






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       42
							_UpperCAmelCase     =       42
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDModel       ,	UpperCamelCase:      ScoreSdeVeScheduler						)		->		int:
											super().__init__()
											self.register_modules(unet=UpperCamelCase       ,	scheduler=UpperCamelCase						)


							@torch.no_grad()
							def __call__(     self:      Optional[int]       ,	UpperCamelCase:      int = 1       ,	UpperCamelCase:      int = 20_00       ,	UpperCamelCase:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[ImagePipelineOutput, Tuple]:
											snake_case__									=     self.unet.config.sample_size
											snake_case__									=     (batch_size, 3, img_size, img_size)

											snake_case__									=     self.unet

											snake_case__									=     randn_tensor(UpperCamelCase       ,	generator=UpperCamelCase						) * self.scheduler.init_noise_sigma
											snake_case__									=     sample.to(self.device						)

											self.scheduler.set_timesteps(UpperCamelCase						)
											self.scheduler.set_sigmas(UpperCamelCase						)

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
															snake_case__									=     self.scheduler.sigmas[i] * torch.ones(shape[0]       ,	device=self.device						)

															# correction step
															for _ in range(self.scheduler.config.correct_steps						):
																			snake_case__									=     self.unet(UpperCamelCase       ,	UpperCamelCase						).sample
																			snake_case__									=     self.scheduler.step_correct(UpperCamelCase       ,	UpperCamelCase       ,	generator=UpperCamelCase						).prev_sample

															# prediction step
															snake_case__									=     model(UpperCamelCase       ,	UpperCamelCase						).sample
															snake_case__									=     self.scheduler.step_pred(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	generator=UpperCamelCase						)

															snake_case__							,						snake_case__									=     output.prev_sample, output.prev_sample_mean

											snake_case__									=     sample_mean.clamp(0       ,	1						)
											snake_case__									=     sample.cpu().permute(0       ,	2       ,	3       ,	1						).numpy()
											if output_type == "pil":
															snake_case__									=     self.numpy_to_pil(UpperCamelCase						)

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=UpperCamelCase						)












def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				if digit_amount > 0:
								return round(number - int(_A  )      ,   _A  )
				return number - int(_A  )


if __name__ == "__main__":
							print(decimal_isolate(1.5_3, 0))
							print(decimal_isolate(3_5.3_4_5, 1))
							print(decimal_isolate(3_5.3_4_5, 2))
							print(decimal_isolate(3_5.3_4_5, 3))
							print(decimal_isolate(-1_4.7_8_9, 3))
							print(decimal_isolate(0, 2))
							print(decimal_isolate(-1_4.1_2_3, 1))
							print(decimal_isolate(-1_4.1_2_3, 2))
							print(decimal_isolate(-1_4.1_2_3, 3))




