


"""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


__SCREAMING_SNAKE_CASE							:      str						=      get_tests_dir('fixtures/test_sentencepiece.model')
__SCREAMING_SNAKE_CASE							:      int						=      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__SCREAMING_SNAKE_CASE							:      Tuple						=      'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class        __A       (snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Any							 =      CamembertTokenizer
							__lowercase:						Optional[int]							 =      CamembertTokenizerFast
							__lowercase:						int							 =      True
							__lowercase:						int							 =      True



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														super().setUp()

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



							def 						lowerCAmelCase    (			self							: str    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""<pad>"""
														snake_case_          =						1

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



							def 						lowerCAmelCase    (			self							: Any    )   ->Any:



														"""simple docstring"""



														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(UpperCAmelCase_    )						,    1_004    )



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



														"""simple docstring"""



														self.assertEqual(self.get_tokenizer().vocab_size						,    1_005    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						CamembertTokenizer(UpperCAmelCase_    )
														tokenizer.save_pretrained(self.tmpdirname    )
														snake_case_          =						CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

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

														snake_case_          =						tokenizer.encode(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.encode(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )

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

														# <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)
														snake_case_          =						tokenizer.convert_ids_to_tokens(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.tokenize(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



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



														"""simple docstring"""



														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(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.tokenize(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )

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

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



							@slow
							def 						lowerCAmelCase    (			self							: List[str]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						{"""input_ids""": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
														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=UpperCAmelCase_						,    model_name="""camembert-base"""						,    revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						,    sequences=UpperCAmelCase_						,    )








"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							return "\n".join(
							    f"""{number} * {i} = {number * i}""" for i in range(1			,	number_of_terms + 1			)			)


if __name__ == "__main__":
				print(multiplication_table(number=5, number_of_terms=10))



