"""simple docstring"""







from typing import TYPE_CHECKING

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


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


"""simple docstring"""







class      _lowercase						:

				def __init__(       self    :      Tuple    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						data
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						None


def 					a__ (						UpperCAmelCase__    :     BinaryTreeNode | None						,     UpperCAmelCase__    :     int     )->		BinaryTreeNode | None:
						if node is None:
												UpperCAmelCase__   		=						BinaryTreeNode(UpperCAmelCase__     )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												UpperCAmelCase__   		=						insert(node.left_child						,     UpperCAmelCase__     )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												UpperCAmelCase__   		=						insert(node.right_child						,     UpperCAmelCase__     )
						return node


def 					a__ (						UpperCAmelCase__    :     None | BinaryTreeNode     )->		list[int]:  # if node is None,return
						if node:
												UpperCAmelCase__   		=						inorder(node.left_child     )
												UpperCAmelCase__   		=						[*inorder_array, node.data]
												UpperCAmelCase__   		=						inorder_array + inorder(node.right_child     )
						else:
												UpperCAmelCase__   		=						[]
						return inorder_array


def 					a__ (						)->		BinaryTreeNode | None:
						UpperCAmelCase__   		=						insert(UpperCAmelCase__						,     1_5     )
						insert(UpperCAmelCase__						,     1_0     )
						insert(UpperCAmelCase__						,     2_5     )
						insert(UpperCAmelCase__						,     6     )
						insert(UpperCAmelCase__						,     1_4     )
						insert(UpperCAmelCase__						,     2_0     )
						insert(UpperCAmelCase__						,     6_0     )
						return root


def 					a__ (						)->		None:
						# main function
						UpperCAmelCase__   		=						make_tree()
						print("""Printing values of binary search tree in Inorder Traversal."""     )
						inorder(UpperCAmelCase__     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

