
from typing import List, Optional, Tuple, Union

import torch

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





class      A							(    snake_case_			):



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Optional[Any]:




									'''simple docstring'''



									super().__init__()
									self.register_modules(unet=lowerCamelCase__    ,       scheduler=lowerCamelCase__    )



			@torch.no_grad()
			def __call__(   self    ,       lowerCamelCase__ = 1    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = 1_000    ,       lowerCamelCase__ = "pil"    ,       lowerCamelCase__ = True    ,       )     ->						Union[ImagePipelineOutput, Tuple]:




									'''simple docstring'''



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

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCamelCase__    )








'''simple docstring'''



def 	_A						(							lowercase__		):
						return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






