code: 
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  from typing import Any, Generic, TypeVar
  
  __A   :     Optional[int]      =			TypeVar('''T''', bound=bool)
  
  
  
  
  
  
  class    __A				(							Generic[T] ):
  
  
  
  
  
  
      def __init__(	self			:    str    ,				UpperCAmelCase_			:    T    ):
          lowerCAmelCase				:			T    			=      value
          lowerCAmelCase				:			RandomizedHeapNode[T] | None    			=      None
          lowerCAmelCase				:			RandomizedHeapNode[T] | None    			=      None
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Dict    ):
          return self._value
  
  
  
  
  
  
      @staticmethod
      def 	lowercase__ (	UpperCAmelCase_			:    RandomizedHeapNode[T] | None    ,				UpperCAmelCase_			:    RandomizedHeapNode[T] | None    ):
          if not roota:
              return roota
  
          if not roota:
              return roota
  
          if roota.value > roota.value:
              lowerCAmelCase			,  lowerCAmelCase				:			List[Any]    			=      roota, roota
  
          if random.choice([True, False]    ):
              lowerCAmelCase			,  lowerCAmelCase				:			Optional[Any]    			=      roota.right, roota.left
  
          lowerCAmelCase				:			Tuple    			=      RandomizedHeapNode.merge(roota.left    ,				UpperCAmelCase_    )
  
          return roota
  
  
  
  
  
  
  class    __A				(							Generic[T] ):
  
  
  
  
  
  
      def __init__(	self			:    Dict    ,				UpperCAmelCase_			:    Iterable[T] | None = ()    ):
          lowerCAmelCase				:			RandomizedHeapNode[T] | None    			=      None
  
          if data:
              for item in data:
                  self.insert(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    T    ):
          lowerCAmelCase				:			List[str]    			=      RandomizedHeapNode.merge(self._root    ,				RandomizedHeapNode(UpperCAmelCase_    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			int    			=      self.top()
  
          if self._root is None:
              return None
  
          lowerCAmelCase				:			Optional[Any]    			=      RandomizedHeapNode.merge(self._root.left    ,				self._root.right    )
  
          return result
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'    )
          return self._root.value
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			str    			=      None
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Optional[int]    			=      []
          while self:
              result.append(self.pop()    )
  
          return result
  
  
  
  
  
  
      def __bool__(	self			:    Tuple    ):
          return self._root is not None
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A   :     Optional[int]      =			{
      '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
      '''tokenization_splinter''': ['''SplinterTokenizer'''],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Optional[int]      =			['''SplinterTokenizerFast''']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Union[str, Any]      =			[
       '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SplinterForQuestionAnswering''',
       '''SplinterForPreTraining''',
       '''SplinterLayer''',
       '''SplinterModel''',
       '''SplinterPreTrainedModel''',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  