






from typing import TYPE_CHECKING

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


a__       =     {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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








from __future__ import annotations


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       data
											snake_case__	  =       None
											snake_case__	  =       None


def _UpperCAmelCase   (	a						:	Node | None ):  # In Order traversal of the tree
							if tree:
														display(tree.left )
														print(tree.data )
														display(tree.right )

def _UpperCAmelCase   (	a						:	Node | None ):
							return 1 + max(depth_of_tree(tree.left )  ,					depth_of_tree(tree.right ) ) if tree else 0

def _UpperCAmelCase   (	a						:	Node ):
							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 _UpperCAmelCase   (	):  # Main function for testing.
							snake_case__	  =       Node(1 )
							snake_case__	  =       Node(2 )
							snake_case__	  =       Node(3 )
							snake_case__	  =       Node(4 )
							snake_case__	  =       Node(5 )
							snake_case__	  =       Node(6 )
							snake_case__	  =       Node(7 )
							snake_case__	  =       Node(8 )
							snake_case__	  =       Node(9 )

							print(is_full_binary_tree(a ) )
							print(depth_of_tree(a ) )
							print("""Tree is: """ )
							display(a )


if __name__ == "__main__":
	main()
