"""simple docstring"""






from __future__ import annotations

from random import randrange

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:list  )				-> list:
						if len(_SCREAMING_SNAKE_CASE  ) < 2:
												return collection
						a_							:			List[str]     		=					randrange(len(_SCREAMING_SNAKE_CASE  )  )  # Use random element as pivot
						a_							:			Union[str, Any]     		=					collection[pivot_index]
						a_							:			list[int]     		=					[]  # All elements greater than pivot
						a_							:			list[int]     		=					[]  # All elements less than or equal to pivot

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

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

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


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





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




