

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							 =			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			[
          '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MPNetForMaskedLM''',
          '''MPNetForMultipleChoice''',
          '''MPNetForQuestionAnswering''',
          '''MPNetForSequenceClassification''',
          '''MPNetForTokenClassification''',
          '''MPNetLayer''',
          '''MPNetModel''',
          '''MPNetPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			[
          '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFMPNetEmbeddings''',
          '''TFMPNetForMaskedLM''',
          '''TFMPNetForMultipleChoice''',
          '''TFMPNetForQuestionAnswering''',
          '''TFMPNetForSequenceClassification''',
          '''TFMPNetForTokenClassification''',
          '''TFMPNetMainLayer''',
          '''TFMPNetModel''',
          '''TFMPNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      _SCREAMING_SNAKE_CASE							 =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     data
   __UpperCAmelCase	:  BinaryTreeNode | None		=     None
   __UpperCAmelCase	:  BinaryTreeNode | None		=     None





def  _snake_case		(snake_case_					:     BinaryTreeNode | None    ,       snake_case_					:     int  )							->		BinaryTreeNode | None:
  if node is None:
    __UpperCAmelCase	:  List[Any]		=     BinaryTreeNode(snake_case_  )
    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	:  Union[str, Any]		=     insert(node.left_child    ,       snake_case_  )
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase	:  Tuple		=     insert(node.right_child    ,       snake_case_  )
  return node





def  _snake_case		(snake_case_					:     None | BinaryTreeNode  )							->		list[int]:  # if node is None,return
  if node:
    __UpperCAmelCase	:  str		=     inorder(node.left_child  )
    __UpperCAmelCase	:  Optional[int]		=     [*inorder_array, node.data]
    __UpperCAmelCase	:  str		=     inorder_array + inorder(node.right_child  )
  else:
    __UpperCAmelCase	:  Union[str, Any]		=     []
  return inorder_array





def  _snake_case		()							->		BinaryTreeNode | None:
  __UpperCAmelCase	:  Tuple		=     insert(snake_case_    ,       15  )
  insert(snake_case_    ,       10  )
  insert(snake_case_    ,       25  )
  insert(snake_case_    ,       6  )
  insert(snake_case_    ,       14  )
  insert(snake_case_    ,       20  )
  insert(snake_case_    ,       60  )
  return root





def  _snake_case		()							->		None:
  # main function
  __UpperCAmelCase	:  Any		=     make_tree()
  print('Printing values of binary search tree in Inorder Traversal.'  )
  inorder(snake_case_  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





