


"""simple docstring"""







import operator


def      lowercase			(				A_  ,   A_ = False  ,   A_ = None       )->      List[str]:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			operator.lt if reverse else operator.gt
  a						:		List[str]      							=			solution or []

  if not arr:
    return solution

  a						:		Any      							=			[arr.pop(0       )]
  for i, item in enumerate(UpperCAmelCase_       ):
    if _operator(UpperCAmelCase_  ,   sublist[-1]       ):
      sublist.append(UpperCAmelCase_       )
      arr.pop(UpperCAmelCase_       )

       #  merging sublist into solution list
  if not solution:
    solution.extend(UpperCAmelCase_       )
  else:
    while sublist:
      a						:		Any      							=			sublist.pop(0       )
      for i, xx in enumerate(UpperCAmelCase_       ):
        if not _operator(UpperCAmelCase_  ,   UpperCAmelCase_       ):
          solution.insert(UpperCAmelCase_  ,   UpperCAmelCase_       )
          break
      else:
        solution.append(UpperCAmelCase_       )

  strand_sort(UpperCAmelCase_  ,   UpperCAmelCase_  ,   UpperCAmelCase_       )
  return solution


if __name__ == "__main__":
 assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
 assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]










'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
    lowerCamelCase_      =  self.scheduler.sigmas[i] * torch.ones(shape[0]			,  device=self.device     )

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



