class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					[0]
        lowerCAmelCase__     	=					0




 def 		__snake_case						(			self					:  List[str]			,    lowercase__					:  int):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  int):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  int			,    lowercase__					:  int):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  Dict):







        '''simple docstring'''



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




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







        '''simple docstring'''



        return self.__heap[1:]




 def __len__(			self					:  Tuple):







        '''simple docstring'''



        return self.__size


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # create an instance of BinaryHeap
    lowerCAmelCase__							= 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 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__)
