


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     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  CamembertTokenizer
    UpperCAmelCase__						:		Tuple      =  CamembertTokenizerFast
    UpperCAmelCase__						:		Optional[Any]      =  True
    UpperCAmelCase__						:		List[Any]      =  True






    def _a					(			self    )  ->       List[str]:
         super().setUp()

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






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							='<pad>'
         __UpperCamelCase    							=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:
         __UpperCamelCase    							=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_    )					,   1004    )






    def _a					(			self    )  ->       int:
         self.assertEqual(self.get_tokenizer().vocab_size					,   1005    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=CamembertTokenizer(A_    )
         tokenizer.save_pretrained(self.tmpdirname    )
         __UpperCamelCase    							=CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

         __UpperCamelCase    							='I was born in 92000, and this is falsé.'

         __UpperCamelCase    							=tokenizer.encode(A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
         __UpperCamelCase    							=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    							=tokenizer.convert_ids_to_tokens(A_    )
         __UpperCamelCase    							=rust_tokenizer.tokenize(A_    )
         self.assertListEqual(A_					,   A_    )






    def _a					(			self    )  ->       Optional[Any]:
         if not self.test_rust_tokenizer:
              return

         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=self.get_rust_tokenizer()

         __UpperCamelCase    							='I was born in 92000, and this is falsé.'

         __UpperCamelCase    							=tokenizer.tokenize(A_    )
         __UpperCamelCase    							=rust_tokenizer.tokenize(A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_					,   add_special_tokens=A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=self.get_rust_tokenizer()
         __UpperCamelCase    							=tokenizer.encode(A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_    )
         self.assertListEqual(A_					,   A_    )






    @slow
    def _a					(			self    )  ->       Union[str, Any]:
         # fmt: off
         __UpperCamelCase    							={'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.
         __UpperCamelCase    							=[
             '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_					,   )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
       'JukeboxModel',
       'JukeboxPreTrainedModel',
       'JukeboxVQVAE',
       'JukeboxPrior',
   ]

if TYPE_CHECKING:
   from .configuration_jukebox import (
       JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
       JukeboxConfig,
       JukeboxPriorConfig,
       JukeboxVQVAEConfig,
   )
   from .tokenization_jukebox import JukeboxTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_jukebox import (
          JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
          JukeboxModel,
          JukeboxPreTrainedModel,
          JukeboxPrior,
          JukeboxVQVAE,
      )

else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





