code: 
  
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  from typing import Any, Generic, TypeVar
  
  UpperCamelCase__				       =			TypeVar('T', bound=bool)
  
  class 						UpperCAmelCase__   (      Generic[T]		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Optional[int]	,      UpperCamelCase       :       T		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				T           =			value
              _lowercase					:				RandomizedHeapNode[T] | None           =			None
              _lowercase					:				RandomizedHeapNode[T] | None           =			None
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return self._value
  
  
  
  
       @staticmethod
       def   lowerCAmelCase_   ( UpperCamelCase       :       RandomizedHeapNode[T] | None	,      UpperCamelCase       :       RandomizedHeapNode[T] | None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if not roota:
                     return roota
  
              if not roota:
                     return roota
  
              if roota.value > roota.value:
                     _lowercase       ,	_lowercase					:				List[str]           =			roota, roota
  
              if random.choice([True, False]		):
                     _lowercase       ,	_lowercase					:				List[Any]           =			roota.right, roota.left
  
              _lowercase					:				Optional[Any]           =			RandomizedHeapNode.merge(roota.left	,      UpperCamelCase		)
  
              return roota
  
  
  
  
  class 						UpperCAmelCase__   (      Generic[T]		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Union[str, Any]	,      UpperCamelCase       :       Iterable[T] | None = ()		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				RandomizedHeapNode[T] | None           =			None
  
              if data:
                     for item in data:
                            self.insert(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       T		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			RandomizedHeapNode.merge(self._root	,      RandomizedHeapNode(UpperCamelCase		)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			self.top()
  
              if self._root is None:
                     return None
  
              _lowercase					:				Optional[Any]           =			RandomizedHeapNode.merge(self._root.left	,      self._root.right		)
  
              return result
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if not self._root:
                     raise IndexError('''Can\'t get top element for the empty heap.'''		)
              return self._root.value
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			None
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			[]
              while self:
                     result.append(self.pop()		)
  
              return result
  
  
  
  
       def __bool__( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              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
  
  
  UpperCamelCase__				       =			{
      'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
      'tokenization_realm': ['RealmTokenizer'],
  }
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			['RealmTokenizerFast']
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RealmEmbedder',
         'RealmForOpenQA',
         'RealmKnowledgeAugEncoder',
         'RealmPreTrainedModel',
         'RealmReader',
         'RealmScorer',
         'load_tf_weights_in_realm',
     ]
     UpperCamelCase__				       =			['RealmRetriever']
  
  
  if TYPE_CHECKING:
     from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
     from .tokenization_realm import RealmTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_realm import RealmTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_realm import (
            REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RealmEmbedder,
            RealmForOpenQA,
            RealmKnowledgeAugEncoder,
            RealmPreTrainedModel,
            RealmReader,
            RealmScorer,
            load_tf_weights_in_realm,
        )
        from .retrieval_realm import RealmRetriever
  
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)