

class 							A_				:


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	int      )							->			None:
      __lowerCAmelCase:     Union[str, Any]     			=   data
      __lowerCAmelCase:     BinaryTreeNode | None     			=   None
      __lowerCAmelCase:     BinaryTreeNode | None     			=   None







def _a				( SCREAMING_SNAKE_CASE	:    BinaryTreeNode | None					,      SCREAMING_SNAKE_CASE	:    int	)					->					BinaryTreeNode | None:

 """simple docstring"""






 if node is None:
  __lowerCAmelCase:     List[Any]     			=   BinaryTreeNode(SCREAMING_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:
  __lowerCAmelCase:     Any     			=   insert(node.left_child					,      SCREAMING_SNAKE_CASE	)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  __lowerCAmelCase:     Optional[Any]     			=   insert(node.right_child					,      SCREAMING_SNAKE_CASE	)
 return node







def _a				( SCREAMING_SNAKE_CASE	:    None | BinaryTreeNode	)					->					list[int]:  # if node is None,return

 """simple docstring"""






 if node:
  __lowerCAmelCase:     Optional[Any]     			=   inorder(node.left_child	)
  __lowerCAmelCase:     List[Any]     			=   [*inorder_array, node.data]
  __lowerCAmelCase:     List[Any]     			=   inorder_array + inorder(node.right_child	)
 else:
  __lowerCAmelCase:     Union[str, Any]     			=   []
 return inorder_array







def _a				( )					->					BinaryTreeNode | None:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   insert(SCREAMING_SNAKE_CASE					,      15	)
 insert(SCREAMING_SNAKE_CASE					,      10	)
 insert(SCREAMING_SNAKE_CASE					,      25	)
 insert(SCREAMING_SNAKE_CASE					,      6	)
 insert(SCREAMING_SNAKE_CASE					,      14	)
 insert(SCREAMING_SNAKE_CASE					,      20	)
 insert(SCREAMING_SNAKE_CASE					,      60	)
 return root







def _a				( )					->					None:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'	)
 inorder(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()








from typing import TYPE_CHECKING

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


_a	 =     {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''GPT2TokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GPT2DoubleHeadsModel''',
      '''GPT2ForQuestionAnswering''',
      '''GPT2ForSequenceClassification''',
      '''GPT2ForTokenClassification''',
      '''GPT2LMHeadModel''',
      '''GPT2Model''',
      '''GPT2PreTrainedModel''',
      '''load_tf_weights_in_gpt2''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFGPT2DoubleHeadsModel''',
      '''TFGPT2ForSequenceClassification''',
      '''TFGPT2LMHeadModel''',
      '''TFGPT2MainLayer''',
      '''TFGPT2Model''',
      '''TFGPT2PreTrainedModel''',
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TFGPT2Tokenizer''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

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





