
"""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


UpperCAmelCase__   :					Union[str, Any]												=				get_tests_dir('fixtures/test_sentencepiece.model')
UpperCAmelCase__   :					Tuple												=				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

UpperCAmelCase__   :					Optional[Any]												=				'pt' if is_torch_available() else 'tf'



@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		int       =     CamembertTokenizer
				__UpperCamelCase   :		int       =     CamembertTokenizerFast
				__UpperCamelCase   :		Optional[Any]       =     True
				__UpperCamelCase   :		str       =     True



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											super().setUp()

											# We have a SentencePiece fixture for testing
											SCREAMING_SNAKE_CASE__		: List[Any]     =     CamembertTokenizer(SCREAMING_SNAKE_CASE__					)
											tokenizer.save_pretrained(self.tmpdirname					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     """<pad>"""
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     1

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



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     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(SCREAMING_SNAKE_CASE__					)     , 10_04					)



				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											self.assertEqual(self.get_tokenizer().vocab_size     , 10_05					)



				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     CamembertTokenizer(SCREAMING_SNAKE_CASE__					)
											tokenizer.save_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """I was born in 92000, and this is falsé."""

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer.encode(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

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

											# <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__		: Optional[int]     =     tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											if not self.test_rust_tokenizer:
																		return

											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.get_tokenizer()
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.get_rust_tokenizer()

											SCREAMING_SNAKE_CASE__		: List[str]     =     """I was born in 92000, and this is falsé."""

											SCREAMING_SNAKE_CASE__		: List[Any]     =     tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Any     =     tokenizer.encode(SCREAMING_SNAKE_CASE__     , add_special_tokens=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     , add_special_tokens=SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE__		: Dict     =     tokenizer.encode(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: int     =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				@slow
				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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__		: Dict     =     [
											    """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=SCREAMING_SNAKE_CASE__     , model_name="""camembert-base"""     , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""     , sequences=SCREAMING_SNAKE_CASE__     , )






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




