



from __future__ import annotations







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Dict  ,lowerCamelCase_:     int = 0							)	->      None:
       UpperCAmelCase_     :       Any  		=					value
       UpperCAmelCase_     :       Node | None  		=					None
       UpperCAmelCase_     :       Node | None  		=					None







def       lowerCamelCase_  (    _a       :						Node | None   ,		_a       :						Node | None    ):



    '''simple docstring'''




    if treea is None:
        return treea
    if treea is None:
        return treea

    UpperCAmelCase_     :       int  		=					treea.value + treea.value
    UpperCAmelCase_     :       Union[str, Any]  		=					merge_two_binary_trees(treea.left   ,		treea.left    )
    UpperCAmelCase_     :       List[str]  		=					merge_two_binary_trees(treea.right   ,		treea.right    )
    return treea







def       lowerCamelCase_  (    _a       :						Node | None    ):



    '''simple docstring'''




    if root:
        print(root.value    )
        print_preorder(root.left    )
        print_preorder(root.right    )


if __name__ == "__main__":
      UpperCamelCase_									=   Node(1)
      UpperCamelCase_									=   Node(2)
      UpperCamelCase_									=   Node(3)
      UpperCamelCase_									=   Node(4)

      UpperCamelCase_									=   Node(2)
      UpperCamelCase_									=   Node(4)
      UpperCamelCase_									=   Node(6)
      UpperCamelCase_									=   Node(9)
      UpperCamelCase_									=   Node(5)

      print('''Tree1 is: ''')
      print_preorder(treea)
      print('''Tree2 is: ''')
      print_preorder(treea)
      UpperCamelCase_									=   merge_two_binary_trees(treea, treea)
      print('''Merged Tree is: ''')
      print_preorder(merged_tree)


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						=      {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      ["MarkupLMTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      [
	    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MarkupLMForQuestionAnswering",
	    "MarkupLMForSequenceClassification",
	    "MarkupLMForTokenClassification",
	    "MarkupLMModel",
	    "MarkupLMPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
	from .feature_extraction_markuplm import MarkupLMFeatureExtractor
	from .processing_markuplm import MarkupLMProcessor
	from .tokenization_markuplm import MarkupLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_markuplm_fast import MarkupLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_markuplm import (
		    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		    MarkupLMPreTrainedModel,
		)


else:
	import sys

	__SCREAMING_SNAKE_CASE						=      _LazyModule(__name__, globals()["__file__"], _import_structure)




