


from __future__ import annotations

import queue

class 						A__							:




						def __init__(       self					:      Optional[Any]				,    _a					:      Union[str, Any]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =data
										_SCREAMING_SNAKE_CASE			  =None
										_SCREAMING_SNAKE_CASE			  =None







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







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







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







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







def 						lowerCamelCase(   a__):
				if not isinstance(a__	,a__) or not node:
								return
				_SCREAMING_SNAKE_CASE			  =queue.Queue()
				q.put(a__)
				while not q.empty():
								_SCREAMING_SNAKE_CASE			  =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(   a__):
				if not isinstance(a__	,a__) or not node:
								return
				_SCREAMING_SNAKE_CASE			  =queue.Queue()
				q.put(a__)
				while not q.empty():
								_SCREAMING_SNAKE_CASE			  =[]
								while not q.empty():
												_SCREAMING_SNAKE_CASE			  =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 						lowerCamelCase(   a__):
				if not isinstance(a__	,a__) or not node:
								return
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =node
				while n or stack:
								while n:  # start from root node, find its left child
												print(n.data	,end=''',''')
												stack.append(a__)
												_SCREAMING_SNAKE_CASE			  =n.left
								# end of while means current node doesn't have left child
								_SCREAMING_SNAKE_CASE			  =stack.pop()
								# start to traverse its right child
								_SCREAMING_SNAKE_CASE			  =n.right







def 						lowerCamelCase(   a__):
				if not isinstance(a__	,a__) or not node:
								return
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =node
				while n or stack:
								while n:
												stack.append(a__)
												_SCREAMING_SNAKE_CASE			  =n.left
								_SCREAMING_SNAKE_CASE			  =stack.pop()
								print(n.data	,end=''',''')
								_SCREAMING_SNAKE_CASE			  =n.right







def 						lowerCamelCase(   a__):
				if not isinstance(a__	,a__) or not node:
								return
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =[], []
				_SCREAMING_SNAKE_CASE			  =node
				stacka.append(a__)
				while stacka:  # to find the reversed order of post order, store it in stack2
								_SCREAMING_SNAKE_CASE			  =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 						lowerCamelCase(   a__ = ""	,a__=50	,a__="*"):
				if not s:
								return "\n" + width * char
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =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'''))

							snake_case_						:			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 json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LEDTokenizer
						UpperCAmelCase             =						LEDTokenizerFast
						UpperCAmelCase             =						True




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


										"""simple docstring"""





										super().setUp()
										_SCREAMING_SNAKE_CASE			  =[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										_SCREAMING_SNAKE_CASE			  =dict(zip(_a				,    range(len(_a  )  )  )  )
										_SCREAMING_SNAKE_CASE			  =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										_SCREAMING_SNAKE_CASE			  ={'''unk_token''': '''<unk>'''}

										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''merges_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write(json.dumps(_a  ) + '''\n'''  )
										with open(self.merges_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write('''\n'''.join(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    **_a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    **_a					:      List[Any]  )							->     str:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Any  )							->     Dict:


										"""simple docstring"""





										return "lower newer", "lower newer"




						@cached_property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''  )




						@cached_property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Any:


										"""simple docstring"""





										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    max_length=len(_a  )				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )

														self.assertEqual((2, 9)				,    batch.input_ids.shape  )
														self.assertEqual((2, 9)				,    batch.attention_mask.shape  )
														_SCREAMING_SNAKE_CASE			  =batch.input_ids.tolist()[0]
														self.assertListEqual(_a				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIn('''input_ids'''				,    _a  )
														self.assertIn('''attention_mask'''				,    _a  )
														self.assertNotIn('''labels'''				,    _a  )
														self.assertNotIn('''decoder_attention_mask'''				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    max_length=32				,    padding='''max_length'''				,    return_tensors='''pt'''  )
														self.assertEqual(32				,    targets['''input_ids'''].shape[1]  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Any  )							->     Union[str, Any]:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(
														    ['''I am a small frog''' * 1024, '''I am a small frog''']				,    padding=_a				,    truncation=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )
														self.assertEqual(batch.input_ids.shape				,    (2, 5122)  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =inputs['''input_ids''']
														_SCREAMING_SNAKE_CASE			  =targets['''input_ids''']
														self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
														self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =['''Summary of the text.''', '''Another summary.''']
														_SCREAMING_SNAKE_CASE			  =[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a  )
														_SCREAMING_SNAKE_CASE			  =[[0] * len(_a  ) for x in encoded_output['''input_ids''']]
														_SCREAMING_SNAKE_CASE			  =tokenizer.pad(_a  )
														self.assertSequenceEqual(outputs['''global_attention_mask''']				,    _a  )




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


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
																		_SCREAMING_SNAKE_CASE			  =self.rust_tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  ='''A, <mask> AllenNLP sentence.'''
																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		self.assertEqual(sum(tokens_r['''token_type_ids''']  )				,    sum(tokens_p['''token_type_ids''']  )  )
																		self.assertEqual(
																		    sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )				,    sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )				,    )

																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )
																		self.assertSequenceEqual(tokens_p['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
																		self.assertSequenceEqual(tokens_r['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )