"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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


class 			_lowercase		(   A_  ):

				def __init__(     self       :    Dict       ,    _lowercase       :    str       ,    _lowercase       :    Dict     ):


										'''simple docstring'''
										super().__init__()
										self.register_modules(unet=_lowercase       ,    scheduler=_lowercase     )

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


										'''simple docstring'''



										if isinstance(self.unet.config.sample_size       ,    _lowercase     ):
																UpperCAmelCase__						=		(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																UpperCAmelCase__						=		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=_lowercase     )


"""simple docstring"""







def   a__      (		UpperCAmelCase__       :			list[int]			)->	int:
						UpperCAmelCase__						=		len(UpperCAmelCase__			)
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__			)			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

