




"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										super().__init__()
										self.register_modules(unet=SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ )





				@torch.no_grad()
				def __call__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       Optional[Union[torch.Generator, List[torch.Generator]]] = None ,SCREAMING_SNAKE_CASE_				:       int = 1_0_0_0 ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size ,SCREAMING_SNAKE_CASE_ ):
																snake_case__    :   Any							     =   (
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																snake_case__    :   Dict							     =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if self.device.type == "mps":
																# randn does not work reproducibly on mps
																snake_case__    :   Any							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Tuple							     =   image.to(self.device )
										else:
																snake_case__    :   Dict							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=self.device )

										# set step values
										self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )

										for t in self.progress_bar(self.scheduler.timesteps ):
																# 1. predict noise model_output
																snake_case__    :   List[str]							     =   self.unet(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ).sample

																# 2. compute previous image: x_t -> x_t-1
																snake_case__    :   Optional[Any]							     =   self.scheduler.step(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ).prev_sample

										snake_case__    :   str							     =   (image / 2 + 0.5).clamp(0 ,1 )
										snake_case__    :   str							     =   image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
										if output_type == "pil":
																snake_case__    :   List[Any]							     =   self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )








"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						return int(lowerCamelCase						) if x - int(lowerCamelCase						) <= 0 else int(lowerCamelCase						) + 1


if __name__ == "__main__":
							import doctest

							doctest.testmod()


