

from __future__ import annotations

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

UpperCAmelCase__	:       Optional[Any]			      =      TypeVar("""T""", bound=bool)







class     a__    (				Generic[T]    ):







  """simple docstring"""




  def __init__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	T						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				value
        SCREAMING_SNAKE_CASE	: Dict					       =				None
        SCREAMING_SNAKE_CASE	: Dict					       =				None


  @property
  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        return self._value




  @staticmethod
  def 	_lowercase (     UpperCAmelCase__      :	SkewNode[T] | None			,			UpperCAmelCase__      :	SkewNode[T] | None						)   ->Union[str, Any]:
        """simple docstring"""







        if not roota:
              return roota

        if not roota:
              return roota

        if roota.value > roota.value:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				roota, roota

        SCREAMING_SNAKE_CASE	: Dict					       =				roota
        SCREAMING_SNAKE_CASE	: int					       =				roota.right
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				roota.left
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				SkewNode.merge(_lowerCamelCase			,			_lowerCamelCase						)

        return result







class     a__    (				Generic[T]    ):







  """simple docstring"""




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







        SCREAMING_SNAKE_CASE	: List[Any]					       =				None
        if data:
              for item in data:
                    self.insert(_lowerCamelCase						)


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







        return self._root is not None


  def __iter__(     self      :	List[Any]						)   ->Optional[int]:
        """simple docstring"""







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

        # Pushing items back to the heap not to clear it.
        for item in result:
              self.insert(_lowerCamelCase						)

        return iter(_lowerCamelCase						)


  def 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	T						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				SkewNode.merge(self._root			,			SkewNode(_lowerCamelCase						)						)


  def 	_lowercase (     self      :	Tuple						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.top()
        SCREAMING_SNAKE_CASE	: Tuple					       =				(
            SkewNode.merge(self._root.left			,			self._root.right						) if self._root else None
        )

        return result


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







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




  def 	_lowercase (     self      :	Tuple						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				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__)




