code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCAmelCase_   =   {
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
     if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   ["BigBirdTokenizer"]
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   ["BigBirdTokenizerFast"]
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BigBirdForCausalLM",
         "BigBirdForMaskedLM",
         "BigBirdForMultipleChoice",
         "BigBirdForPreTraining",
         "BigBirdForQuestionAnswering",
         "BigBirdForSequenceClassification",
         "BigBirdForTokenClassification",
         "BigBirdLayer",
         "BigBirdModel",
         "BigBirdPreTrainedModel",
         "load_tf_weights_in_big_bird",
     ]
  
  try:
     if not is_flax_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "FlaxBigBirdForCausalLM",
         "FlaxBigBirdForMaskedLM",
         "FlaxBigBirdForMultipleChoice",
         "FlaxBigBirdForPreTraining",
         "FlaxBigBirdForQuestionAnswering",
         "FlaxBigBirdForSequenceClassification",
         "FlaxBigBirdForTokenClassification",
         "FlaxBigBirdModel",
         "FlaxBigBirdPreTrainedModel",
     ]
  
  if TYPE_CHECKING:
     from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
     try:
        if not is_sentencepiece_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_big_bird import BigBirdTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_big_bird import (
            BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdForCausalLM,
            BigBirdForMaskedLM,
            BigBirdForMultipleChoice,
            BigBirdForPreTraining,
            BigBirdForQuestionAnswering,
            BigBirdForSequenceClassification,
            BigBirdForTokenClassification,
            BigBirdLayer,
            BigBirdModel,
            BigBirdPreTrainedModel,
            load_tf_weights_in_big_bird,
        )
  
     try:
        if not is_flax_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_flax_big_bird import (
            FlaxBigBirdForCausalLM,
            FlaxBigBirdForMaskedLM,
            FlaxBigBirdForMultipleChoice,
            FlaxBigBirdForPreTraining,
            FlaxBigBirdForQuestionAnswering,
            FlaxBigBirdForSequenceClassification,
            FlaxBigBirdForTokenClassification,
            FlaxBigBirdModel,
            FlaxBigBirdPreTrainedModel,
        )
  
  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,)))
  
  
  
  
  
  
  