

'''simple docstring'''






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class     lowercase_			(lowercase_   ):




		"""simple docstring"""




		def __init__(   self     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  int    ):
			super().__init__()
			self.register_modules(unet=lowercase__      ,scheduler=lowercase__    )






		@torch.no_grad()
		def __call__(   self     :  str      ,lowercase__     :  List[str] = 1      ,lowercase__     :  int = None      ,lowercase__     :  List[Any] = 5_0      ,lowercase__     :  str = "pil"      ,lowercase__     :  List[Any] = True      ,**lowercase__     :  Optional[Any]      ,):
			__lowercase													=				torch.randn(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,generator=lowercase__      ,)
			__lowercase													=				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
				__lowercase													=				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
				__lowercase													=				self.scheduler.step(lowercase__      ,lowercase__      ,lowercase__    ).prev_sample

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




'''simple docstring'''






def 	_A  (					A__   ):






 """simple docstring"""



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

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()

