





"""simple docstring"""




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class  _a						(      _lowerCAmelCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    List[str], lowerCAmelCase__		:    Union[str, Any]							)	->      Optional[Any]:




										'''simple docstring'''



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






				@torch.no_grad()
				def __call__( self		:    Any, lowerCAmelCase__		:    int = 1, lowerCAmelCase__		:    Optional[torch.Generator] = None, lowerCAmelCase__		:    int = 5_0, lowerCAmelCase__		:    Optional[str] = "pil", lowerCAmelCase__		:    bool = True, **lowerCAmelCase__		:    Optional[int], )	->      Union[ImagePipelineOutput, Tuple]:




										'''simple docstring'''



										_UpperCamelCase			:					Tuple     =    torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), generator=lowerCAmelCase__, )
										_UpperCamelCase			:					Optional[Any]     =    image.to(self.device							)

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

										for t in self.progress_bar(self.scheduler.timesteps							):
																# 1. predict noise model_output
																_UpperCamelCase			:					Any     =    self.unet(lowerCAmelCase__, lowerCAmelCase__							).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
																_UpperCamelCase			:					List[Any]     =    self.scheduler.step(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__							).prev_sample

										_UpperCamelCase			:					Dict     =    (image / 2 + 0.5).clamp(0, 1							)
										_UpperCamelCase			:					Optional[Any]     =    image.cpu().permute(0, 2, 3, 1							).numpy()
										if output_type == "pil":
																_UpperCamelCase			:					int     =    self.numpy_to_pil(lowerCAmelCase__							)

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

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










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



