





def 			_lowerCAmelCase						(							lowerCAmelCase_							:list[int]							)->tuple[int, int]:
   '''simple docstring'''






   if len(lowerCAmelCase_							) == 1:
      return a[0], a[0]
   else:
      # split A into half.
      snake_case_					    =					a[: len(lowerCAmelCase_							) // 2]
      snake_case_					    =					a[len(lowerCAmelCase_							) // 2 :]

      # 2 sub problems, 1/2 of original size.
      snake_case_    ,    snake_case_					    =					max_difference(lowerCAmelCase_							)
      snake_case_    ,    snake_case_					    =					max_difference(lowerCAmelCase_							)

      # get min of first and max of second
      # linear time
      snake_case_					    =					min(lowerCAmelCase_							)
      snake_case_					    =					max(lowerCAmelCase_							)

      # 3 cases, either (small1, big1),
      # (min_first, max_second), (small2, big2)
      # constant comparisons
      if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
         return smalla, biga
      elif biga - smalla > max_second - min_first:
         return smalla, biga
      else:
         return min_first, max_second


if __name__ == "__main__":
   import doctest

   doctest.testmod()








from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Tuple  )   -> str:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase    ,				scheduler=_lowerCAmelCase  )



  @torch.no_grad()
  def __call__(     self       :       Tuple    ,				_lowerCAmelCase       :       int = 1    ,				_lowerCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				_lowerCAmelCase       :       int = 1_0_0_0    ,				_lowerCAmelCase       :       Optional[str] = "pil"    ,				_lowerCAmelCase       :       bool = True    ,				)   -> Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size    ,				_lowerCAmelCase  ):
        snake_case_					    =					(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
     else:
        snake_case_					    =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
        # randn does not work reproducibly on mps
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase  )
        snake_case_					    =					image.to(self.device  )
     else:
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase    ,				device=self.device  )

     # set step values
     self.scheduler.set_timesteps(_lowerCAmelCase  )

     for t in self.progress_bar(self.scheduler.timesteps  ):
        # 1. predict noise model_output
        snake_case_					    =					self.unet(_lowerCAmelCase    ,				_lowerCAmelCase  ).sample

        # 2. compute previous image: x_t -> x_t-1
        snake_case_					    =					self.scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				generator=_lowerCAmelCase  ).prev_sample

     snake_case_					    =					(image / 2 + 0.5).clamp(0    ,				1  )
     snake_case_					    =					image.cpu().permute(0    ,				2    ,				3    ,				1  ).numpy()
     if output_type == "pil":
        snake_case_					    =					self.numpy_to_pil(_lowerCAmelCase  )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase  )




