code: 
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self					):
           snake_case					=       [0]
           snake_case					=       0
      def 	a_    ( self    ,      __snake_case					):
           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    ,      __snake_case					):
           self.__heap.append(UpperCAmelCase_					)
           self.__size += 1
           self.__swap_up(self.__size					)
      def 	a_    ( self    ,      __snake_case					):
           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					):
           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					):
           return self.__heap[1:]
  
  
  
  
      def __len__( self					):
           return self.__size
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
   # create an instance of BinaryHeap
   _SCREAMING_SNAKE_CASE    :				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))
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  