code: 
  
  
  '''simple docstring'''
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
  
   def __init__( self :	Dict,					a_ :	Optional[Any],					a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__()
      self.register_modules(unet=a_,					scheduler=a_					)
  
   @torch.no_grad()
   def __call__( self :	List[Any],					a_ :	int = 1,					a_ :	Optional[Union[torch.Generator, List[torch.Generator]]] = None,					a_ :	int = 1000,					a_ :	Optional[str] = "pil",					a_ :	bool = True,					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if isinstance(self.unet.config.sample_size,					a_					):
         UpperCamelCase__ 						=						(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
      else:
         UpperCamelCase__ 						=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
  
      if self.device.type == "mps":
         # randn does not work reproducibly on mps
         UpperCamelCase__ 						=						randn_tensor(a_,					generator=a_					)
         UpperCamelCase__ 						=						image.to(self.device					)
      else:
         UpperCamelCase__ 						=						randn_tensor(a_,					generator=a_,					device=self.device					)
  
      # set step values
      self.scheduler.set_timesteps(a_					)
  
      for t in self.progress_bar(self.scheduler.timesteps					):
         # 1. predict noise model_output
         UpperCamelCase__ 						=						self.unet(a_,					a_					).sample
  
         # 2. compute previous image: x_t -> x_t-1
         UpperCamelCase__ 						=						self.scheduler.step(a_,					a_,					a_,					generator=a_					).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(a_					)
  
      if not return_dict:
         return (image,)
  
      return ImagePipelineOutput(images=a_					)
style_context: 
  
  
  '''simple docstring'''
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int = 10_00   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     return sum(i for i in range(_UpperCamelCase   ) if i % 3 == 0 or i % 5 == 0   )
  
  
  if __name__ == "__main__":
         print(F"""{solution() = }""")