

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  def __init__(	self    :				Optional[Any]     ,  A_    :				Union[str, Any]     ,  A_    :				List[str]	)					-> Union[str, Any]:
      super().__init__()
      self.register_modules(unet=A_     ,  scheduler=A_	)







  @torch.no_grad()
  def __call__(	self    :				List[Any]     ,  A_    :				int = 1     ,  A_    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  A_    :				int = 1000     ,  A_    :				Optional[str] = "pil"     ,  A_    :				bool = True     ,  )					-> Union[ImagePipelineOutput, Tuple]:
      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size     ,  A_	):
          SCREAMING_SNAKE_CASE__					     =						(
              batch_size,
              self.unet.config.in_channels,
              self.unet.config.sample_size,
              self.unet.config.sample_size,
          )
      else:
          SCREAMING_SNAKE_CASE__					     =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if self.device.type == "mps":
          # randn does not work reproducibly on mps
          SCREAMING_SNAKE_CASE__					     =						randn_tensor(A_     ,  generator=A_	)
          SCREAMING_SNAKE_CASE__					     =						image.to(self.device	)
      else:
          SCREAMING_SNAKE_CASE__					     =						randn_tensor(A_     ,  generator=A_     ,  device=self.device	)

      # set step values
      self.scheduler.set_timesteps(A_	)

      for t in self.progress_bar(self.scheduler.timesteps	):
          # 1. predict noise model_output
          SCREAMING_SNAKE_CASE__					     =						self.unet(A_     ,  A_	).sample

          # 2. compute previous image: x_t -> x_t-1
          SCREAMING_SNAKE_CASE__					     =						self.scheduler.step(A_     ,  A_     ,  A_     ,  generator=A_	).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(A_	)

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=A_	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						index
    SCREAMING_SNAKE_CASE__					     =						2 * index + 1
    SCREAMING_SNAKE_CASE__					     =						2 * index + 2
    if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
        SCREAMING_SNAKE_CASE__					     =						left_index

    if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
        SCREAMING_SNAKE_CASE__					     =						right_index

    if largest != index:
        SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						unsorted[index], unsorted[largest]
        heapify(__A ,       __A ,       __A	)





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						len(__A	)
    for i in range(n // 2 - 1 ,       -1 ,       -1	):
        heapify(__A ,       __A ,       __A	)
    for i in range(n - 1 ,       0 ,       -1	):
        SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						unsorted[i], unsorted[0]
        heapify(__A ,       0 ,       __A	)
    return unsorted


if __name__ == "__main__":
  A__        =			input("Enter numbers separated by a comma:\n").strip()
  A__        =			[int(item) for item in user_input.split(",")]
  print(heap_sort(unsorted))


