





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  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							42
  _a								=							42





  def __init__(  self	,  A	,  A						)		->   Any:





       '''simple docstring'''







       super().__init__()

       __magic_name__				=       PNDMScheduler.from_config(scheduler.config						)

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







  @torch.no_grad()
  def __call__(  self	,  A = 1	,  A = 50	,  A = None	,  A = "pil"	,  A = True	,  **A	,  )		->   Union[ImagePipelineOutput, Tuple]:





       '''simple docstring'''







       __magic_name__				=       randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,  generator=A	,  device=self.device	,  )

       self.scheduler.set_timesteps(A						)
       for t in self.progress_bar(self.scheduler.timesteps						):
            __magic_name__				=       self.unet(A	,  A						).sample

            __magic_name__				=       self.scheduler.step(A	,  A	,  A						).prev_sample

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=A						)






from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)