


'''simple docstring'''







from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class  lowerCAmelCase_						(       snake_case__      ):





		"""simple docstring"""



		def __init__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





							'''simple docstring'''

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


		@torch.no_grad()
		def __call__(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							int = 1				,							SCREAMING_SNAKE_CASE__     :							Optional[torch.Generator] = None				,							SCREAMING_SNAKE_CASE__     :							int = 5_0				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





							'''simple docstring'''

							__a       =   torch.randn(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,							generator=SCREAMING_SNAKE_CASE__				,							)
							__a       =   image.to(self.device						)

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

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

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												__a       =   self.scheduler.step(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						).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(SCREAMING_SNAKE_CASE__						)

							if not return_dict:
												return (image,), "This is a local test"

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__						), "This is a local test"










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     return " ".join(input_str.split()[::-1]		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






