






'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	42
     _snake_case							   =	42






     def __init__(self     :						Any					,					lowercase__     :						UNetaDModel					,					lowercase__     :						PNDMScheduler       )	->							Optional[Any]:
           super().__init__()

           __A		 =   PNDMScheduler.from_config(scheduler.config       )

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






     @torch.no_grad()
     def __call__(self     :						List[str]					,					lowercase__     :						int = 1					,					lowercase__     :						int = 50					,					lowercase__     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None					,					lowercase__     :						Optional[str] = "pil"					,					lowercase__     :						bool = True					,					**lowercase__     :						Union[str, Any]					,					)	->							Union[ImagePipelineOutput, Tuple]:
           __A		 =   randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,					generator=lowercase__					,					device=self.device					,					)

           self.scheduler.set_timesteps(lowercase__       )
           for t in self.progress_bar(self.scheduler.timesteps       ):
                 __A		 =   self.unet(lowercase__					,					lowercase__       ).sample

                 __A		 =   self.scheduler.step(lowercase__					,					lowercase__					,					lowercase__       ).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=lowercase__       )










'''simple docstring'''







def     _UpperCamelCase				(  snake_case__				):
      if len(snake_case__				) <= 1:
            return data
      else:
            return [
                *quick_sort([e for e in data[1:] if e <= data[0]]				),
                data[0],
                *quick_sort([e for e in data[1:] if e > data[0]]				),
            ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


