



from __future__ import annotations



class 			lowerCamelCase__     :




		'''simple docstring'''



		def __init__(self						,__lowerCamelCase					)	->     Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__	:							List[str]				=	data
						lowerCAmelCase__	:							Dict				=	None
						lowerCAmelCase__	:							List[str]				=	None






def       lowerCAmelCase__						(       lowerCamelCase_						:						List[Any]):  # In Order traversal of the tree





				'''simple docstring'''

				if tree:
								display(tree.left)
								print(tree.data)
								display(tree.right)






def       lowerCAmelCase__						(       lowerCamelCase_						:						Tuple):





				'''simple docstring'''

				return 1 + max(depth_of_tree(tree.left)			,depth_of_tree(tree.right)) if tree else 0






def       lowerCAmelCase__						(       lowerCamelCase_						:						Dict):





				'''simple docstring'''

				if not tree:
								return True
				if tree.left and tree.right:
								return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
				else:
								return not tree.left and not tree.right






def       lowerCAmelCase__						(       ):  # Main function for testing.





				'''simple docstring'''

				lowerCAmelCase__	:							Optional[Any]				=	Node(1)
				lowerCAmelCase__	:							List[str]				=	Node(2)
				lowerCAmelCase__	:							Dict				=	Node(3)
				lowerCAmelCase__	:							Tuple				=	Node(4)
				lowerCAmelCase__	:							Tuple				=	Node(5)
				lowerCAmelCase__	:							Optional[Any]				=	Node(6)
				lowerCAmelCase__	:							List[str]				=	Node(7)
				lowerCAmelCase__	:							int				=	Node(8)
				lowerCAmelCase__	:							Optional[int]				=	Node(9)

				print(is_full_binary_tree(lowerCAmelCase__))
				print(depth_of_tree(lowerCAmelCase__))
				print('''Tree is: ''')
				display(lowerCAmelCase__)


if __name__ == "__main__":
							main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

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



