

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 				lowercase       :
				__SCREAMING_SNAKE_CASE      :				int
				__SCREAMING_SNAKE_CASE      :				Node | None     =    None
				__SCREAMING_SNAKE_CASE      :				Node | None     =    None





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Node(1   )
							snake_case_           =					Node(2   )
							snake_case_           =					Node(3   )
							snake_case_           =					Node(4   )
							snake_case_           =					Node(5   )
							return tree





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return [root.data, *preorder(root.left   ), *preorder(root.right   )] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return postorder(root.left   ) + postorder(root.right   ) + [root.data] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return [*inorder(root.left   ), root.data, *inorder(root.right   )] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return (max(height(root.left   )     ,							height(root.right   )   ) + 1) if root else 0





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							if root is None:
														return output

							snake_case_           =					deque([root]   )

							while process_queue:
														snake_case_           =					process_queue.popleft()
														output.append(node.data   )

														if node.left:
																					process_queue.append(node.left   )
														if node.right:
																					process_queue.append(node.right   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							def populate_output(UpperCamelCase__     ,							UpperCamelCase__   ) -> None:
														if not root:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.left     ,							level - 1   )
																					populate_output(root.right     ,							level - 1   )

							populate_output(UpperCamelCase__     ,							UpperCamelCase__   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							def populate_output(UpperCamelCase__     ,							UpperCamelCase__   ) -> None:
														if root is None:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.right     ,							level - 1   )
																					populate_output(root.left     ,							level - 1   )

							populate_output(UpperCamelCase__     ,							UpperCamelCase__   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if root is None:
														return []

							snake_case_           =					[]

							snake_case_           =					0
							snake_case_           =					height(UpperCamelCase__   )

							for h in range(1     ,							height_tree + 1   ):
														if not flag:
																					output.append(get_nodes_from_left_to_right(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					snake_case_           =					1
														else:
																					output.append(get_nodes_from_right_to_left(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					snake_case_           =					0

							return output





def       __lowerCamelCase			(				):  # Main function for testing.







							'''simple docstring'''






							snake_case_           =					make_tree()
							print(F'''In-order Traversal: {inorder(UpperCamelCase__   )}'''   )
							print(F'''Pre-order Traversal: {preorder(UpperCamelCase__   )}'''   )
							print(F'''Post-order Traversal: {postorder(UpperCamelCase__   )}'''     ,							'\n'   )

							print(F'''Height of Tree: {height(UpperCamelCase__   )}'''     ,							'\n'   )

							print('Complete Level Order Traversal: '   )
							print(level_order(UpperCamelCase__   )     ,							'\n'   )

							print('Level-wise order Traversal: '   )

							for level in range(1     ,							height(UpperCamelCase__   ) + 1   ):
														print(F'''Level {level}:'''     ,							get_nodes_from_left_to_right(UpperCamelCase__     ,							level=UpperCamelCase__   )   )

							print('\nZigZag order Traversal: '   )
							print(zigzag(UpperCamelCase__   )   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors







@require_tokenizers
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizer
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizerFast
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    True
				__SCREAMING_SNAKE_CASE      :				Tuple     =    filter_roberta_detectors




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

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




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											return "lower newer", "lower newer"




				@cached_property
				def 					a							(      self ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )




				@cached_property
				def 					a							(      self ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_           =					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

																		self.assertEqual((2, 9)   ,     batch.input_ids.shape )
																		self.assertEqual((2, 9)   ,     batch.attention_mask.shape )
																		snake_case_           =					batch.input_ids.tolist()[0]
																		self.assertListEqual(snake_case   ,     snake_case )
																		# Test that special tokens are reset




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     padding=snake_case   ,     return_tensors='pt' )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids'   ,     snake_case )
																		self.assertIn('attention_mask'   ,     snake_case )
																		self.assertNotIn('labels'   ,     snake_case )
																		self.assertNotIn('decoder_attention_mask'   ,     snake_case )




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




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




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.']
											snake_case_           =					[
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     text_target=snake_case   ,     return_tensors='pt' )
																		snake_case_           =					inputs['input_ids']
																		snake_case_           =					inputs['labels']
																		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() )




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																									snake_case_           =					self.rust_tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					self.tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					'A, <mask> AllenNLP sentence.'
																									snake_case_           =					tokenizer_r.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=snake_case )
																									snake_case_           =					tokenizer_p.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=snake_case )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r['token_type_ids'] )   ,     sum(tokens_p['token_type_ids'] ) )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )   ,     sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )   ,     )

																									snake_case_           =					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																									snake_case_           =					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

																									# Rust correctly handles the space before the mask while python doesnt
																									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(
																									    snake_case   ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
																									self.assertSequenceEqual(
																									    snake_case   ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )




