





from __future__ import annotations

import queue
class SCREAMING_SNAKE_CASE_						:






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







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	data
							snake_case_		:			Union[str, Any]											=	None
							snake_case_		:			Tuple											=	None


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


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ) or not node:
		return
	print(node.data   , end=','   )
	pre_order(node.left   )
	pre_order(node.right   )


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ) or not node:
		return
	in_order(node.left   )
	print(node.data   , end=','   )
	in_order(node.right   )


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ) or not node:
		return
	post_order(node.left   )
	post_order(node.right   )
	print(node.data   , end=','   )


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ) or not node:
		return
	snake_case_		:			queue.Queue											=	queue.Queue()
	q.put(__a   )
	while not q.empty():
		snake_case_		:			int											=	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__						(    __a   ):
	if not isinstance(__a   , __a   ) or not node:
		return
	snake_case_		:			queue.Queue											=	queue.Queue()
	q.put(__a   )
	while not q.empty():
		snake_case_		:			str											=	[]
		while not q.empty():
			snake_case_		:			Dict											=	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(__a   )


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


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ) or not node:
		return
	snake_case_		:			list[TreeNode]											=	[]
	snake_case_		:			str											=	node
	while n or stack:
		while n:
			stack.append(__a   )
			snake_case_		:			List[str]											=	n.left
		snake_case_		:			Dict											=	stack.pop()
		print(n.data   , end=','   )
		snake_case_		:			List[str]											=	n.right


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


def 							SCREAMING_SNAKE_CASE__						(    __a = ""   , __a=50   , __a="*"   ):
	if not s:
		return "\n" + width * char
	snake_case_						,snake_case_		:			Tuple											=	divmod(width - len(__a   ) - 2   , 2   )
	return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
						import doctest

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

						_SCREAMING_SNAKE_CASE      	=		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 transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)		,   _A	)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)		,   _A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

