






from __future__ import annotations

import queue

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					int ,  __lowerCamelCase				:					Union[str, Any]	):
		UpperCamelCase     :Optional[Any]          =	data
		UpperCamelCase     :Dict          =	None
		UpperCamelCase     :List[Any]          =	None

def    SCREAMING_SNAKE_CASE_							(							)		->			TreeNode:






	"""simple docstring"""



	print("""\n********Press N to stop entering at any point of time********\n"""					)
	UpperCamelCase     :List[str]          =	input("""Enter the value of the root node: """					).strip().lower()
	UpperCamelCase     :queue.Queue          =	queue.Queue()
	UpperCamelCase     :int          =	TreeNode(int(__magic_name__					)					)
	q.put(__magic_name__					)
	while not q.empty():
		UpperCamelCase     :Any          =	q.get()
		UpperCamelCase     :Union[str, Any]          =	f"""Enter the left node of {node_found.data}: """
		UpperCamelCase     :Tuple          =	input(__magic_name__					).strip().lower() or """n"""
		if check == "n":
			return tree_node
		UpperCamelCase     :int          =	TreeNode(int(__magic_name__					)					)
		UpperCamelCase     :List[Any]          =	left_node
		q.put(__magic_name__					)
		UpperCamelCase     :Optional[Any]          =	f"""Enter the right node of {node_found.data}: """
		UpperCamelCase     :Any          =	input(__magic_name__					).strip().lower() or """n"""
		if check == "n":
			return tree_node
		UpperCamelCase     :Union[str, Any]          =	TreeNode(int(__magic_name__					)					)
		UpperCamelCase     :Any          =	right_node
		q.put(__magic_name__					)
	raise

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



	if not isinstance(__magic_name__	,			__magic_name__					) or not node:
		return
	UpperCamelCase     :queue.Queue          =	queue.Queue()
	q.put(__magic_name__					)
	while not q.empty():
		UpperCamelCase     :Optional[Any]          =	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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



	if not isinstance(__magic_name__	,			__magic_name__					) or not node:
		return
	UpperCamelCase     :queue.Queue          =	queue.Queue()
	q.put(__magic_name__					)
	while not q.empty():
		UpperCamelCase     :str          =	[]
		while not q.empty():
			UpperCamelCase     :List[Any]          =	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(__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



	if not isinstance(__magic_name__	,			__magic_name__					) or not node:
		return
	UpperCamelCase     :list[TreeNode]          =	[]
	UpperCamelCase     :List[Any]          =	node
	while n or stack:
		while n:  # start from root node, find its left child
			print(n.data	,			end=""","""					)
			stack.append(__magic_name__					)
			UpperCamelCase     :Optional[int]          =	n.left
		# end of while means current node doesn't have left child
		UpperCamelCase     :Dict          =	stack.pop()
		# start to traverse its right child
		UpperCamelCase     :Union[str, Any]          =	n.right

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



	if not isinstance(__magic_name__	,			__magic_name__					) or not node:
		return
	UpperCamelCase     :list[TreeNode]          =	[]
	UpperCamelCase     :List[Any]          =	node
	while n or stack:
		while n:
			stack.append(__magic_name__					)
			UpperCamelCase     :str          =	n.left
		UpperCamelCase     :List[str]          =	stack.pop()
		print(n.data	,			end=""","""					)
		UpperCamelCase     :str          =	n.right

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  TreeNode					)		->			None:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str = ""	,			__magic_name__					:  Any=50	,			__magic_name__					:  Optional[Any]="*"					)		->			str:






	"""simple docstring"""



	if not s:
		return "\n" + width * char
	UpperCamelCase     ,      UpperCamelCase     :Union[str, Any]          =	divmod(width - len(__magic_name__					) - 2	,			2					)
	return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
	import doctest

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

	UpperCAmelCase_ :						TreeNode      =      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())








import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_tensorflow_text_available():
	from transformers.models.bert import TFBertTokenizer


UpperCAmelCase_ :						Optional[Any]      =      ['''bert-base-uncased''', '''bert-base-cased''']
UpperCAmelCase_ :						List[str]      =      '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():

	class     _SCREAMING_SNAKE_CASE       (			tf.keras.Model      ):





		def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any]	):
			super().__init__()
			UpperCamelCase     :Any          =	tokenizer
			UpperCamelCase     :List[str]          =	AutoConfig.from_pretrained(__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	TFAutoModel.from_config(__lowerCamelCase	)





		def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str	):
			UpperCamelCase     :str          =	self.tokenizer(__lowerCamelCase	)
			UpperCamelCase     :Any          =	self.bert(**__lowerCamelCase	)
			return out["pooler_output"]

@require_tf
@require_tensorflow_text
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def 						_A (		self				:					Dict	):
		super().setUp()

		UpperCamelCase     :int          =	[
		    BertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
		]  # repeat for when fast_bert_tokenizer=false
		UpperCamelCase     :Any          =	[TFBertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in TOKENIZER_CHECKPOINTS] + [
		    TFBertTokenizer.from_pretrained(__lowerCamelCase ,  use_fast_bert_tokenizer=__lowerCamelCase	)
		    for checkpoint in TOKENIZER_CHECKPOINTS
		]
		assert len(self.tokenizers	) == len(self.tf_tokenizers	)

		UpperCamelCase     :Any          =	[
		    """This is a straightforward English test sentence.""",
		    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
		    """Now we're going to add some Chinese: 一 二 三 一二三""",
		    """And some much more rare Chinese: 齉 堃 齉堃""",
		    """Je vais aussi écrire en français pour tester les accents""",
		    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
		]
		UpperCamelCase     :Union[str, Any]          =	list(zip(self.test_sentences ,  self.test_sentences[::-1]	)	)





	def 						_A (		self				:					Optional[int]	):
		for tokenizer, tf_tokenizer in zip(self.tokenizers ,  self.tf_tokenizers	):
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tokenizer(__lowerCamelCase ,  return_tensors="""tf""" ,  padding="""longest"""	)
				UpperCamelCase     :str          =	tf_tokenizer(__lowerCamelCase	)

				for key in python_outputs.keys():
					self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape	)	)
					self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,  tf.intaa	) == tf_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Dict	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :str          =	tf_tokenizer(self.paired_sentences	)
			UpperCamelCase     :Any          =	tf_tokenizer(
			    text=[sentence[0] for sentence in self.paired_sentences] ,  text_pair=[sentence[1] for sentence in self.paired_sentences] ,  )
			for key in merged_outputs.keys():
				self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,  tf.intaa	) == separated_outputs[key]	)	)





	@slow
	def 						_A (		self				:					List[str]	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[Any]          =	tf.function(__lowerCamelCase	)
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tf.constant(__lowerCamelCase	)
				UpperCamelCase     :List[str]          =	compiled_tokenizer(__lowerCamelCase	)
				UpperCamelCase     :Optional[Any]          =	tf_tokenizer(__lowerCamelCase	)

				for key in eager_outputs.keys():
					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[str]          =	ModelToSave(tokenizer=__lowerCamelCase	)
			UpperCamelCase     :Union[str, Any]          =	tf.convert_to_tensor(self.test_sentences	)
			UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)  # Build model with some sample inputs
			with TemporaryDirectory() as tempdir:
				UpperCamelCase     :List[str]          =	Path(__lowerCamelCase	) / """saved.model"""
				model.save(__lowerCamelCase	)
				UpperCamelCase     :List[Any]          =	tf.keras.models.load_model(__lowerCamelCase	)
			UpperCamelCase     :Dict          =	loaded_model(__lowerCamelCase	)
			# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
			self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output	)	) ,  1E-5	)
