



'''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 		_UpperCamelCase						(							__lowerCAmelCase					):
			_A   :	Any    					=	42
			_A   :	Union[str, Any]    					=	42

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				UNetaDModel							,  lowerCAmelCase__  :				ScoreSdeVeScheduler		):





					"""simple docstring"""






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



			@torch.no_grad()
			def __call__(							self  :				Dict							,  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__  :				Optional[Any]							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.unet.config.sample_size
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(batch_size, 3, img_size, img_size)

					__SCREAMING_SNAKE_CASE :	int					 =	self.unet

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	randn_tensor(_UpperCamelCase							,  generator=_UpperCamelCase		) * self.scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	List[str]					 =	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		)		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler.sigmas[i] * torch.ones(shape[0]							,  device=self.device		)

							# correction step
							for _ in range(self.scheduler.config.correct_steps		):
									__SCREAMING_SNAKE_CASE :	List[str]					 =	self.unet(_UpperCamelCase							,  _UpperCamelCase		).sample
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.scheduler.step_correct(_UpperCamelCase							,  _UpperCamelCase							,  generator=_UpperCamelCase		).prev_sample

							# prediction step
							__SCREAMING_SNAKE_CASE :	str					 =	model(_UpperCamelCase							,  _UpperCamelCase		).sample
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.scheduler.step_pred(_UpperCamelCase							,  _UpperCamelCase							,  _UpperCamelCase							,  generator=_UpperCamelCase		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	output.prev_sample, output.prev_sample_mean

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	sample_mean.clamp(0							,  1		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	sample.cpu().permute(0							,  2							,  3							,  1		).numpy()
					if output_type == "pil":
							__SCREAMING_SNAKE_CASE :	Any					 =	self.numpy_to_pil(_UpperCamelCase		)

					if not return_dict:
							return (sample,)

					return ImagePipelineOutput(images=_UpperCamelCase		)




'''simple docstring'''




def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if number > 0:
				raise ValueError("""input must be a negative integer"""      )
		__SCREAMING_SNAKE_CASE :	str					 =	len(bin(_lowerCamelCase      )[3:]      )
		__SCREAMING_SNAKE_CASE :	Any					 =	bin(abs(_lowerCamelCase      ) - (1 << binary_number_length)      )[3:]
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(
		    (
		        """1"""
		        + """0""" * (binary_number_length - len(_lowerCamelCase      ))
		        + twos_complement_number
		    )
		    if number < 0
		    else """0"""
		)
		return "0b" + twos_complement_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()