

from __future__ import annotations

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

UpperCAmelCase__	:       Tuple			      =      TypeVar("""T""", bound=bool)







class     a__    (				Generic[T]    ):







  """simple docstring"""




  def __init__(     self      :	int			,			UpperCAmelCase__      :	T						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: T					       =				value
        SCREAMING_SNAKE_CASE	: RandomizedHeapNode[T] | None					       =				None
        SCREAMING_SNAKE_CASE	: RandomizedHeapNode[T] | None					       =				None


  @property
  def 	_lowercase (     self      :	Union[str, Any]						)   ->T:
        """simple docstring"""







        return self._value




  @staticmethod
  def 	_lowercase (     UpperCAmelCase__      :	RandomizedHeapNode[T] | None			,			UpperCAmelCase__      :	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	: Union[str, Any]					       =				roota, roota

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

        SCREAMING_SNAKE_CASE	: List[str]					       =				RandomizedHeapNode.merge(roota.left			,			UpperCAmelCase__						)

        return roota







class     a__    (				Generic[T]    ):







  """simple docstring"""




  def __init__(     self      :	int			,			UpperCAmelCase__      :	Iterable[T] | None = ()						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: RandomizedHeapNode[T] | None					       =				None

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


  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	T						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				RandomizedHeapNode.merge(self._root			,			RandomizedHeapNode(UpperCAmelCase__						)						)


  def 	_lowercase (     self      :	List[str]						)   ->T | None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				self.top()

        if self._root is None:
              return None

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

        return result


  def 	_lowercase (     self      :	int						)   ->T:
        """simple docstring"""







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


  def 	_lowercase (     self      :	List[str]						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				None


  def 	_lowercase (     self      :	List[str]						)   ->list[Any]:
        """simple docstring"""







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

        return result




  def __bool__(     self      :	Tuple						)   ->bool:
        """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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__	:       int			      =      {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__	:       int			      =      [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__	:       int			      =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




