"""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_:					str     =					get_tests_dir("fixtures/test_sentencepiece.model")
lowerCAmelCase_:					int     =					get_tests_dir("fixtures/test_sentencepiece_bpe.model")

lowerCAmelCase_:					List[Any]     =					"pt" if is_torch_available() else "tf"






@require_sentencepiece
@require_tokenizers
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      CamembertTokenizer
   snake_case_							=      CamembertTokenizerFast
   snake_case_							=      True
   snake_case_							=      True


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().setUp()

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"<pad>"
        lowercase__				 =							1

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							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  ),				1004  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.assertEqual(self.get_tokenizer().vocab_size,				1005  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							CamembertTokenizer(_UpperCAmelCase  )
        tokenizer.save_pretrained(self.tmpdirname  )
        lowercase__				 =							CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

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

        lowercase__				 =							tokenizer.encode(_UpperCAmelCase  )
        lowercase__				 =							rust_tokenizer.encode(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							tokenizer.encode(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase  )
        lowercase__				 =							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)
        lowercase__				 =							tokenizer.convert_ids_to_tokens(_UpperCAmelCase  )
        lowercase__				 =							rust_tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if not self.test_rust_tokenizer:
             return

        lowercase__				 =							self.get_tokenizer()
        lowercase__				 =							self.get_rust_tokenizer()

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

        lowercase__				 =							tokenizer.tokenize(_UpperCAmelCase  )
        lowercase__				 =							rust_tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							tokenizer.encode(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase  )
        lowercase__				 =							rust_tokenizer.encode(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							self.get_rust_tokenizer()
        lowercase__				 =							tokenizer.encode(_UpperCAmelCase  )
        lowercase__				 =							rust_tokenizer.encode(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{"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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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.
        lowercase__				 =							[
            "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"""


from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




