code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __magic_name__	:   str          =							{
      'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
  }
  
  try:
  					if not is_vision_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   int          =							['DeformableDetrFeatureExtractor']
  					__magic_name__	:   Any          =							['DeformableDetrImageProcessor']
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   int          =							[
  					    'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'DeformableDetrForObjectDetection',
  					    'DeformableDetrModel',
  					    'DeformableDetrPreTrainedModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig
  
  					try:
  										if not is_vision_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
  										from .image_processing_deformable_detr import DeformableDetrImageProcessor
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_deformable_detr import (
  										    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    DeformableDetrForObjectDetection,
  										    DeformableDetrModel,
  										    DeformableDetrPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   Any          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			np.exp(lowercase__    )
  
  			# Add up the all the exponentials
  			__lowerCamelCase        =			np.sum(lowercase__    )
  
  			# Divide every exponent by the sum of all exponents
  			__lowerCamelCase        =			exponent_vector / sum_of_exponents
  
  			return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))
  
  
  
  
  