

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	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			UNetaDModel
  lowerCamelCase    :			PNDMScheduler







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    UNetaDModel       ,							lowerCAmelCase :    PNDMScheduler    )							->	Dict:






     """simple docstring"""







     super().__init__()

     __lowerCAmelCase  :			Union[str, Any]					    =						PNDMScheduler.from_config(scheduler.config    )

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







  @torch.no_grad()
  def __call__(			self :    Optional[Any]       ,							lowerCAmelCase :    int = 1       ,							lowerCAmelCase :    int = 50       ,							lowerCAmelCase :    Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							lowerCAmelCase :    Optional[str] = "pil"       ,							lowerCAmelCase :    bool = True       ,							**lowerCAmelCase :    Dict       ,							)							->	Union[ImagePipelineOutput, Tuple]:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,							generator=lowerCAmelCase       ,							device=self.device       ,							)

     self.scheduler.set_timesteps(lowerCAmelCase    )
     for t in self.progress_bar(self.scheduler.timesteps    ):
        __lowerCAmelCase  :			int					    =						self.unet(lowerCAmelCase       ,							lowerCAmelCase    ).sample

        __lowerCAmelCase  :			Any					    =						self.scheduler.step(lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    ).prev_sample

     __lowerCAmelCase  :			List[str]					    =						(image / 2 + 0.5).clamp(0       ,							1    )
     __lowerCAmelCase  :			Any					    =						image.cpu().permute(0       ,							2       ,							3       ,							1    ).numpy()
     if output_type == "pil":
        __lowerCAmelCase  :			Any					    =						self.numpy_to_pil(lowerCAmelCase    )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase    )





from __future__ import annotations

from collections.abc import Generator



def    UpperCamelCase(					lowercase_							)	->				Generator[int, None, None]:





  '''simple docstring'''





  if not isinstance(lowercase_			,					lowercase_							) or n < 1:
    raise Exception("""Sequence only defined for positive integers"""							)

  yield n
  while n != 1:
    if n % 2 == 0:
      n //= 2
    else:
      snake_case_       						=      3 * n + 1
    yield n



def    UpperCamelCase(					)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      43
  snake_case_       						=      tuple(collatz_sequence(lowercase_							)							)
  print(lowercase_							)
  print(f'''Collatz sequence from {n} took {len(lowercase_							)} steps.'''							)


if __name__ == "__main__":
    main()