



'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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






class        UpperCAmelCase__						(		UpperCamelCase__			):



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      List[Any]:
						super().__init__()
						self.register_modules(unet=UpperCamelCase      ,					scheduler=UpperCamelCase  )



					@torch.no_grad()
					def __call__(     self      ,					UpperCamelCase = 1      ,					UpperCamelCase = None      ,					UpperCamelCase = 1000      ,					UpperCamelCase = "pil"      ,					UpperCamelCase = True      ,					)  ->      Union[ImagePipelineOutput, Tuple]:
						# Sample gaussian noise to begin loop
						if isinstance(self.unet.config.sample_size      ,					UpperCamelCase  ):
							__lowerCAmelCase       				=	(
							    batch_size,
							    self.unet.config.in_channels,
							    self.unet.config.sample_size,
							    self.unet.config.sample_size,
							)
						else:
							__lowerCAmelCase       				=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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




'''simple docstring'''



from __future__ import annotations

from random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	return [*quick_sort(lowerCamelCase       ), pivot, *quick_sort(lowerCamelCase       )]


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