



"""simple docstring"""





import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
					import bsa
					from bsa import BeautifulSoup


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       **snake_case						)			->							List[str]:
									requires_backends(self	,       ['bs4']						)
									super().__init__(**snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
											_UpperCAmelCase							 =		parent.find_all(child.name	,       recursive=snake_case						)
											xpath_tags.append(child.name						)
											xpath_subscripts.append(
											    0 if 1 == len(snake_case						) else next(i for i, s in enumerate(snake_case	,       1						) if s is child						)						)
											_UpperCAmelCase							 =		parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts






							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		BeautifulSoup(snake_case	,       'html.parser'						)

									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[]

									for element in html_code.descendants:
											if type(snake_case						) == bsa.element.NavigableString:
													if type(element.parent						) != bsa.element.Tag:
															continue

													_UpperCAmelCase							 =		html.unescape(snake_case						).strip()
													if not text_in_this_tag:
															continue

													all_doc_strings.append(snake_case						)

													_UpperCAmelCase ,					_UpperCAmelCase							 =		self.xpath_soup(snake_case						)
													stringaxtag_seq.append(snake_case						)
													stringaxsubs_seq.append(snake_case						)

									if len(snake_case						) != len(snake_case						):
											raise ValueError('Number of doc strings and xtags does not correspond'						)
									if len(snake_case						) != len(snake_case						):
											raise ValueError('Number of doc strings and xsubs does not correspond'						)

									return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							List[str]:
									_UpperCAmelCase							 =		''
									for tagname, subs in zip(snake_case	,       snake_case						):
											xpath += f'/{tagname}'
											if subs != 0:
													xpath += f'[{subs}]'
									return xpath






							def __call__(  self	,       snake_case						)			->							BatchFeature:
									_UpperCAmelCase							 =		False

									# Check that strings has a valid type
									if isinstance(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		True
									elif isinstance(snake_case	,       (list, tuple)						):
											if len(snake_case						) == 0 or isinstance(html_strings[0]	,       snake_case						):
													_UpperCAmelCase							 =		True

									if not valid_strings:
											raise ValueError(
											    'HTML strings must of type `str`, `List[str]` (batch of examples), '
											    f'but is of type {type(snake_case						)}.'						)

									_UpperCAmelCase							 =		bool(isinstance(snake_case	,       (list, tuple)						) and (isinstance(html_strings[0]	,       snake_case						))						)

									if not is_batched:
											_UpperCAmelCase							 =		[html_strings]

									# Get nodes + xpaths
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[]
									for html_string in html_strings:
											_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		self.get_three_from_single(snake_case						)
											nodes.append(snake_case						)
											_UpperCAmelCase							 =		[]
											for node, tag_list, sub_list in zip(snake_case	,       snake_case	,       snake_case						):
													_UpperCAmelCase							 =		self.construct_xpath(snake_case	,       snake_case						)
													xpath_strings.append(snake_case						)
											xpaths.append(snake_case						)

									# return as Dict
									_UpperCAmelCase							 =		{'nodes': nodes, 'xpaths': xpaths}
									_UpperCAmelCase							 =		BatchFeature(data=snake_case	,       tensor_type=snake_case						)

									return encoded_inputs







"""simple docstring"""





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					BarthezTokenizer
							_UpperCAmelCase      				=					BarthezTokenizerFast
							_UpperCAmelCase      				=					True
							_UpperCAmelCase      				=					True






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									super().setUp()

									_UpperCAmelCase							 =		BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'						)
									tokenizer.save_pretrained(self.tmpdirname						)
									tokenizer.save_pretrained(self.tmpdirname	,       legacy_format=snake_case						)
									_UpperCAmelCase							 =		tokenizer






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		'<pad>'
									_UpperCAmelCase							 =		1

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






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]	,       '<s>'						)
									self.assertEqual(vocab_keys[1]	,       '<pad>'						)
									self.assertEqual(vocab_keys[-1]	,       '<mask>'						)
									self.assertEqual(len(snake_case						)	,       101122						)






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									self.assertEqual(self.get_tokenizer().vocab_size	,       101122						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_UpperCAmelCase							 =		[0, 57, 3018, 70307, 91, 2]

									_UpperCAmelCase							 =		self.tokenizer(
									    snake_case	,       max_length=len(snake_case						)	,       padding=snake_case	,       truncation=snake_case	,       return_tensors='pt'						)
									self.assertIsInstance(snake_case	,       snake_case						)

									self.assertEqual((2, 6)	,       batch.input_ids.shape						)
									self.assertEqual((2, 6)	,       batch.attention_mask.shape						)
									_UpperCAmelCase							 =		batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									if not self.test_rust_tokenizer:
											return

									_UpperCAmelCase							 =		self.get_tokenizer()
									_UpperCAmelCase							 =		self.get_rust_tokenizer()

									_UpperCAmelCase							 =		'I was born in 92000, and this is falsé.'

									_UpperCAmelCase							 =		tokenizer.tokenize(snake_case						)
									_UpperCAmelCase							 =		rust_tokenizer.tokenize(snake_case						)
									self.assertListEqual(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		tokenizer.encode(snake_case	,       add_special_tokens=snake_case						)
									_UpperCAmelCase							 =		rust_tokenizer.encode(snake_case	,       add_special_tokens=snake_case						)
									self.assertListEqual(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		self.get_rust_tokenizer()
									_UpperCAmelCase							 =		tokenizer.encode(snake_case						)
									_UpperCAmelCase							 =		rust_tokenizer.encode(snake_case						)
									self.assertListEqual(snake_case	,       snake_case						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									# fmt: off
									_UpperCAmelCase							 =		{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_UpperCAmelCase							 =		[
									    '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=snake_case	,       model_name='moussaKam/mbarthez'	,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,       sequences=snake_case	,       )


