






"""simple docstring"""






def 						_UpperCamelCase			(  A    ,       A    ,       A			):
			UpperCamelCase_     =index
			UpperCamelCase_     =2 * index + 1
			UpperCamelCase_     =2 * index + 2
			if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
						UpperCamelCase_     =left_index

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

			if largest != index:
						UpperCamelCase_    ,							UpperCamelCase_     =unsorted[index], unsorted[largest]
						heapify(A    ,       A    ,       A			)




def 						_UpperCamelCase			(  A			):
			UpperCamelCase_     =len(A			)
			for i in range(n // 2 - 1    ,       -1    ,       -1			):
						heapify(A    ,       A    ,       A			)
			for i in range(n - 1    ,       0    ,       -1			):
						UpperCamelCase_    ,							UpperCamelCase_     =unsorted[i], unsorted[0]
						heapify(A    ,       0    ,       A			)
			return unsorted


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








"""simple docstring"""






import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput
@dataclass
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  __lowerCamelCase       :    Union[List[PIL.Image.Image], np.ndarray]




class 						__lowerCAmelCase   (			unittest.TestCase       ):







  '''simple docstring'''


  def 					UpperCamelCase__   (      self:		Tuple					):
     UpperCamelCase_     =CustomOutput(images=np.random.rand(1				,			3				,			4				,			4					)					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			np.ndarray					)
     assert outputs.images.shape == (1, 3, 4, 4)
     assert isinstance(outputs["images"]				,			np.ndarray					)
     assert outputs["images"].shape == (1, 3, 4, 4)
     assert isinstance(outputs[0]				,			np.ndarray					)
     assert outputs[0].shape == (1, 3, 4, 4)

     # test with a non-tensor attribute
     UpperCamelCase_     =CustomOutput(images=[PIL.Image.new("RGB"				,			(4, 4)					)]					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			UpperCamelCase_					)
     assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
     assert isinstance(outputs["images"]				,			UpperCamelCase_					)
     assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
     assert isinstance(outputs[0]				,			UpperCamelCase_					)
     assert isinstance(outputs[0][0]				,			PIL.Image.Image					)





  def 					UpperCamelCase__   (      self:		Optional[int]					):
     # test output reinitialization with a `dict` for compatibility with `accelerate`
     UpperCamelCase_     =CustomOutput({"images": np.random.rand(1				,			3				,			4				,			4					)}					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			np.ndarray					)
     assert outputs.images.shape == (1, 3, 4, 4)
     assert isinstance(outputs["images"]				,			np.ndarray					)
     assert outputs["images"].shape == (1, 3, 4, 4)
     assert isinstance(outputs[0]				,			np.ndarray					)
     assert outputs[0].shape == (1, 3, 4, 4)

     # test with a non-tensor attribute
     UpperCamelCase_     =CustomOutput({"images": [PIL.Image.new("RGB"				,			(4, 4)					)]}					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			UpperCamelCase_					)
     assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
     assert isinstance(outputs["images"]				,			UpperCamelCase_					)
     assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
     assert isinstance(outputs[0]				,			UpperCamelCase_					)
     assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
