




class  lowerCamelCase     :






			'''simple docstring'''





			def __init__(			self      : List[str]	)    -> str:
								__magic_name__				:						Tuple     				=				[0]
								__magic_name__				:						List[str]     				=				0

			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : int	)    -> None:
								__magic_name__				:						Any     				=				self.__heap[i]
								while i // 2 > 0:
													if self.__heap[i] > self.__heap[i // 2]:
																		__magic_name__				:						List[str]     				=				self.__heap[i // 2]
																		__magic_name__				:						List[Any]     				=				temporary
													i //= 2

			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : int	)    -> None:
								self.__heap.append(lowerCamelCase_	)
								self.__size += 1
								self.__swap_up(self.__size	)

			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : int	)    -> None:
								while self.__size >= 2 * i:
													if 2 * i + 1 > self.__size:
																		__magic_name__				:						Optional[Any]     				=				2 * i
													else:
																		if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																							__magic_name__				:						Dict     				=				2 * i
																		else:
																							__magic_name__				:						List[Any]     				=				2 * i + 1
													__magic_name__				:						Union[str, Any]     				=				self.__heap[i]
													if self.__heap[i] < self.__heap[bigger_child]:
																		__magic_name__				:						Dict     				=				self.__heap[bigger_child]
																		__magic_name__				:						str     				=				temporary
													__magic_name__				:						Tuple     				=				bigger_child

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> int:
								__magic_name__				:						Dict     				=				self.__heap[1]
								__magic_name__				:						Optional[int]     				=				self.__heap[self.__size]
								self.__size -= 1
								self.__heap.pop()
								self.__swap_down(1	)
								return max_value

			@property
			def 				UpperCAmelCase__							(			self      : int	)    -> str:
								return self.__heap[1:]


			def __len__(			self      : Optional[int]	)    -> Optional[int]:
								return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	__lowerCamelCase :    Dict      						=      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))









import random
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():
		import PIL

		from transformers import FlavaImageProcessor
		from transformers.image_utils import PILImageResampling
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)
else:
		__A    :				List[str]    						=	None




class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						List[Any]							,  a							:						Tuple=7							,  a							:						Dict=3							,  a							:						List[Any]=30							,  a							:						List[Any]=400							,  a							:						Optional[Any]=True							,  a							:						Optional[int]=None							,  a							:						Any=True							,  a							:						Union[str, Any]=None							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Union[str, Any]=1 / 255							,  a							:						Optional[Any]=True							,  a							:						int=FLAVA_IMAGE_MEAN							,  a							:						int=FLAVA_IMAGE_STD							,  a							:						Tuple=14							,  a							:						str=75							,  a							:						str=None							,  a							:						int=16							,  a							:						List[str]=0.3							,  a							:						Optional[Any]=None							,  a							:						int=True							,  a							:						int=None							,  a							:						Optional[int]=None							,  a							:						Optional[Any]=True							,  a							:						List[Any]=None							,  a							:						str=True							,  a							:						int=True							,  a							:						str=FLAVA_CODEBOOK_MEAN							,  a							:						str=FLAVA_CODEBOOK_STD							,  )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       size if size is not None else {"""height""": 224, """width""": 224}
										SCREAMING_SNAKE_CASE     =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										SCREAMING_SNAKE_CASE     =       codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
										SCREAMING_SNAKE_CASE     =       codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

										SCREAMING_SNAKE_CASE     =       parent
										SCREAMING_SNAKE_CASE     =       batch_size
										SCREAMING_SNAKE_CASE     =       num_channels
										SCREAMING_SNAKE_CASE     =       do_resize
										SCREAMING_SNAKE_CASE     =       do_rescale
										SCREAMING_SNAKE_CASE     =       rescale_factor
										SCREAMING_SNAKE_CASE     =       min_resolution
										SCREAMING_SNAKE_CASE     =       max_resolution
										SCREAMING_SNAKE_CASE     =       size
										SCREAMING_SNAKE_CASE     =       resample if resample is not None else PILImageResampling.BICUBIC
										SCREAMING_SNAKE_CASE     =       do_normalize
										SCREAMING_SNAKE_CASE     =       image_mean
										SCREAMING_SNAKE_CASE     =       image_std
										SCREAMING_SNAKE_CASE     =       do_center_crop
										SCREAMING_SNAKE_CASE     =       crop_size

										SCREAMING_SNAKE_CASE     =       input_size_patches
										SCREAMING_SNAKE_CASE     =       total_mask_patches
										SCREAMING_SNAKE_CASE     =       mask_group_max_patches
										SCREAMING_SNAKE_CASE     =       mask_group_min_patches
										SCREAMING_SNAKE_CASE     =       mask_group_min_aspect_ratio
										SCREAMING_SNAKE_CASE     =       mask_group_max_aspect_ratio

