






"""simple docstring"""



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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor

class 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=3				,						_lowerCamelCase=1_8				,						_lowerCamelCase=3_0				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						):
      lowercase				       =			size if size is not None else {'shortest_edge': 2_0}
      lowercase				       =			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			num_channels
      lowercase				       =			image_size
      lowercase				       =			min_resolution
      lowercase				       =			max_resolution
      lowercase				       =			do_resize
      lowercase				       =			size
      lowercase				       =			do_center_crop
      lowercase				       =			crop_size







    def      UpperCamelCase_ (     self			):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }


@require_torch
@require_vision
class 			a	(  __UpperCAmelCase,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=MobileNetVaImageProcessor if is_vision_available() else None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			MobileNetVaImageProcessingTester(self			)







    @property
    def      UpperCamelCase_ (     self			):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_resize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'size'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'center_crop'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size				,						{'shortest_edge': 2_0}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 1_8, 'width': 1_8}			)

      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict				,						size=4_2				,						crop_size=8_4			)
      self.assertEqual(image_processor.size				,						{'shortest_edge': 4_2}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 8_4, 'width': 8_4}			)







    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase			)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						Image.Image			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase				,						numpify=_lowerCamelCase			)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						np.ndarray			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase				,						torchify=_lowerCamelCase			)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						torch.Tensor			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)









def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _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(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )


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


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






