




'''simple docstring'''



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A,							A  ):







             '''simple docstring'''







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







       @torch.no_grad()
       def __call__(      self,							A = 1,							A = None,							A = 50,							A = "pil",							A = True,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						torch.randn(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),							generator=A,							)
             SCREAMING_SNAKE_CASE   :      Dict    					=						image.to(self.device  )

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

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

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

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

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








'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					int    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
            raise TypeError('Input value must be an \'int\' type'    )

      SCREAMING_SNAKE_CASE   :      int    					=						0
      while number:
            position += 1
            number >>= 1

      return position


if __name__ == "__main__":
      import doctest

      doctest.testmod()


