



'''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_							: int						 =	get_tests_dir("""fixtures/test_sentencepiece.model""")
A_							: Any						 =	get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

A_							: Any						 =	"""pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class 							lowercase       (  _lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  CamembertTokenizer
 UpperCAmelCase							=  CamembertTokenizerFast
 UpperCAmelCase							=  True
 UpperCAmelCase							=  True







 def _snake_case (							self							)		->   int:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _UpperCAmelCase  :						int			     = CamembertTokenizer(a_							)
        tokenizer.save_pretrained(self.tmpdirname							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						List[str]			     = """<pad>"""
        _UpperCAmelCase  :						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 _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = 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(a_							)  ,1_004							)







 def _snake_case (							self							)		->   Dict:
        self.assertEqual(self.get_tokenizer().vocab_size  ,1_005							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Optional[Any]			     = CamembertTokenizer(a_							)
        tokenizer.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						List[Any]			     = CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

        _UpperCAmelCase  :						Any			     = """I was born in 92000, and this is falsé."""

        _UpperCAmelCase  :						Union[str, Any]			     = tokenizer.encode(a_							)
        _UpperCAmelCase  :						List[Any]			     = rust_tokenizer.encode(a_							)
        self.assertListEqual(a_  ,a_							)

        _UpperCAmelCase  :						str			     = tokenizer.encode(a_  ,add_special_tokens=a_							)
        _UpperCAmelCase  :						Dict			     = rust_tokenizer.encode(a_  ,add_special_tokens=a_							)
        self.assertListEqual(a_  ,a_							)

        # <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)
        _UpperCAmelCase  :						List[Any]			     = tokenizer.convert_ids_to_tokens(a_							)
        _UpperCAmelCase  :						Dict			     = rust_tokenizer.tokenize(a_							)
        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self							)		->   List[Any]:
        if not self.test_rust_tokenizer:
               return

        _UpperCAmelCase  :						Union[str, Any]			     = self.get_tokenizer()
        _UpperCAmelCase  :						int			     = self.get_rust_tokenizer()

        _UpperCAmelCase  :						str			     = """I was born in 92000, and this is falsé."""

        _UpperCAmelCase  :						Dict			     = tokenizer.tokenize(a_							)
        _UpperCAmelCase  :						Dict			     = rust_tokenizer.tokenize(a_							)
        self.assertListEqual(a_  ,a_							)

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

        _UpperCAmelCase  :						Union[str, Any]			     = self.get_rust_tokenizer()
        _UpperCAmelCase  :						Optional[int]			     = tokenizer.encode(a_							)
        _UpperCAmelCase  :						Dict			     = rust_tokenizer.encode(a_							)
        self.assertListEqual(a_  ,a_							)







 @slow
 def _snake_case (							self							)		->   List[Any]:
        # fmt: off
        _UpperCAmelCase  :						Optional[Any]			     = {"""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.
        _UpperCAmelCase  :						Any			     = [
            """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="""camembert-base"""  ,revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""  ,sequences=a_  ,)








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



