'''simple docstring'''


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

snake_case__	:     Optional[int]      =    TypeVar('''T''', bound=bool)






class 	__SCREAMING_SNAKE_CASE		(			Generic[T]			):







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = value
       UpperCAmelCase_				:  Union[str, Any]  = None
       UpperCAmelCase_				:  Dict  = None


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self._value






  @staticmethod
  def _UpperCamelCase	(					snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if not roota:
            return roota

       if not roota:
            return roota

       if roota.value > roota.value:
            UpperCAmelCase_  ,     UpperCAmelCase_				:  int  = roota, roota

       if random.choice([True, False]		):
            UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = roota.right, roota.left

       UpperCAmelCase_				:  Any  = RandomizedHeapNode.merge(roota.left ,						A__		)

       return roota







class 	__SCREAMING_SNAKE_CASE		(			Generic[T]			):







  '''simple docstring'''






  def __init__(					self ,						snake_case_ = ()		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = None

       if data:
            for item in data:
                 self.insert(A__		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = RandomizedHeapNode.merge(self._root ,						RandomizedHeapNode(A__		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = self.top()

       if self._root is None:
            return None

       UpperCAmelCase_				:  Union[str, Any]  = RandomizedHeapNode.merge(self._root.left ,						self._root.right		)

       return result


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if not self._root:
            raise IndexError('Can\'t get top element for the empty heap.'		)
       return self._root.value


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = None


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = []
       while self:
            result.append(self.pop()		)

       return result






  def __bool__(					self		):




       '''simple docstring'''

       return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

