

# 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     ,    UpperCAmelCase     :						List[str]     ,    UpperCAmelCase     :						List[str]   )				->      Any:
      super().__init__()
      self.register_modules(unet=UpperCAmelCase     ,    scheduler=UpperCAmelCase   )


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

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

      for t in self.progress_bar(self.scheduler.timesteps   ):
       # 1. predict noise model_output
       __lowerCAmelCase:  Any        = self.unet(UpperCAmelCase     ,    UpperCAmelCase   ).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
       __lowerCAmelCase:  Union[str, Any]        = self.scheduler.step(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   ).prev_sample

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

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

      return ImagePipelineOutput(images=UpperCAmelCase   ), "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()





