
class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	def __init__(						self			:					int			):


				'''simple docstring'''


				_snake_case						=    [0]
				_snake_case						=    0



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    self.__heap[i]
				while i // 2 > 0:
							if self.__heap[i] > self.__heap[i // 2]:
										_snake_case						=    self.__heap[i // 2]
										_snake_case						=    temporary
							i //= 2



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					int			):


				'''simple docstring'''


				self.__heap.append(lowercase			)
				self.__size += 1
				self.__swap_up(self.__size			)



	def 				A							(						self			:					Tuple    ,						lowercase			:					int			):


				'''simple docstring'''


				while self.__size >= 2 * i:
							if 2 * i + 1 > self.__size:
										_snake_case						=    2 * i
							else:
										if self.__heap[2 * i] > self.__heap[2 * i + 1]:
													_snake_case						=    2 * i
										else:
													_snake_case						=    2 * i + 1
							_snake_case						=    self.__heap[i]
							if self.__heap[i] < self.__heap[bigger_child]:
										_snake_case						=    self.__heap[bigger_child]
										_snake_case						=    temporary
							_snake_case						=    bigger_child



	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    self.__heap[1]
				_snake_case						=    self.__heap[self.__size]
				self.__size -= 1
				self.__heap.pop()
				self.__swap_down(1			)
				return max_value



	@property
	def 				A							(						self			:					Any			):


				'''simple docstring'''


				return self.__heap[1:]





	def __len__(						self			:					List[str]			):


				'''simple docstring'''


				return self.__size


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# create an instance of BinaryHeap
			_lowerCamelCase :       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))

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)