'''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    UpperCAmelCase__	(							lowercase__      ):






   """simple docstring"""
   A				:							UNetaDModel
   A				:							PNDMScheduler
   def __init__(self							,					_a							,					_a					)     ->  List[str]:
        super().__init__()

        lowercase_       :							List[str]  					=       PNDMScheduler.from_config(scheduler.config					)

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





   @torch.no_grad()
   def __call__(self							,					_a = 1							,					_a = 50							,					_a = None							,					_a = "pil"							,					_a = True							,					**_a							,					)     ->  Union[ImagePipelineOutput, Tuple]:
        lowercase_       :							Union[str, Any]  					=       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					):
             lowercase_       :							Tuple  					=       self.unet(lowerCAmelCase__							,					lowerCAmelCase__					).sample

             lowercase_       :							List[str]  					=       self.scheduler.step(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					).prev_sample

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

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase__					)












from __future__ import annotations

import re





def 						A_					(						lowercase_			)				->list[str]:







 """simple docstring"""

 def alphanum_key(lowercase_			):
  return [int(lowercase_			) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'	,  lowercase_			)]

 return sorted(lowercase_	,  key=lowercase_			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




