

"""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 (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    42
			SCREAMING_SNAKE_CASE_      =    42






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       UNetaDModel    ,	lowercase_  :       PNDMScheduler    ):
								super().__init__()

								UpperCamelCase__		:     Tuple								=PNDMScheduler.from_config(scheduler.config    )

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






			@torch.no_grad()
			def __call__(				self  :       List[Any]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 50    ,	lowercase_  :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,	lowercase_  :       Optional[str] = "pil"    ,	lowercase_  :       bool = True    ,	**lowercase_  :       Tuple    ,	):
								UpperCamelCase__		:     List[str]								=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    ):
													UpperCamelCase__		:     Tuple								=self.unet(lowercase_    ,	lowercase_    ).sample

													UpperCamelCase__		:     Optional[Any]								=self.scheduler.step(lowercase_    ,	lowercase_    ,	lowercase_    ).prev_sample

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

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=lowercase_    )





"""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) = }''')


