



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 						lowerCamelCase__			(		_a ):


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







						@torch.no_grad()
						def __call__(			self    :	Any   ,							_a    :	int = 1   ,							_a    :	Optional[torch.Generator] = None   ,							_a    :	int = 5_0   ,							_a    :	Optional[str] = "pil"   ,							_a    :	bool = True   ,							**_a    :	Union[str, Any]   ,							):
													a__:							int									=torch.randn(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,							generator=_a   ,							)
													a__:							str									=image.to(self.device      )

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

													for t in self.progress_bar(self.scheduler.timesteps      ):
																				# 1. predict noise model_output
																				a__:							List[Any]									=self.unet(_a   ,							_a      ).sample

																				# 2. predict previous mean of image x_t-1 and add variance depending on eta
																				# eta corresponds to η in paper and should be between [0, 1]
																				# do x_t -> x_t-1
																				a__:							int									=self.scheduler.step(_a   ,							_a   ,							_a      ).prev_sample

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

													if not return_dict:
																				return (image,), "This is a local test"

													return ImagePipelineOutput(images=_a      ), "This is a local test"










from typing import Callable





def     __lowerCamelCase							(   __magic_name__						:	Callable    ):

							def wrapper(__magic_name__						:	Optional[Any]    ):
														a__:							List[Any]									=function_with_docstring.__doc__
														return target_function

							return wrapper





