"""simple docstring"""






def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int  )				-> Optional[int]:
						a_							:			List[Any]     		=					len(_SCREAMING_SNAKE_CASE  )
						for _ in range(
						    0		,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
												for j in range(0		,			no_of_elements - 1  ):
																		if (
																		    lst[j + 1] < lst[j]
																		):  # applying bubble sort algorithm from left to right (or forwards)
																								a_							:			List[str]     		=					lst[j + 1]
																								a_							:			Optional[Any]     		=					lst[j]
																								a_							:			Optional[Any]     		=					temp
																		if (
																		    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
																		):  # applying bubble sort algorithm from right to left (or backwards)
																								a_							:			Dict     		=					lst[no_of_elements - 1 - j]
																								a_							:			Dict     		=					lst[no_of_elements - 2 - j]
																								a_							:			Any     		=					temp
						return lst


if __name__ == "__main__":
				print('enter the list to be sorted')
				UpperCamelCase     					=     [int(x) for x in input().split()]  # inputing elements of the list in one line
				UpperCamelCase     					=     double_sort(lst)
				print('the sorted list is')
				print(sorted_lst)





"""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 )




