

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()








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__)





