






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"""



     __snake_case		:				UNetaDModel
     __snake_case		:				PNDMScheduler





     def __init__(		self					:	Tuple  ,  lowerCAmelCase__					:	UNetaDModel  ,  lowerCAmelCase__					:	PNDMScheduler)							->	List[str]:

      """simple docstring"""
      super().__init__()

      SCREAMING_SNAKE_CASE				    =		PNDMScheduler.from_config(scheduler.config)

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





     @torch.no_grad()
     def __call__(		self					:	List[Any]  ,  lowerCAmelCase__					:	int = 1  ,  lowerCAmelCase__					:	int = 5_0  ,  lowerCAmelCase__					:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowerCAmelCase__					:	Optional[str] = "pil"  ,  lowerCAmelCase__					:	bool = True  ,  **lowerCAmelCase__					:	int  ,  )							->	Union[ImagePipelineOutput, Tuple]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		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):
       SCREAMING_SNAKE_CASE				    =		self.unet(lowerCAmelCase__  ,  lowerCAmelCase__).sample

       SCREAMING_SNAKE_CASE				    =		self.scheduler.step(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__).prev_sample

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




