
def 						lowerCamelCase__							(      a	,	a	,	a   )   ->     int:
   _A:						Optional[Any]	    = index
   _A:						Dict	    = 2 * index + 1
   _A:						int	    = 2 * index + 2
   if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
      _A:						Optional[int]	    = left_index

   if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
      _A:						List[Any]	    = right_index

   if largest != index:
      _A    ,						_A:						List[str]	    = unsorted[index], unsorted[largest]
      heapify(a	,	a	,	a   )






def 						lowerCamelCase__							(      a   )   ->     List[Any]:
   _A:						Any	    = len(a   )
   for i in range(n // 2 - 1	,	-1	,	-1   ):
      heapify(a	,	a	,	a   )
   for i in range(n - 1	,	0	,	-1   ):
      _A    ,						_A:						Union[str, Any]	    = unsorted[i], unsorted[0]
      heapify(a	,	0	,	a   )
   return unsorted


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





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size






class UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   def __init__(      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							Optional[int]=7    ,    lowerCAmelCase_  :							Dict=3    ,    lowerCAmelCase_  :							Optional[Any]=1_8    ,    lowerCAmelCase_  :							Union[str, Any]=3_0    ,    lowerCAmelCase_  :							int=4_0_0    ,    lowerCAmelCase_  :							int=True    ,    lowerCAmelCase_  :							Dict=1 / 2_5_5    ,    lowerCAmelCase_  :							Union[str, Any]=True    ,    lowerCAmelCase_  :							str=8    ,    ):

      """simple docstring"""
      _A:						Optional[Any]	    = parent
      _A:						int	    = batch_size
      _A:						List[str]	    = num_channels
      _A:						Union[str, Any]	    = image_size
      _A:						Optional[int]	    = min_resolution
      _A:						int	    = max_resolution
      _A:						int	    = do_rescale
      _A:						Optional[Any]	    = rescale_factor
      _A:						List[Any]	    = do_pad
      _A:						Optional[int]	    = pad_size


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      return {
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
          "pad_size": self.pad_size,
      }


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[Any]=False    ,    lowerCAmelCase_  :							Tuple=False    ,    lowerCAmelCase_  :							Dict=False  ):

      """simple docstring"""
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         _A:						Dict	    = []
         for i in range(self.batch_size  ):
            image_inputs.append(
                np.random.randint(
                    2_5_5    ,    size=(self.num_channels, self.max_resolution, self.max_resolution)    ,    dtype=np.uinta  )  )
      else:
         _A:						List[str]	    = []
         for i in range(self.batch_size  ):
            _A    ,						_A:						Optional[Any]	    = np.random.choice(np.arange(self.min_resolution    ,    self.max_resolution  )    ,    2  )
            image_inputs.append(np.random.randint(2_5_5    ,    size=(self.num_channels, width, height)    ,    dtype=np.uinta  )  )

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         _A:						Union[str, Any]	    = [Image.fromarray(np.moveaxis(lowerCAmelCase_    ,    0    ,    -1  )  ) for x in image_inputs]

      if torchify:
         _A:						List[Any]	    = [torch.from_numpy(lowerCAmelCase_  ) for x in image_inputs]

      return image_inputs






@require_torch
@require_vision
class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





   '''simple docstring'''
   __UpperCamelCase	:					int				       =					SwinaSRImageProcessor if is_vision_available() else None


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						int	    = SwinaSRImageProcessingTester(self  )


   @property
   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Any	    = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_rescale'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''rescale_factor'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_pad'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''pad_size'''  )  )


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Dict  ):

      """simple docstring"""
      _A    ,						_A:						Any	    = get_image_size(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = self.image_processor_tester.pad_size

      _A:						Optional[Any]	    = (old_height // size + 1) * size - old_height
      _A:						int	    = (old_width // size + 1) * size - old_width
      return old_height + pad_height, old_width + pad_width


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[int]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _A:						str	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    Image.Image  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						str	    = self.calculate_expected_size(np.array(image_inputs[0]  )  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Union[str, Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _A:						List[str]	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    numpify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    np.ndarray  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Union[str, Any]	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _A:						Tuple	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    torchify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    torch.Tensor  )

      # Test not batched input
      _A:						List[str]	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Dict	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )



