



from __future__ import annotations

import queue
class 				_snake_case			:




				def __init__(						self					:						Optional[Any],					__lowercase					:						Union[str, Any]						):
							lowercase__					  =				data
							lowercase__					  =				None
							lowercase__					  =				None



def __lowerCAmelCase (     ):
			print("\n********Press N to stop entering at any point of time********\n"  )
			lowercase__					  =				input("Enter the value of the root node: "  ).strip().lower()
			lowercase__					  =				queue.Queue()
			lowercase__					  =				TreeNode(int(SCREAMING_SNAKE_CASE_  )  )
			q.put(SCREAMING_SNAKE_CASE_  )
			while not q.empty():
						lowercase__					  =				q.get()
						lowercase__					  =				f'''Enter the left node of {node_found.data}: '''
						lowercase__					  =				input(SCREAMING_SNAKE_CASE_  ).strip().lower() or "n"
						if check == "n":
									return tree_node
						lowercase__					  =				TreeNode(int(SCREAMING_SNAKE_CASE_  )  )
						lowercase__					  =				left_node
						q.put(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				f'''Enter the right node of {node_found.data}: '''
						lowercase__					  =				input(SCREAMING_SNAKE_CASE_  ).strip().lower() or "n"
						if check == "n":
									return tree_node
						lowercase__					  =				TreeNode(int(SCREAMING_SNAKE_CASE_  )  )
						lowercase__					  =				right_node
						q.put(SCREAMING_SNAKE_CASE_  )
			raise



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			print(node.data						,  end=","  )
			pre_order(node.left  )
			pre_order(node.right  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			in_order(node.left  )
			print(node.data						,  end=","  )
			in_order(node.right  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			post_order(node.left  )
			post_order(node.right  )
			print(node.data						,  end=","  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			lowercase__					  =				queue.Queue()
			q.put(SCREAMING_SNAKE_CASE_  )
			while not q.empty():
						lowercase__					  =				q.get()
						print(node_dequeued.data						,  end=","  )
						if node_dequeued.left:
									q.put(node_dequeued.left  )
						if node_dequeued.right:
									q.put(node_dequeued.right  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			lowercase__					  =				queue.Queue()
			q.put(SCREAMING_SNAKE_CASE_  )
			while not q.empty():
						lowercase__					  =				[]
						while not q.empty():
									lowercase__					  =				q.get()
									print(node_dequeued.data						,  end=","  )
									if node_dequeued.left:
												list_.append(node_dequeued.left  )
									if node_dequeued.right:
												list_.append(node_dequeued.right  )
						print()
						for node in list_:
									q.put(SCREAMING_SNAKE_CASE_  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			lowercase__					  =				[]
			lowercase__					  =				node
			while n or stack:
						while n:  # start from root node, find its left child
									print(n.data						,  end=","  )
									stack.append(SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				n.left
						# end of while means current node doesn't have left child
						lowercase__					  =				stack.pop()
						# start to traverse its right child
						lowercase__					  =				n.right



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			lowercase__					  =				[]
			lowercase__					  =				node
			while n or stack:
						while n:
									stack.append(SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				n.left
						lowercase__					  =				stack.pop()
						print(n.data						,  end=","  )
						lowercase__					  =				n.right



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if not isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) or not node:
						return
			lowercase__  ,       lowercase__					  =				[], []
			lowercase__					  =				node
			stacka.append(SCREAMING_SNAKE_CASE_  )
			while stacka:  # to find the reversed order of post order, store it in stack2
						lowercase__					  =				stacka.pop()
						if n.left:
									stacka.append(n.left  )
						if n.right:
									stacka.append(n.right  )
						stacka.append(SCREAMING_SNAKE_CASE_  )
			while stacka:  # pop up from stack2 will be the post order
						print(stacka.pop().data						,  end=","  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_ = ""						,  SCREAMING_SNAKE_CASE_=50						,  SCREAMING_SNAKE_CASE_="*"  ):
			if not s:
						return "\n" + width * char
			lowercase__  ,       lowercase__					  =				divmod(width - len(SCREAMING_SNAKE_CASE_  ) - 2						,  2  )
			return f'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(prompt("""Binary Tree Traversals"""))

							lowercase_								=     build_tree()
							print(prompt("""Pre Order Traversal"""))
							pre_order(node)
							print(prompt() + """\n""")

							print(prompt("""In Order Traversal"""))
							in_order(node)
							print(prompt() + """\n""")

							print(prompt("""Post Order Traversal"""))
							post_order(node)
							print(prompt() + """\n""")

							print(prompt("""Level Order Traversal"""))
							level_order(node)
							print(prompt() + """\n""")

							print(prompt("""Actual Level Order Traversal"""))
							level_order_actual(node)
							print("""*""" * 50 + """\n""")

							print(prompt("""Pre Order Traversal - Iteration Version"""))
							pre_order_iter(node)
							print(prompt() + """\n""")

							print(prompt("""In Order Traversal - Iteration Version"""))
							in_order_iter(node)
							print(prompt() + """\n""")

							print(prompt("""Post Order Traversal - Iteration Version"""))
							post_order_iter(node)
							print(prompt())









from typing import TYPE_CHECKING

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


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

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




