

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							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     BarthezTokenizer
				__SCREAMING_SNAKE_CASE   :					str			     =     BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE   :					Dict			     =     True
				__SCREAMING_SNAKE_CASE   :					Dict			     =     True




				def        a				(   self			):
											super().setUp()

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




				def        a				(   self			):
											snake_case_     							=       '<pad>'
											snake_case_     							=       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        a				(   self			):
											snake_case_     							=       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			) ,  10_1122			)




				def        a				(   self			):
											self.assertEqual(self.get_tokenizer().vocab_size ,  10_1122			)




				@require_torch
				def        a				(   self			):
											snake_case_     							=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_     							=       [0, 57, 3018, 7_0307, 91, 2]

											snake_case_     							=       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			)
											snake_case_     							=       batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											if not self.test_rust_tokenizer:
																		return

											snake_case_     							=       self.get_tokenizer()
											snake_case_     							=       self.get_rust_tokenizer()

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

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

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

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




				@slow
				def        a				(   self			):
											# fmt: off
											snake_case_     							=       {'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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.
											snake_case_     							=       [
											    '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 ,  )







_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




