
"""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 DeiTImageProcessor



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__=None       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=None       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]       ,	) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       size if size is not None else {"""height""": 20, """width""": 20}
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       parent
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       batch_size
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       num_channels
											SCREAMING_SNAKE_CASE__						:	List[str]									=       image_size
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       min_resolution
											SCREAMING_SNAKE_CASE__						:	Dict									=       max_resolution
											SCREAMING_SNAKE_CASE__						:	str									=       do_resize
											SCREAMING_SNAKE_CASE__						:	List[str]									=       size
											SCREAMING_SNAKE_CASE__						:	str									=       do_center_crop
											SCREAMING_SNAKE_CASE__						:	Dict									=       crop_size
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       do_normalize
											SCREAMING_SNAKE_CASE__						:	Dict									=       image_mean
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       image_std



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






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


				"""simple docstring"""



				__UpperCamelCase				:						List[str] 	=       DeiTImageProcessor if is_vision_available() else None
				__UpperCamelCase				:						Dict 	=       True



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       DeiTImageProcessingTester(self   )



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""do_resize"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""size"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""do_center_crop"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""center_crop"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""do_normalize"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""image_mean"""   )   )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,	"""image_std"""   )   )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       self.image_processing_class.from_dict(self.image_processor_dict   )
											self.assertEqual(image_processor.size       ,	{"""height""": 20, """width""": 20}   )
											self.assertEqual(image_processor.crop_size       ,	{"""height""": 18, """width""": 18}   )

											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.image_processing_class.from_dict(self.image_processor_dict       ,	size=42       ,	crop_size=84   )
											self.assertEqual(image_processor.size       ,	{"""height""": 42, """width""": 42}   )
											self.assertEqual(image_processor.crop_size       ,	{"""height""": 84, """width""": 84}   )



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											pass



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       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
											SCREAMING_SNAKE_CASE__						:	Dict									=       image_processing(SCREAMING_SNAKE_CASE__       ,	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      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__						:	Any									=       prepare_image_inputs(self.image_processor_tester       ,	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__						:	Tuple									=       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
											SCREAMING_SNAKE_CASE__						:	str									=       image_processing(SCREAMING_SNAKE_CASE__       ,	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      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       prepare_image_inputs(self.image_processor_tester       ,	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__						:	List[Any]									=       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
											SCREAMING_SNAKE_CASE__						:	int									=       image_processing(SCREAMING_SNAKE_CASE__       ,	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"""],
											    )       ,	)






"""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))




