
'''simple docstring'''





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Tuple:







      """simple docstring"""
      __A    		=     index
      __A    		=     2 * index + 1
      __A    		=     2 * index + 2
      if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
            __A    		=     left_index

      if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
            __A    		=     right_index

      if largest != index:
            __A		, __A    		=     unsorted[index], unsorted[largest]
            heapify(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)





def     snake_case							(						UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     len(UpperCAmelCase		)
      for i in range(n // 2 - 1 ,					-1 ,					-1		):
            heapify(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
      for i in range(n - 1 ,					0 ,					-1		):
            __A		, __A    		=     unsorted[i], unsorted[0]
            heapify(UpperCAmelCase ,					0 ,					UpperCAmelCase		)
      return unsorted


if __name__ == "__main__":
 a__						:				str           =    input("Enter numbers separated by a comma:\n").strip()
 a__						:				Optional[Any]           =    [int(item) for item in user_input.split(",")]
 print(heap_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):






 def 		lowercase_  (     self					:Optional[int]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     'runwayml/stable-diffusion-v1-5'
       __A    		=     TextToVideoZeroPipeline.from_pretrained(_A		,			torch_dtype=torch.floataa     ).to('cuda'     )
       __A    		=     DDIMScheduler.from_config(pipe.scheduler.config     )
       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )

       __A    		=     'A bear is playing a guitar on Times Square'
       __A    		=     pipe(prompt=_A		,			generator=_A     ).images

       __A    		=     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(_A		,			_A     )






