

"""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 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     42
						__snake_case 			=     42







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Optional[int]:
											super().__init__()

											__lowercase												=			PNDMScheduler.from_config(scheduler.config							)

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







						@torch.no_grad()
						def __call__(self     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							**UpperCAmelCase_     ,							)			->						Union[ImagePipelineOutput, Tuple]:
											__lowercase												=			randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,							generator=UpperCAmelCase_     ,							device=self.device     ,							)

											self.scheduler.set_timesteps(UpperCAmelCase_							)
											for t in self.progress_bar(self.scheduler.timesteps							):
																__lowercase												=			self.unet(UpperCAmelCase_     ,							UpperCAmelCase_							).sample

																__lowercase												=			self.scheduler.step(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							).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(UpperCAmelCase_							)

											if not return_dict:
																return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase_							)


"""simple docstring"""

def 				__UpperCamelCase   (    a__: list ,	a__: list ,	a__: list ,	a__: int ,	a__: int			)				->			None:




					"""simple docstring"""



					if target == 0:
										answer.append(path.copy()			)
					else:
										for index in range(a__ ,	len(a__			)			):
															if target >= candidates[index]:
																				path.append(candidates[index]			)
																				backtrack(a__ ,	a__ ,	a__ ,	target - candidates[index] ,	a__			)
																				path.pop(len(a__			) - 1			)





def 				__UpperCamelCase   (    a__: list ,	a__: int			)				->			list:




					"""simple docstring"""



					__lowercase												=			[]  # type: list[int]
					__lowercase												=			[]  # type: list[int]
					backtrack(a__ ,	a__ ,	a__ ,	a__ ,	0			)
					return answer





def 				__UpperCamelCase   (    )				->			None:




					"""simple docstring"""



					print(combination_sum([-8, 2.3, 0] ,	1			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()


