

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case ):
											super().__init__()
											self.register_modules(unet=snake_case   ,     scheduler=snake_case )




				@torch.no_grad()
				def __call__(      self   ,     snake_case = 1   ,     snake_case = None   ,     snake_case = 50   ,     snake_case = "pil"   ,     snake_case = True   ,     **snake_case   ,     ):
											snake_case_           =					torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,     generator=snake_case   ,     )
											snake_case_           =					image.to(self.device )

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

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

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

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

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







def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if not nums:  # Makes sure that the list is not empty
														raise ValueError('List is empty'   )

							snake_case_           =					sum(UpperCamelCase__   ) / len(UpperCamelCase__   )  # Calculate the average
							return sum(abs(x - average   ) for x in nums   ) / len(UpperCamelCase__   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




