code: 
  """simple docstring"""
  
  
  
  
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import PNDMScheduler
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		UNetaDModel
    SCREAMING_SNAKE_CASE__    :		PNDMScheduler
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       super().__init__()
  
       UpperCAmelCase_      :      Tuple      =					PNDMScheduler.from_config(scheduler.config						)
  
       self.register_modules(unet=lowercase_       ,     scheduler=lowercase_						)
  
  
  
  
  
  
  
    @torch.no_grad()
    def __call__(	self       ,     lowercase_ = 1       ,     lowercase_ = 50       ,     lowercase_ = None       ,     lowercase_ = "pil"       ,     lowercase_ = True       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,     generator=lowercase_       ,     device=self.device       ,     )
  
       self.scheduler.set_timesteps(lowercase_						)
       for t in self.progress_bar(self.scheduler.timesteps						):
          UpperCAmelCase_      :      Optional[int]      =					self.unet(lowercase_       ,     lowercase_						).sample
  
          UpperCAmelCase_      :      Union[str, Any]      =					self.scheduler.step(lowercase_       ,     lowercase_       ,     lowercase_						).prev_sample
  
       UpperCAmelCase_      :      Optional[int]      =					(image / 2 + 0.5).clamp(0       ,     1						)
       UpperCAmelCase_      :      List[Any]      =					image.cpu().permute(0       ,     2       ,     3       ,     1						).numpy()
       if output_type == "pil":
          UpperCAmelCase_      :      Union[str, Any]      =					self.numpy_to_pil(lowercase_						)
  
       if not return_dict:
          return (image,)
  
       return ImagePipelineOutput(images=lowercase_						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  def 			__a  (	__lowerCamelCase = 1000						):
     UpperCAmelCase_      :      List[Any]      =					0
     UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
     total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
     UpperCAmelCase_      :      Any      =					(n - 1) // 5
     total += ((terms) * (10 + (terms - 1) * 5)) // 2
     UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
     total -= ((terms) * (30 + (terms - 1) * 15)) // 2
     return total
  
  
  if __name__ == "__main__":
      print(f"""{solution() = }""")
  
  
  