



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 				a_    (						snake_case__							):

						def __init__(       self      :Dict  ,		_lowercase      :List[str]  ,		_lowercase      :List[str])						->   Any:
												super().__init__()
												self.register_modules(unet=_lowercase  ,		scheduler=_lowercase)




						@torch.no_grad()
						def __call__(       self      :Union[str, Any]  ,		_lowercase      :int = 1  ,		_lowercase      :Optional[torch.Generator] = None  ,		_lowercase      :int = 50  ,		_lowercase      :Optional[str] = "pil"  ,		_lowercase      :bool = True  ,		**_lowercase      :int  ,		)						->   Union[ImagePipelineOutput, Tuple]:
												UpperCAmelCase_					=	torch.randn(
												    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,		generator=_lowercase  ,		)
												UpperCAmelCase_					=	image.to(self.device)

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

												for t in self.progress_bar(self.scheduler.timesteps):
																		# 1. predict noise model_output
																		UpperCAmelCase_					=	self.unet(_lowercase  ,		_lowercase).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_					=	self.scheduler.step(_lowercase  ,		_lowercase  ,		_lowercase).prev_sample

												UpperCAmelCase_					=	(image / 2 + 0.5).clamp(0  ,		1)
												UpperCAmelCase_					=	image.cpu().permute(0  ,		2  ,		3  ,		1).numpy()
												if output_type == "pil":
																		UpperCAmelCase_					=	self.numpy_to_pil(_lowercase)

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

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








def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





