






"""simple docstring"""

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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				42
				UpperCAmelCase__        =				42







				def __init__(							self							:						Optional[int]						,    __lowerCamelCase							:						UNetaDModel						,    __lowerCamelCase							:						ScoreSdeVeScheduler	)				->					List[Any]:
										super().__init__()
										self.register_modules(unet=__lowerCamelCase						,    scheduler=__lowerCamelCase	)







				@torch.no_grad()
				def __call__(							self							:						Any						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						int = 2_0_0_0						,    __lowerCamelCase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,    __lowerCamelCase							:						Optional[str] = "pil"						,    __lowerCamelCase							:						bool = True						,    **__lowerCamelCase							:						Dict						,    )				->					Union[ImagePipelineOutput, Tuple]:
										__magic_name__            =     self.unet.config.sample_size
										__magic_name__            =     (batch_size, 3, img_size, img_size)

										__magic_name__            =     self.unet

										__magic_name__            =     randn_tensor(__lowerCamelCase						,    generator=__lowerCamelCase	) * self.scheduler.init_noise_sigma
										__magic_name__            =     sample.to(self.device	)

										self.scheduler.set_timesteps(__lowerCamelCase	)
										self.scheduler.set_sigmas(__lowerCamelCase	)

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

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

																# prediction step
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	).sample
																__magic_name__            =     self.scheduler.step_pred(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    generator=__lowerCamelCase	)

																__magic_name__      ,    __magic_name__            =     output.prev_sample, output.prev_sample_mean

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

										if not return_dict:
																return (sample,)

										return ImagePipelineOutput(images=__lowerCamelCase	)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						_validate_point(__lowerCamelCase   )
						_validate_point(__lowerCamelCase   )
						if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(a - b   ) for a, b in zip(__lowerCamelCase ,						__lowerCamelCase   )   )   )






def _lowerCAmelCase	(  __lowerCamelCase:list[float]   ):

						'''simple docstring'''






						if point:
												if isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
																		for item in point:
																								if not isinstance(__lowerCamelCase ,						(int, float)   ):
																														__magic_name__            =     (
																														    "Expected a list of numbers as input, found "
																														    f'''{type(__lowerCamelCase   ).__name__}'''
																														)
																														raise TypeError(__lowerCamelCase   )
												else:
																		__magic_name__            =     f'''Expected a list of numbers as input, found {type(__lowerCamelCase   ).__name__}'''
																		raise TypeError(__lowerCamelCase   )
						else:
												raise ValueError("Missing an input"   )






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						_validate_point(__lowerCamelCase   )
						_validate_point(__lowerCamelCase   )
						if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(x - y   ) for x, y in zip(__lowerCamelCase ,						__lowerCamelCase   )   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



