
"""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


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      lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=7       ,	SCREAMING_SNAKE_CASE__=3       ,	SCREAMING_SNAKE_CASE__=18       ,	SCREAMING_SNAKE_CASE__=30       ,	SCREAMING_SNAKE_CASE__=4_00       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=1 / 2_55       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=8       ,	) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       parent
											SCREAMING_SNAKE_CASE__						:	int									=       batch_size
											SCREAMING_SNAKE_CASE__						:	str									=       num_channels
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       image_size
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       min_resolution
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       max_resolution
											SCREAMING_SNAKE_CASE__						:	int									=       do_rescale
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       rescale_factor
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       do_pad
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       pad_size



				def      __magic_name__   (self   ) ->		List[str]:



											"""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       ,	SCREAMING_SNAKE_CASE__=False       ,	SCREAMING_SNAKE_CASE__=False       ,	SCREAMING_SNAKE_CASE__=False   ) ->		Optional[int]:



											"""simple docstring"""




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

											if equal_resolution:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       []
																		for i in range(self.batch_size   ):
																									image_inputs.append(
																									    np.random.randint(
																									        2_55       ,	size=(self.num_channels, self.max_resolution, self.max_resolution)       ,	dtype=np.uinta   )   )
											else:
																		SCREAMING_SNAKE_CASE__						:	int									=       []
																		for i in range(self.batch_size   ):
																									SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       np.random.choice(np.arange(self.min_resolution       ,	self.max_resolution   )       ,	2   )
																									image_inputs.append(np.random.randint(2_55       ,	size=(self.num_channels, width, height)       ,	dtype=np.uinta   )   )

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

											if torchify:
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [torch.from_numpy(SCREAMING_SNAKE_CASE__   ) for x in image_inputs]

											return image_inputs






@require_torch
@require_vision
class      lowerCAmelCase_						(a__			, unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase				:						int 	=       SwinaSRImageProcessor if is_vision_available() else None



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       SwinaSRImageProcessingTester(self   )



				@property
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""do_rescale"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""rescale_factor"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""do_pad"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""pad_size"""   )   )



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											pass



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       get_image_size(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.image_processor_tester.pad_size

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



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											SCREAMING_SNAKE_CASE__						:	int									=       self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__   )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,	Image.Image   )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:	Dict									=       image_processor(image_inputs[0]       ,	return_tensors="""pt"""   ).pixel_values
											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       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   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__       ,	numpify=SCREAMING_SNAKE_CASE__   )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,	np.ndarray   )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:	Dict									=       image_processor(image_inputs[0]       ,	return_tensors="""pt"""   ).pixel_values
											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	List[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   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__       ,	torchify=SCREAMING_SNAKE_CASE__   )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,	torch.Tensor   )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:	Dict									=       image_processor(image_inputs[0]       ,	return_tensors="""pt"""   ).pixel_values
											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.calculate_expected_size(image_inputs[0]   )
											self.assertEqual(
											    encoded_images.shape       ,	(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,	)






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       index
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       2 * index + 1
							SCREAMING_SNAKE_CASE__						:	Any									=       2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	str									=       left_index

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

							if largest != index:
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Tuple									=       unsorted[index], unsorted[largest]
														heapify(_snake_case       ,_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(_snake_case						)
							for i in range(n // 2 - 1       ,-1       ,-1						):
														heapify(_snake_case       ,_snake_case       ,_snake_case						)
							for i in range(n - 1       ,0       ,-1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       unsorted[i], unsorted[0]
														heapify(_snake_case       ,0       ,_snake_case						)
							return unsorted


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




