






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 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    UNetaDModel
     __lowerCamelCase       :    PNDMScheduler





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				List[Any]:
        super().__init__()

        lowerCamelCase									=PNDMScheduler.from_config(scheduler.config  )

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





     @torch.no_grad()
     def __call__(	self							, _UpperCAmelCase = 1							, _UpperCAmelCase = 50							, _UpperCAmelCase = None							, _UpperCAmelCase = "pil"							, _UpperCAmelCase = True							, **_UpperCAmelCase							, )		->				Union[ImagePipelineOutput, Tuple]:
        lowerCamelCase									=randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							, generator=_UpperCAmelCase							, device=self.device							, )

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

           lowerCamelCase									=self.scheduler.step(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  ).prev_sample

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

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=_UpperCAmelCase  )







def  UpperCAmelCase__						(    lowerCamelCase	:			list    ):
   lowerCamelCase									=1.3
   lowerCamelCase									=len(lowerCamelCase    )
   lowerCamelCase									=False

   while not completed:
      # Update the gap value for a next comb
      lowerCamelCase									=int(gap / shrink_factor    )
      if gap <= 1:
         lowerCamelCase									=True

      lowerCamelCase									=0
      while index + gap < len(lowerCamelCase    ):
         if data[index] > data[index + gap]:
            # Swap values
            lowerCamelCase				,       lowerCamelCase									=data[index + gap], data[index]
            lowerCamelCase									=False
         index += 1

   return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _UpperCAmelCase					:						List[Any]         =     input('Enter numbers separated by a comma:\n').strip()
   _UpperCAmelCase					:						Optional[Any]         =     [int(item) for item in user_input.split(',')]
   print(comb_sort(unsorted))