code: 
  
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  class   __lowerCamelCase (_a	):
  
  
  
  
  
  
  
         def __init__( self:   int,A_:   Any,A_:   Dict				):
  
  
  
  
  
  
              '''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: 
  
  
  from math import cos, sin
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = float(_lowercase   )
       return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       return [[c, -s], [s, c]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[c * c, cs], [cs, s * s]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]
  
  
  print(f"""    {scaling(5) = }""")
  print(f"""  {rotation(4_5) = }""")
  print(f"""{projection(4_5) = }""")
  
  
  print(f"""{reflection(4_5) = }""")
  
  
  