


'''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 _lowerCamelCase			(		lowercase_						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     List[str]        =							BarthezTokenizer
		A_      :     str        =							BarthezTokenizerFast
		A_      :     Dict        =							True
		A_      :     Dict        =							True







		def   __lowerCAmelCase						(   self						:		Any     )    ->					Optional[Any]:
			super().setUp()

			__magic_name__			:       List[str]	      =	BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'     )
			tokenizer.save_pretrained(self.tmpdirname     )
			tokenizer.save_pretrained(self.tmpdirname    ,	legacy_format=_A     )
			__magic_name__			:       Union[str, Any]	      =	tokenizer







		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					int:
			__magic_name__			:       str	      =	'<pad>'
			__magic_name__			:       Tuple	      =	1

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







		def   __lowerCAmelCase						(   self						:		Optional[int]     )    ->					Tuple:
			__magic_name__			:       Union[str, Any]	      =	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(_A     )    ,	101122     )







		def   __lowerCAmelCase						(   self						:		str     )    ->					int:
			self.assertEqual(self.get_tokenizer().vocab_size    ,	101122     )







		@require_torch
		def   __lowerCAmelCase						(   self						:		Dict     )    ->					Tuple:
			__magic_name__			:       Union[str, Any]	      =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
			__magic_name__			:       str	      =	[0, 57, 3018, 70307, 91, 2]

			__magic_name__			:       List[str]	      =	self.tokenizer(
			    _A    ,	max_length=len(_A     )    ,	padding=_A    ,	truncation=_A    ,	return_tensors='pt'     )
			self.assertIsInstance(_A    ,	_A     )

			self.assertEqual((2, 6)    ,	batch.input_ids.shape     )
			self.assertEqual((2, 6)    ,	batch.attention_mask.shape     )
			__magic_name__			:       Union[str, Any]	      =	batch.input_ids.tolist()[0]
			self.assertListEqual(_A    ,	_A     )







		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					Dict:
			if not self.test_rust_tokenizer:
				return

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

			__magic_name__			:       int	      =	'I was born in 92000, and this is falsé.'

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

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

			__magic_name__			:       List[str]	      =	self.get_rust_tokenizer()
			__magic_name__			:       Tuple	      =	tokenizer.encode(_A     )
			__magic_name__			:       str	      =	rust_tokenizer.encode(_A     )
			self.assertListEqual(_A    ,	_A     )







		@slow
		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					int:
			# fmt: off
			__magic_name__			:       List[str]	      =	{'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.
			__magic_name__			:       int	      =	[
			    '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='moussaKam/mbarthez'    ,	revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'    ,	sequences=_A    ,	)


_UpperCAmelCase						:  Any									=			6_5521





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       1
							snake_case_     							=       0
							for plain_chr in plain_text:
														snake_case_     							=       (a + ord(UpperCamelCase__ )) % MOD_ADLER
														snake_case_     							=       (b + a) % MOD_ADLER
							return (b << 16) | a




