



from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE      (				snake_case__      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

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







				@torch.no_grad()
				def __call__(				self						,	SCREAMING_SNAKE_CASE__ = 1						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 50						,	SCREAMING_SNAKE_CASE__ = "pil"						,	SCREAMING_SNAKE_CASE__ = True						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Any					     =   torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=lowercase_						,	)
					_snake_case					:							Union[str, Any]					     =   image.to(self.device      )

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

					for t in self.progress_bar(self.scheduler.timesteps      ):
						# 1. predict noise model_output
						_snake_case					:							Optional[int]					     =   self.unet(lowercase_						,	lowercase_      ).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
						_snake_case					:							Tuple					     =   self.scheduler.step(lowercase_						,	lowercase_						,	lowercase_      ).prev_sample

					_snake_case					:							str					     =   (image / 2 + 0.5).clamp(0						,	1      )
					_snake_case					:							List[str]					     =   image.cpu().permute(0						,	2						,	3						,	1      ).numpy()
					if output_type == "pil":
						_snake_case					:							List[str]					     =   self.numpy_to_pil(lowercase_      )

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

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








"""simple docstring"""





from __future__ import annotations



def 							_lowerCAmelCase   (    UpperCAmelCase  :				list[int]   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     dict[int, int]								={}
					for index, val in enumerate(UpperCAmelCase					):
										UpperCamelCase__		:     List[Any]								=target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										UpperCamelCase__		:     Any								=index
					return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F'''{two_sum([2, 7, 1_1, 1_5], 9) = }''')


