

"""simple docstring"""






from __future__ import annotations

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

lowerCamelCase__			:					Any							  =					TypeVar('''T''', bound=bool)






class       _UpperCAmelCase     (						Generic[T]):
			def __init__(						self ,						_A       )      ->      None:






				'''simple docstring'''







				_UpperCAmelCase      :							T       =   value
				_UpperCAmelCase      :							RandomizedHeapNode[T] | None       =   None
				_UpperCAmelCase      :							RandomizedHeapNode[T] | None       =   None
			@property
			def 							__snake_case	(						self       )      ->      T:






				'''simple docstring'''







				return self._value
			@staticmethod
			def 							__snake_case	(						_A ,						_A       )      ->      RandomizedHeapNode[T] | None:






				'''simple docstring'''







				if not roota:
					return roota

				if not roota:
					return roota

				if roota.value > roota.value:
					_UpperCAmelCase  ,				_UpperCAmelCase      :							Dict       =   roota, roota

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

				_UpperCAmelCase      :							Optional[int]       =   RandomizedHeapNode.merge(roota.left ,						_A       )

				return roota






class       _UpperCAmelCase     (						Generic[T]):
			def __init__(						self ,						_A = ()       )      ->      None:






				'''simple docstring'''







				_UpperCAmelCase      :							RandomizedHeapNode[T] | None       =   None

				if data:
					for item in data:
						self.insert(_A       )
			def 							__snake_case	(						self ,						_A       )      ->      None:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   RandomizedHeapNode.merge(self._root ,						RandomizedHeapNode(_A       )       )
			def 							__snake_case	(						self       )      ->      T | None:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   self.top()

				if self._root is None:
					return None

				_UpperCAmelCase      :							Optional[Any]       =   RandomizedHeapNode.merge(self._root.left ,						self._root.right       )

				return result
			def 							__snake_case	(						self       )      ->      T:






				'''simple docstring'''







				if not self._root:
					raise IndexError("""Can't get top element for the empty heap."""       )
				return self._root.value
			def 							__snake_case	(						self       )      ->      None:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   None
			def 							__snake_case	(						self       )      ->      list[Any]:






				'''simple docstring'''







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

				return result
			def __bool__(						self       )      ->      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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			:					Any							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





