code: 
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	Union[str, Any]     ,			A_:	int		)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __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 			lowercase__  (				A_:	Optional[int]		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __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))
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  from numpy import divide, inta, multiply, ravel, sort, zeros_like
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=int(mask / 2		)
         # copy image size
         __UpperCAmelCase    							=zeros_like(A_		)
         for i in range(A_     ,			gray_img.shape[0] - bd		):
                for j in range(A_     ,			gray_img.shape[1] - bd		):
                       # get mask according with mask
                       __UpperCAmelCase    							=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
                       # calculate mask median
                       __UpperCAmelCase    							=sort(A_		)[inta(divide((multiply(A_     ,			A_		))     ,			2		) + 1		)]
                       __UpperCAmelCase    							=median
         return median_img
  
  
  if __name__ == "__main__":
     # read original image
     __A  							=  imread("../image_data/lena.jpg")
     # turn image in gray scale value
     __A  							=  cvtColor(img, COLOR_BGR2GRAY)
  
     # get values with two different mask size
     __A  							=  median_filter(gray, 3)
     __A  							=  median_filter(gray, 5)
  
     # show result images
     imshow("median filter with 3x3 mask", medianaxa)
     imshow("median filter with 5x5 mask", medianaxa)
     waitKey(0)
  
  
  
  
  