




import torch

from diffusers import DiffusionPipeline


class 							UpperCamelCase_       (					a__	):

    '''simple docstring'''






    def __init__(     self :      Optional[Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int)		->str:





           '''simple docstring'''

           super().__init__()

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





    def __call__(     self :      str)		->Dict:





           '''simple docstring'''

           A__ 					=       torch.randn(
               (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,					)
           A__ 					=       1

           A__ 					=       self.unet(_UpperCAmelCase   ,					_UpperCAmelCase).sample
           A__ 					=       self.scheduler.step(_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase).prev_sample

           A__ 					=       scheduler_output - scheduler_output + torch.ones_like(_UpperCAmelCase)

           return result









'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()