code: 
  
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _lowerCAmelCase     :			Optional[int]    ={"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					_lowerCAmelCase     :			List[str]    =[
  					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """RegNetForImageClassification""",
  					    """RegNetModel""",
  					    """RegNetPreTrainedModel""",
  					]
  
  try:
  					if not is_tf_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					_lowerCAmelCase     :			Tuple    =[
  					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """TFRegNetForImageClassification""",
  					    """TFRegNetModel""",
  					    """TFRegNetPreTrainedModel""",
  					]
  
  try:
  					if not is_flax_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					_lowerCAmelCase     :			Union[str, Any]    =[
  					    """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     :			List[Any]    =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
style_context: 
  
  import numpy as np
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)
  
  	# Add up the all the exponentials
  	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)
  
  	# Divide every exponent by the sum of all exponents
  	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents
  
  	return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))