
class   __UpperCamelCase				:






     """simple docstring"""







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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						data
       __SCREAMING_SNAKE_CASE     :					BinaryTreeNode | None			=						None
       __SCREAMING_SNAKE_CASE     :					BinaryTreeNode | None			=						None






def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  if node is None:
    __SCREAMING_SNAKE_CASE     :					Dict			=						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:
    __SCREAMING_SNAKE_CASE     :					List[str]			=						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
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						insert(node.right_child   ,	snake_case    )
  return node


def     a__			(    snake_case    ):  # if node is None,return





  """simple docstring"""
  if node:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						inorder(node.left_child    )
    __SCREAMING_SNAKE_CASE     :					Dict			=						[*inorder_array, node.data]
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						inorder_array + inorder(node.right_child    )
  else:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						[]
  return inorder_array


def     a__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						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     a__			(    ):





  """simple docstring"""
  # main function
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						make_tree()
  print('''Printing values of binary search tree in Inorder Traversal.'''    )
  inorder(snake_case    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


from typing import TYPE_CHECKING

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


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
