGPTForSequenceClassification",
         "OpenAIGPTLMHeadModel",
         "OpenAIGPTModel",
         "OpenAIGPTPreTrainedModel",
         "load_tf_weights_in_openai_gpt",
     ]
  
  try:
     if not is_tf_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFOpenAIGPTDoubleHeadsModel",
         "TFOpenAIGPTForSequenceClassification",
         "TFOpenAIGPTLMHeadModel",
         "TFOpenAIGPTMainLayer",
         "TFOpenAIGPTModel",
         "TFOpenAIGPTPreTrainedModel",
     ]
  
  
  if TYPE_CHECKING:
     from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
     from .tokenization_openai import OpenAIGPTTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_openai_fast import OpenAIGPTTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_openai import (
            OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OpenAIGPTDoubleHeadsModel,
            OpenAIGPTForSequenceClassification,
            OpenAIGPTLMHeadModel,
            OpenAIGPTModel,
            OpenAIGPTPreTrainedModel,
            load_tf_weights_in_openai_gpt,
        )
  
     try:
        if not is_tf_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_tf_openai import (
            TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFOpenAIGPTDoubleHeadsModel,
            TFOpenAIGPTForSequenceClassification,
            TFOpenAIGPTLMHeadModel,
            TFOpenAIGPTMainLayer,
            TFOpenAIGPTModel,
            TFOpenAIGPTPreTrainedModel,
        )
  
  else:
     import sys
  
     lowerCAmelCase_   =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_  )			->						Optional[Any]:
         _snake_case      :							Union[str, Any]     				=						np.exp(lowercase_  )
  
         # Add up the all the exponentials
         _snake_case      :							List[str]     				=						np.sum(lowercase_  )
  
         # Divide every exponent by the sum of all exponents
         _snake_case      :							Optional[int]     				=						exponent_vector / sum_of_exponents
  
         return softmax_vector
  
  
  if __name__ == "__main__":
     print(softmax((0,)))
  
  
  
  
  
  
  