





import numpy as np




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						List[Any]						):
     __magic_name__				=       np.exp(snake_case_						)

     # Add up the all the exponentials
     __magic_name__				=       np.sum(snake_case_						)

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

     return softmax_vector


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






from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)