



'''simple docstring'''





from __future__ import annotations

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

UpperCamelCase__    :     Dict			  =   TypeVar('T', bound=bool)


class 							_lowerCAmelCase	(  Generic[T]							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase  )    ->    None:
    A_  :		T						=					value
    A_  :		SkewNode[T] | None						=					None
    A_  :		SkewNode[T] | None						=					None







 @property
 def       UpperCAmelCase_       (      self  )    ->    T:
    return self._value







 @staticmethod
 def       UpperCAmelCase_       (      _lowerCamelCase					,    _lowerCamelCase  )    ->    SkewNode[T] | None:
    if not roota:
       return roota

    if not roota:
       return roota

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

    A_  :		Dict						=					roota
    A_  :		Union[str, Any]						=					roota.right
    A_  :		Optional[Any]						=					roota.left
    A_  :		List[str]						=					SkewNode.merge(_lowerCamelCase					,    _lowerCamelCase  )

    return result


class 							_lowerCAmelCase	(  Generic[T]							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase = ()  )    ->    None:
    A_  :		SkewNode[T] | None						=					None
    if data:
       for item in data:
          self.insert(_lowerCamelCase  )







 def __bool__(      self  )    ->    bool:
    return self._root is not None







 def __iter__(      self  )    ->    Iterator[T]:
    A_  :		list[Any]						=					[]
    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       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    A_  :		Optional[Any]						=					SkewNode.merge(self._root					,    SkewNode(_lowerCamelCase  )  )







 def       UpperCAmelCase_       (      self  )    ->    T | None:
    A_  :		List[str]						=					self.top()
    A_  :		Optional[int]						=					(
        SkewNode.merge(self._root.left					,    self._root.right  ) if self._root else None
    )

    return result







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







 def       UpperCAmelCase_       (      self  )    ->    None:
    A_  :		Dict						=					None


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    :     List[Any]			  =   {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Any			  =   ['LayoutLMTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   [
      'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMForMaskedLM',
      'LayoutLMForSequenceClassification',
      'LayoutLMForTokenClassification',
      'LayoutLMForQuestionAnswering',
      'LayoutLMModel',
      'LayoutLMPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     str			  =   [
      'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLayoutLMForMaskedLM',
      'TFLayoutLMForSequenceClassification',
      'TFLayoutLMForTokenClassification',
      'TFLayoutLMForQuestionAnswering',
      'TFLayoutLMMainLayer',
      'TFLayoutLMModel',
      'TFLayoutLMPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    :     List[str]			  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
