





"""simple docstring"""


import torch

from diffusers import DiffusionPipeline






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A    ):






									'''simple docstring'''







									super().__init__()

									self.register_modules(unet=_A						,   scheduler=_A    )







			def __call__(      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			torch.randn(
									    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,   )
									UpperCamelCase				:		Any				   =			1

									UpperCamelCase				:		Optional[Any]				   =			self.unet(_A						,   _A    ).sample
									UpperCamelCase				:		List[str]				   =			self.scheduler.step(_A						,   _A						,   _A    ).prev_sample

									UpperCamelCase				:		Optional[int]				   =			scheduler_output - scheduler_output + torch.ones_like(_A    )

									return result










def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  [1]
 for i in range(2				,  _lowerCAmelCase	):
  factorials.append(factorials[-1] * i	)
 assert 0 <= k < factorials[-1] * n, "k out of bounds"

 UpperCamelCase      :   Tuple			      =  []
 UpperCamelCase      :   str			      =  list(range(_lowerCAmelCase	)	)

 # Find permutation
 while factorials:
  UpperCamelCase      :   int			      =  factorials.pop()
  UpperCamelCase      ,       UpperCamelCase      :   int			      =  divmod(_lowerCAmelCase				,  _lowerCAmelCase	)
  permutation.append(elements[number]	)
  elements.remove(elements[number]	)
 permutation.append(elements[0]	)

 return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()
