






class 							__A	:





				"""simple docstring"""







				def __init__(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=[0]
									__UpperCamelCase :   int									=0


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=self.__heap[i]
									while i // 2 > 0:
														if self.__heap[i] > self.__heap[i // 2]:
																			__UpperCamelCase :   List[str]									=self.__heap[i // 2]
																			__UpperCamelCase :   int									=temporary
														i //= 2


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

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


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									while self.__size >= 2 * i:
														if 2 * i + 1 > self.__size:
																			__UpperCamelCase :   int									=2 * i
														else:
																			if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																								__UpperCamelCase :   Union[str, Any]									=2 * i
																			else:
																								__UpperCamelCase :   Optional[int]									=2 * i + 1
														__UpperCamelCase :   Union[str, Any]									=self.__heap[i]
														if self.__heap[i] < self.__heap[bigger_child]:
																			__UpperCamelCase :   Tuple									=self.__heap[bigger_child]
																			__UpperCamelCase :   Optional[int]									=temporary
														__UpperCamelCase :   str									=bigger_child


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Tuple									=self.__heap[1]
									__UpperCamelCase :   int									=self.__heap[self.__size]
									self.__size -= 1
									self.__heap.pop()
									self.__swap_down(1       )
									return max_value


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self.__heap[1:]







				def __len__(					self       ):




									"""simple docstring"""

									return self.__size


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# create an instance of BinaryHeap
							A_			:Union[str, Any]	    =		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))








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''DebertaV2TokenizerFast''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaV2ForMaskedLM''',
						    '''TFDebertaV2ForQuestionAnswering''',
						    '''TFDebertaV2ForSequenceClassification''',
						    '''TFDebertaV2ForTokenClassification''',
						    '''TFDebertaV2Model''',
						    '''TFDebertaV2PreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaV2ForMaskedLM''',
						    '''DebertaV2ForMultipleChoice''',
						    '''DebertaV2ForQuestionAnswering''',
						    '''DebertaV2ForSequenceClassification''',
						    '''DebertaV2ForTokenClassification''',
						    '''DebertaV2Model''',
						    '''DebertaV2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






