





'''simple docstring'''






class 				UpperCAmelCase  :




      def __init__(    self    :				Optional[Any]						)       ->   List[str]:
            _lowerCAmelCase							  =							[0]
            _lowerCAmelCase							  =							0




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            _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        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				int						)       ->   None:
            self.__heap.append(__snake_case						)
            self.__size += 1
            self.__swap_up(self.__size						)




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            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        lowercase__					(    self    :				Tuple						)       ->   int:
            _lowerCAmelCase							  =							self.__heap[1]
            _lowerCAmelCase							  =							self.__heap[self.__size]
            self.__size -= 1
            self.__heap.pop()
            self.__swap_down(1						)
            return max_value




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




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


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A__       :     Tuple          =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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[Any]          =[
          '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MPNetForMaskedLM''',
          '''MPNetForMultipleChoice''',
          '''MPNetForQuestionAnswering''',
          '''MPNetForSequenceClassification''',
          '''MPNetForTokenClassification''',
          '''MPNetLayer''',
          '''MPNetModel''',
          '''MPNetPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     str          =[
          '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFMPNetEmbeddings''',
          '''TFMPNetForMaskedLM''',
          '''TFMPNetForMultipleChoice''',
          '''TFMPNetForQuestionAnswering''',
          '''TFMPNetForSequenceClassification''',
          '''TFMPNetForTokenClassification''',
          '''TFMPNetMainLayer''',
          '''TFMPNetModel''',
          '''TFMPNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      A__       :     List[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






