code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  lowerCAmelCase_   =   {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RegNetForImageClassification",
         "RegNetModel",
         "RegNetPreTrainedModel",
     ]
  
  try:
     if not is_tf_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFRegNetForImageClassification",
         "TFRegNetModel",
         "TFRegNetPreTrainedModel",
     ]
  
  try:
     if not is_flax_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "FlaxRegNetForImageClassification",
         "FlaxRegNetModel",
         "FlaxRegNetPreTrainedModel",
     ]
  
  
  if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_regnet import (
            REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            RegNetForImageClassification,
            RegNetModel,
            RegNetPreTrainedModel,
        )
  
     try:
        if not is_tf_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_tf_regnet import (
            TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRegNetForImageClassification,
            TFRegNetModel,
            TFRegNetPreTrainedModel,
        )
  
     try:
        if not is_flax_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_flax_regnet import (
            FlaxRegNetForImageClassification,
            FlaxRegNetModel,
            FlaxRegNetPreTrainedModel,
        )
  
  
  else:
     import sys
  
     lowerCAmelCase_   =   _LazyModule(__name__, globals()["__file__"], _import_structure)
  
  
  
  
  
  
  
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,)))
  
  
  
  
  
  
  