										SCREAMING_SNAKE_CASE     =       codebook_do_resize
										SCREAMING_SNAKE_CASE     =       codebook_size
										SCREAMING_SNAKE_CASE     =       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
										SCREAMING_SNAKE_CASE     =       codebook_do_center_crop
										SCREAMING_SNAKE_CASE     =       codebook_crop_size
										SCREAMING_SNAKE_CASE     =       codebook_do_map_pixels
										SCREAMING_SNAKE_CASE     =       codebook_do_normalize
										SCREAMING_SNAKE_CASE     =       codebook_image_mean
										SCREAMING_SNAKE_CASE     =       codebook_image_std





							def  _UpperCAmelCase  (       self							:						str      )      ->       str:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "resample": self.resample,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "input_size_patches": self.input_size_patches,
										    "total_mask_patches": self.total_mask_patches,
										    "mask_group_max_patches": self.mask_group_max_patches,
										    "mask_group_min_patches": self.mask_group_min_patches,
										    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
										    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
										    "codebook_do_resize": self.codebook_do_resize,
										    "codebook_size": self.codebook_size,
										    "codebook_resample": self.codebook_resample,
										    "codebook_do_center_crop": self.codebook_do_center_crop,
										    "codebook_crop_size": self.codebook_crop_size,
										    "codebook_do_map_pixels": self.codebook_do_map_pixels,
										    "codebook_do_normalize": self.codebook_do_normalize,
										    "codebook_image_mean": self.codebook_image_mean,
										    "codebook_image_std": self.codebook_image_std,
										}





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       List[Any]:
										return (self.size["height"], self.size["width"])





							def  _UpperCAmelCase  (       self							:						int      )      ->       int:
										return (
										    (self.input_size_patches, self.input_size_patches)
										    if not isinstance(self.input_size_patches							,  a      )
										    else self.input_size_patches
										)







							def  _UpperCAmelCase  (       self							:						str      )      ->       List[Any]:
										return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class       UpperCAmelCase_   ( A		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  FlavaImageProcessor if is_vision_available() else None
							a__    	=  None





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       FlavaImageProcessingTester(self      )





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





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(a							,  """image_mean"""      )      )
										self.assertTrue(hasattr(a							,  """image_std"""      )      )
										self.assertTrue(hasattr(a							,  """do_normalize"""      )      )
										self.assertTrue(hasattr(a							,  """do_resize"""      )      )
										self.assertTrue(hasattr(a							,  """resample"""      )      )
										self.assertTrue(hasattr(a							,  """crop_size"""      )      )
										self.assertTrue(hasattr(a							,  """do_center_crop"""      )      )
										self.assertTrue(hasattr(a							,  """do_rescale"""      )      )
										self.assertTrue(hasattr(a							,  """rescale_factor"""      )      )
										self.assertTrue(hasattr(a							,  """masking_generator"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_resize"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_size"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_resample"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_center_crop"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_crop_size"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_map_pixels"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_normalize"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_image_mean"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_image_std"""      )      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE     =       self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size							,  {"""height""": 224, """width""": 224}      )
										self.assertEqual(image_processor.crop_size							,  {"""height""": 224, """width""": 224}      )
										self.assertEqual(image_processor.codebook_size							,  {"""height""": 112, """width""": 112}      )
										self.assertEqual(image_processor.codebook_crop_size							,  {"""height""": 112, """width""": 112}      )

										SCREAMING_SNAKE_CASE     =       self.image_processing_class.from_dict(
										    self.image_processor_dict							,  size=42							,  crop_size=84							,  codebook_size=33							,  codebook_crop_size=66      )
										self.assertEqual(image_processor.size							,  {"""height""": 42, """width""": 42}      )
										self.assertEqual(image_processor.crop_size							,  {"""height""": 84, """width""": 84}      )
										self.assertEqual(image_processor.codebook_size							,  {"""height""": 33, """width""": 33}      )
										self.assertEqual(image_processor.codebook_crop_size							,  {"""height""": 66, """width""": 66}      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Optional[int]:
										pass





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

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_tensors="""pt"""      )

										# Test no bool masked pos
										self.assertFalse("""bool_masked_pos""" in encoded_images      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()

										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

										# Test batched
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()

										# Test no bool masked pos
										self.assertFalse("""bool_masked_pos""" in encoded_images      )

										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )





							def  _UpperCAmelCase  (       self							:						Any							,  a							:						List[str]							,  a							:						Tuple      )      ->       Union[str, Any]:
										# Initialize image_processing
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random tensors
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a							,  **a      )
										for image in image_inputs:
													self.assertIsInstance(a							,  a      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_tensors="""pt"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_image_mask=a							,  return_tensors="""pt"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_mask_size()
										self.assertEqual(
										    encoded_images.bool_masked_pos.shape							,  (
										        self.image_processor_tester.batch_size,
										        expected_height,
										        expected_width,
										    )							,  )

										# Test batched
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      ).pixel_values

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )

										# Test masking
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_image_mask=a							,  return_tensors="""pt"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_mask_size()
										self.assertEqual(
										    encoded_images.bool_masked_pos.shape							,  (
										        self.image_processor_tester.batch_size,
										        expected_height,
										        expected_width,
										    )							,  )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Dict:
										self._test_call_framework(np.ndarray							,  prepare_kwargs={"""numpify""": True}      )





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Dict:
										self._test_call_framework(torch.Tensor							,  prepare_kwargs={"""torchify""": True}      )





							def  _UpperCAmelCase  (       self							:						str      )      ->       Optional[int]:
										# Initialize image_processing
										random.seed(1_234      )
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a							,  torchify=a      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_image_mask=a							,  return_tensors="""pt"""      )
										self.assertEqual(encoded_images.bool_masked_pos.sum().item()							,  75      )







							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										# Initialize image_processing
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a      )
										for image in image_inputs:
													self.assertIsInstance(a							,  PIL.Image.Image      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_codebook_pixels=a							,  return_tensors="""pt"""      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_codebook_image_size()
										self.assertEqual(
										    encoded_images.codebook_pixel_values.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

										# Test batched
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_codebook_pixels=a							,  return_tensors="""pt"""      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_codebook_image_size()
										self.assertEqual(
										    encoded_images.codebook_pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )






