






from __future__ import annotations

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

__UpperCAmelCase      =				TypeVar("T", bound=bool)
class 	a_(		Generic[T]	):



     """simple docstring"""



     def __init__(		self					:	Tuple  ,  lowerCAmelCase__					:	T)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		value
      SCREAMING_SNAKE_CASE				    =		None
      SCREAMING_SNAKE_CASE				    =		None





     @property
     def     __UpperCamelCase						(		self					:	int)							->	T:

      """simple docstring"""
      return self._value





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	RandomizedHeapNode[T] | None  ,  lowerCAmelCase__					:	RandomizedHeapNode[T] | None)							->	RandomizedHeapNode[T] | None:

      """simple docstring"""
      if not roota:
       return roota

      if not roota:
       return roota

      if roota.value > roota.value:
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		roota, roota

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

      SCREAMING_SNAKE_CASE				    =		RandomizedHeapNode.merge(roota.left  ,  lowerCAmelCase__)

      return roota

class 	a_(		Generic[T]	):



     """simple docstring"""



     def __init__(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	Iterable[T] | None = ())							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		None

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





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	T)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		RandomizedHeapNode.merge(self._root  ,  RandomizedHeapNode(lowerCAmelCase__))





     def     __UpperCamelCase						(		self					:	Optional[int])							->	T | None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.top()

      if self._root is None:
       return None

      SCREAMING_SNAKE_CASE				    =		RandomizedHeapNode.merge(self._root.left  ,  self._root.right)

      return result





     def     __UpperCamelCase						(		self					:	Tuple)							->	T:

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





     def     __UpperCamelCase						(		self					:	Optional[int])							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		None





     def     __UpperCamelCase						(		self					:	List[str])							->	list[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		[]
      while self:
       result.append(self.pop())

      return result





     def __bool__(		self					:	Optional[int])							->	bool:

      """simple docstring"""
      return self._root is not None


if __name__ == "__main__":
      import doctest

      doctest.testmod()












'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

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


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''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

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
