


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Optional[Any]:






								'''simple docstring'''






								super().__init__()
								self.register_modules(unet=UpperCAmelCase						,       scheduler=UpperCAmelCase  )




							@torch.no_grad()
							def __call__(  self						,       UpperCAmelCase = 1						,       UpperCAmelCase = None						,       UpperCAmelCase = 50						,       UpperCAmelCase = "pil"						,       UpperCAmelCase = True						,       **UpperCAmelCase						,       )     ->       Union[ImagePipelineOutput, Tuple]:






								'''simple docstring'''






								lowercase_			 = torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,       generator=UpperCAmelCase						,       )
								lowercase_			 = image.to(self.device  )

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

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

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									lowercase_			 = self.scheduler.step(UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  ).prev_sample

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

								if not return_dict:
									return (image,), "This is a local test"

								return ImagePipelineOutput(images=UpperCAmelCase  ), "This is a local test"





from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # 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__":
							SCREAMING_SNAKE_CASE__       			=				input("""Enter numbers separated by a comma:\n""").strip()
							SCREAMING_SNAKE_CASE__       			=				[int(item) for item in user_input.split(""",""")]
							print(quick_sort(unsorted))

