





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 	UpperCamelCase_						(     unittest.TestCase    ):




					def __init__(						self ,  lowerCAmelCase_ ,  lowerCAmelCase_=7 ,  lowerCAmelCase_=3 ,  lowerCAmelCase_=18 ,  lowerCAmelCase_=30 ,  lowerCAmelCase_=400 ,  lowerCAmelCase_=True ,  lowerCAmelCase_=None ,  lowerCAmelCase_=True ,  lowerCAmelCase_=None ,  lowerCAmelCase_=True ,  lowerCAmelCase_=[0.5, 0.5, 0.5] ,  lowerCAmelCase_=[0.5, 0.5, 0.5] ,  )						->							int:
										_snake_case													= size if size is not None else {'height': 20, 'width': 20}
										_snake_case													= crop_size if crop_size is not None else {'height': 18, 'width': 18}

										_snake_case													= parent
										_snake_case													= batch_size
										_snake_case													= num_channels
										_snake_case													= image_size
										_snake_case													= min_resolution
										_snake_case													= max_resolution
										_snake_case													= do_resize
										_snake_case													= size
										_snake_case													= do_center_crop
										_snake_case													= crop_size
										_snake_case													= do_normalize
										_snake_case													= image_mean
										_snake_case													= image_std






					def 		lowerCAmelCase	(						self     )						->							str:
										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 	UpperCamelCase_						(     _lowerCamelCase							,     unittest.TestCase    ):
					lowerCAmelCase_							=		DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_							=		True




					def 		lowerCAmelCase	(						self     )						->							str:
										_snake_case													= DeiTImageProcessingTester(self     )




					@property
					def 		lowerCAmelCase	(						self     )						->							Any:
										return self.image_processor_tester.prepare_image_processor_dict()




					def 		lowerCAmelCase	(						self     )						->							List[Any]:
										_snake_case													= self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(_lowercase ,  'do_resize'     )     )
										self.assertTrue(hasattr(_lowercase ,  'size'     )     )
										self.assertTrue(hasattr(_lowercase ,  'do_center_crop'     )     )
										self.assertTrue(hasattr(_lowercase ,  'center_crop'     )     )
										self.assertTrue(hasattr(_lowercase ,  'do_normalize'     )     )
										self.assertTrue(hasattr(_lowercase ,  'image_mean'     )     )
										self.assertTrue(hasattr(_lowercase ,  'image_std'     )     )




					def 		lowerCAmelCase	(						self     )						->							int:
										_snake_case													= 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}     )

										_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Tuple:
										pass




					def 		lowerCAmelCase	(						self     )						->							Union[str, Any]:
										# Initialize image_processing
										_snake_case													= self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										_snake_case													= prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowercase     )
										for image in image_inputs:
															self.assertIsInstance(_lowercase ,  Image.Image     )

										# Test not batched input
										_snake_case													= 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
										_snake_case													= image_processing(_lowercase ,  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 		lowerCAmelCase	(						self     )						->							Optional[int]:
										# Initialize image_processing
										_snake_case													= self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										_snake_case													= prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowercase ,  numpify=_lowercase     )
										for image in image_inputs:
															self.assertIsInstance(_lowercase ,  np.ndarray     )

										# Test not batched input
										_snake_case													= 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
										_snake_case													= image_processing(_lowercase ,  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 		lowerCAmelCase	(						self     )						->							List[str]:
										# Initialize image_processing
										_snake_case													= self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										_snake_case													= prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowercase ,  torchify=_lowercase     )
										for image in image_inputs:
															self.assertIsInstance(_lowercase ,  torch.Tensor     )

										# Test not batched input
										_snake_case													= 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
										_snake_case													= image_processing(_lowercase ,  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'],
										    ) ,  )






class 		__magic_name__ :






     def __init__(							self     )->			int:
         UpperCamelCase_          =		[0]
         UpperCamelCase_          =		0






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			None:
         UpperCamelCase_          =		self.__heap[i]
         while i // 2 > 0:
             if self.__heap[i] > self.__heap[i // 2]:
                 UpperCamelCase_          =		self.__heap[i // 2]
                 UpperCamelCase_          =		temporary
             i //= 2






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			None:
         self.__heap.append(_lowercase     )
         self.__size += 1
         self.__swap_up(self.__size     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			None:
         while self.__size >= 2 * i:
             if 2 * i + 1 > self.__size:
                 UpperCamelCase_          =		2 * i
             else:
                 if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                     UpperCamelCase_          =		2 * i
                 else:
                     UpperCamelCase_          =		2 * i + 1
             UpperCamelCase_          =		self.__heap[i]
             if self.__heap[i] < self.__heap[bigger_child]:
                 UpperCamelCase_          =		self.__heap[bigger_child]
                 UpperCamelCase_          =		temporary
             UpperCamelCase_          =		bigger_child






     def      UpperCAmelCase_							(							self     )->			int:
         UpperCamelCase_          =		self.__heap[1]
         UpperCamelCase_          =		self.__heap[self.__size]
         self.__size -= 1
         self.__heap.pop()
         self.__swap_down(1     )
         return max_value






     @property
     def      UpperCAmelCase_							(							self     )->			List[Any]:
         return self.__heap[1:]






     def __len__(							self     )->			List[str]:
         return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 SCREAMING_SNAKE_CASE						:Optional[int]      =			BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))
