




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=			{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A    	=			[
     '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''QDQBertForMaskedLM''',
     '''QDQBertForMultipleChoice''',
     '''QDQBertForNextSentencePrediction''',
     '''QDQBertForQuestionAnswering''',
     '''QDQBertForSequenceClassification''',
     '''QDQBertForTokenClassification''',
     '''QDQBertLayer''',
     '''QDQBertLMHeadModel''',
     '''QDQBertModel''',
     '''QDQBertPreTrainedModel''',
     '''load_tf_weights_in_qdqbert''',
 ]


if TYPE_CHECKING:
 from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_qdqbert import (
      QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      QDQBertForMaskedLM,
      QDQBertForMultipleChoice,
      QDQBertForNextSentencePrediction,
      QDQBertForQuestionAnswering,
      QDQBertForSequenceClassification,
      QDQBertForTokenClassification,
      QDQBertLayer,
      QDQBertLMHeadModel,
      QDQBertModel,
      QDQBertPreTrainedModel,
      load_tf_weights_in_qdqbert,
  )


else:
 import sys

 __A    	=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




import numpy as np



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  _snake_case		=							np.exp(_UpperCamelCase  )

  # Add up the all the exponentials
  _snake_case		=							np.sum(_UpperCamelCase  )

  # Divide every exponent by the sum of all exponents
  _snake_case		=							exponent_vector / sum_of_exponents

  return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

