





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


_UpperCAmelCase        =				get_tests_dir('fixtures/test_sentencepiece.model')
_UpperCAmelCase        =				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_UpperCAmelCase        =				'pt' if is_torch_available() else 'tf'



@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase					(	__lowercase  ,						unittest.TestCase     ):
							_UpperCamelCase				:					Optional[Any]         =   CamembertTokenizer
							_UpperCamelCase				:					Any         =   CamembertTokenizerFast
							_UpperCamelCase				:					Dict         =   True
							_UpperCamelCase				:					Union[str, Any]         =   True

							def lowercase (		self: Dict		)       -> List[Any]:




														"""simple docstring"""


														super().setUp()

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

							def lowercase (		self: Any		)       -> str:




														"""simple docstring"""


														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 lowercase (		self: int		)       -> Any:




														"""simple docstring"""


														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 lowercase (		self: Any		)       -> Any:




														"""simple docstring"""


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

							def lowercase (		self: Dict		)       -> List[str]:




														"""simple docstring"""


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




														"""simple docstring"""


														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 lowercase (		self: Dict		)       -> Tuple:




														"""simple docstring"""


														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					,   )








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

