"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase__   			=      {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BartForCausalLM',
     'BartForConditionalGeneration',
     'BartForQuestionAnswering',
     'BartForSequenceClassification',
     'BartModel',
     'BartPretrainedModel',
     'PretrainedBartModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TFBartForConditionalGeneration',
     'TFBartForSequenceClassification',
     'TFBartModel',
     'TFBartPretrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'FlaxBartDecoderPreTrainedModel',
     'FlaxBartForCausalLM',
     'FlaxBartForConditionalGeneration',
     'FlaxBartForQuestionAnswering',
     'FlaxBartForSequenceClassification',
     'FlaxBartModel',
     'FlaxBartPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
 from .tokenization_bart import BartTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bart_fast import BartTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bart import (
      BART_PRETRAINED_MODEL_ARCHIVE_LIST,
      BartForCausalLM,
      BartForConditionalGeneration,
      BartForQuestionAnswering,
      BartForSequenceClassification,
      BartModel,
      BartPretrainedModel,
      PretrainedBartModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_bart import (
      TFBartForConditionalGeneration,
      TFBartForSequenceClassification,
      TFBartModel,
      TFBartPretrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_bart import (
      FlaxBartDecoderPreTrainedModel,
      FlaxBartForCausalLM,
      FlaxBartForConditionalGeneration,
      FlaxBartForQuestionAnswering,
      FlaxBartForSequenceClassification,
      FlaxBartModel,
      FlaxBartPreTrainedModel,
  )

else:
 import sys

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







class 							lowerCAmelCase_		:







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   int					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	data
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				if node is None:
								_UpperCAmelCase						:  Union[str, Any]     					=	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						:  Any     					=	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						:  List[Any]     					=	insert(node.right_child      ,					_UpperCamelCase			)
				return node


def 	_A	(   _UpperCamelCase			):  # if node is None,return
				if node:
								_UpperCAmelCase						:  str     					=	inorder(node.left_child			)
								_UpperCAmelCase						:  Union[str, Any]     					=	[*inorder_array, node.data]
								_UpperCAmelCase						:  Optional[int]     					=	inorder_array + inorder(node.right_child			)
				else:
								_UpperCAmelCase						:  Tuple     					=	[]
				return inorder_array


def 	_A	(   ):
				_UpperCAmelCase						:  Dict     					=	insert(_UpperCamelCase      ,					15			)
				insert(_UpperCamelCase      ,					10			)
				insert(_UpperCamelCase      ,					25			)
				insert(_UpperCamelCase      ,					6			)
				insert(_UpperCamelCase      ,					14			)
				insert(_UpperCamelCase      ,					20			)
				insert(_UpperCamelCase      ,					60			)
				return root


def 	_A	(   ):
				# main function
				_UpperCAmelCase						:  Tuple     					=	make_tree()
				print('''Printing values of binary search tree in Inorder Traversal.'''			)
				inorder(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
