"""simple docstring"""






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_A   					=	get_tests_dir('fixtures/test_sentencepiece.model')
_A   					=	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_A   					=	'pt' if is_torch_available() else 'tf'

@require_sentencepiece
@require_tokenizers
class lowerCamelCase		(__a    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		CamembertTokenizer
					a   	=		CamembertTokenizerFast
					a   	=		True
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				int		)							->       Any:
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE__		  =				CamembertTokenizer(lowerCAmelCase_		)
									tokenizer.save_pretrained(self.tmpdirname		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				"<pad>"
									SCREAMING_SNAKE_CASE__		  =				1

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



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				list(self.get_tokenizer().get_vocab().keys()		)

									self.assertEqual(vocab_keys[0]   , "<s>NOTUSED"		)
									self.assertEqual(vocab_keys[1]   , "<pad>"		)
									self.assertEqual(vocab_keys[-1]   , "<mask>"		)
									self.assertEqual(len(lowerCAmelCase_		)   , 1004		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Union[str, Any]:
									self.assertEqual(self.get_tokenizer().vocab_size   , 1005		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				CamembertTokenizer(lowerCAmelCase_		)
									tokenizer.save_pretrained(self.tmpdirname		)
									SCREAMING_SNAKE_CASE__		  =				CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

									SCREAMING_SNAKE_CASE__		  =				"I was born in 92000, and this is falsé."

									SCREAMING_SNAKE_CASE__		  =				tokenizer.encode(lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.encode(lowerCAmelCase_		)
									self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_		)

									SCREAMING_SNAKE_CASE__		  =				tokenizer.encode(lowerCAmelCase_   , add_special_tokens=lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.encode(lowerCAmelCase_   , add_special_tokens=lowerCAmelCase_		)
									self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_		)

									# <unk> tokens are not the same for `rust` than for `slow`.
									# Because spm gives back raw token instead of `unk` in EncodeAsPieces
									# tokens = tokenizer.tokenize(sequence)
									SCREAMING_SNAKE_CASE__		  =				tokenizer.convert_ids_to_tokens(lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.tokenize(lowerCAmelCase_		)
									self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:







									if not self.test_rust_tokenizer:
													return

									SCREAMING_SNAKE_CASE__		  =				self.get_tokenizer()
									SCREAMING_SNAKE_CASE__		  =				self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE__		  =				"I was born in 92000, and this is falsé."

									SCREAMING_SNAKE_CASE__		  =				tokenizer.tokenize(lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.tokenize(lowerCAmelCase_		)
									self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_		)

									SCREAMING_SNAKE_CASE__		  =				tokenizer.encode(lowerCAmelCase_   , add_special_tokens=lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.encode(lowerCAmelCase_   , add_special_tokens=lowerCAmelCase_		)
									self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_		)

									SCREAMING_SNAKE_CASE__		  =				self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE__		  =				tokenizer.encode(lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.encode(lowerCAmelCase_		)
									self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_		)



					@slow
					def 			lowerCAmelCase_					(						self			:				str		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], "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, 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]]}  # noqa: E501
									# fmt: on

									# camembert is a french model. So we also use french texts.
									SCREAMING_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=lowerCAmelCase_   , model_name="camembert-base"   , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"   , sequences=lowerCAmelCase_   , )





"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

				if not isinstance(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								raise ValueError("multiplicative_persistence() only accepts integral values"       )
				if num < 0:
								raise ValueError("multiplicative_persistence() does not accept negative values"       )

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

								SCREAMING_SNAKE_CASE__		  =				1
								for i in range(0  ,					len(__UpperCAmelCase       )       ):
												total *= numbers[i]

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

				if not isinstance(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								raise ValueError("additive_persistence() only accepts integral values"       )
				if num < 0:
								raise ValueError("additive_persistence() does not accept negative values"       )

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

								SCREAMING_SNAKE_CASE__		  =				0
								for i in range(0  ,					len(__UpperCAmelCase       )       ):
												total += numbers[i]

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps


if __name__ == "__main__":
						import doctest

						doctest.testmod()




