






"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Dict      				=			{
    """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_						:					List[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_						:					Optional[int]      				=			[
        """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_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""





from __future__ import annotations


class   _lowercase			:


      def __init__(     self					:						int				, a					:						int = 0    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =value
            __snake_case   :     Node | None	      =None
            __snake_case   :     Node | None	      =None



def      __lowercase       (     a			:					Node | None							,		a			:					Node | None			)       ->       Node | None:
      if treea is None:
            return treea
      if treea is None:
            return treea

      __snake_case   :     Dict	      =treea.value + treea.value
      __snake_case   :     int	      =merge_two_binary_trees(treea.left							,		treea.left			)
      __snake_case   :     List[str]	      =merge_two_binary_trees(treea.right							,		treea.right			)
      return treea



def      __lowercase       (     a			:					Node | None			)       ->       None:
      if root:
            print(root.value			)
            print_preorder(root.left			)
            print_preorder(root.right			)


if __name__ == "__main__":
    UpperCamelCase_						:					List[str]      				=			Node(1)
    UpperCamelCase_						:					List[Any]      				=			Node(2)
    UpperCamelCase_						:					Optional[Any]      				=			Node(3)
    UpperCamelCase_						:					int      				=			Node(4)

    UpperCamelCase_						:					int      				=			Node(2)
    UpperCamelCase_						:					List[str]      				=			Node(4)
    UpperCamelCase_						:					Any      				=			Node(6)
    UpperCamelCase_						:					Union[str, Any]      				=			Node(9)
    UpperCamelCase_						:					Dict      				=			Node(5)

    print("""Tree1 is: """)
    print_preorder(treea)
    print("""Tree2 is: """)
    print_preorder(treea)
    UpperCamelCase_						:					Optional[Any]      				=			merge_two_binary_trees(treea, treea)
    print("""Merged Tree is: """)
    print_preorder(merged_tree)


