


"""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


__lowerCamelCase      :Optional[Any]	    =		get_tests_dir('fixtures/test_sentencepiece.model')
__lowerCamelCase      :Optional[Any]	    =		get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__lowerCamelCase      :Any	    =		'pt' if is_torch_available() else 'tf'


@require_sentencepiece
@require_tokenizers
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=CamembertTokenizer
     snake_case__	:					Any													=CamembertTokenizerFast
     snake_case__	:					Dict													=True
     snake_case__	:					Union[str, Any]													=True

     def 		a__						(	self:						Dict	)->    List[Any]:
           super().setUp()

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

     def 		a__						(	self:						Any	)->    str:
           lowerCamelCase					:					List[str]        =	"""<pad>"""
           lowerCamelCase					:					str        =	1

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

     def 		a__						(	self:						int	)->    Any:
           lowerCamelCase					:					Any        =	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 		a__						(	self:						Any	)->    Any:
           self.assertEqual(self.get_tokenizer().vocab_size     ,		1_005	)

     def 		a__						(	self:						Dict	)->    List[str]:
           lowerCamelCase					:					Any        =	CamembertTokenizer(__a	)
           tokenizer.save_pretrained(self.tmpdirname	)
           lowerCamelCase					:					List[str]        =	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

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

           lowerCamelCase					:					List[str]        =	tokenizer.encode(__a	)
           lowerCamelCase					:					int        =	rust_tokenizer.encode(__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					int        =	tokenizer.encode(__a     ,		add_special_tokens=__a	)
           lowerCamelCase					:					str        =	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)
           lowerCamelCase					:					List[Any]        =	tokenizer.convert_ids_to_tokens(__a	)
           lowerCamelCase					:					Tuple        =	rust_tokenizer.tokenize(__a	)
           self.assertListEqual(__a     ,		__a	)

     def 		a__						(	self:						List[str]	)->    Union[str, Any]:
           if not self.test_rust_tokenizer:
                 return

           lowerCamelCase					:					Any        =	self.get_tokenizer()
           lowerCamelCase					:					Dict        =	self.get_rust_tokenizer()

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

           lowerCamelCase					:					Optional[int]        =	tokenizer.tokenize(__a	)
           lowerCamelCase					:					Optional[Any]        =	rust_tokenizer.tokenize(__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Tuple        =	tokenizer.encode(__a     ,		add_special_tokens=__a	)
           lowerCamelCase					:					str        =	rust_tokenizer.encode(__a     ,		add_special_tokens=__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Dict        =	self.get_rust_tokenizer()
           lowerCamelCase					:					Tuple        =	tokenizer.encode(__a	)
           lowerCamelCase					:					List[str]        =	rust_tokenizer.encode(__a	)
           self.assertListEqual(__a     ,		__a	)







     @slow
     def 		a__						(	self:						Dict	)->    Tuple:
           # fmt: off
           lowerCamelCase					:					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.
           lowerCamelCase					:					Optional[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 OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['GPTSw3Tokenizer']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




