


"""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


__SCREAMING_SNAKE_CASE							:      str						=      get_tests_dir('fixtures/test_sentencepiece.model')
__SCREAMING_SNAKE_CASE							:      int						=      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__SCREAMING_SNAKE_CASE							:      Tuple						=      'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class        __A       (snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Any							 =      CamembertTokenizer
							__lowercase:						Optional[int]							 =      CamembertTokenizerFast
							__lowercase:						int							 =      True
							__lowercase:						int							 =      True



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														super().setUp()

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



							def 						lowerCAmelCase    (			self							: str    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""<pad>"""
														snake_case_          =						1

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



							def 						lowerCAmelCase    (			self							: Any    )   ->Any:



														"""simple docstring"""



														snake_case_          =						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_    )						,    1_004    )



							def 						lowerCAmelCase    (			self							: int    )   ->Union[str, Any]:



														"""simple docstring"""



														self.assertEqual(self.get_tokenizer().vocab_size						,    1_005    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						CamembertTokenizer(UpperCAmelCase_    )
														tokenizer.save_pretrained(self.tmpdirname    )
														snake_case_          =						CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

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

														snake_case_          =						tokenizer.encode(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.encode(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )

														snake_case_          =						tokenizer.encode(UpperCAmelCase_						,    add_special_tokens=UpperCAmelCase_    )
														snake_case_          =						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)
														snake_case_          =						tokenizer.convert_ids_to_tokens(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.tokenize(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



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



														"""simple docstring"""



														if not self.test_rust_tokenizer:
																					return

														snake_case_          =						self.get_tokenizer()
														snake_case_          =						self.get_rust_tokenizer()

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

														snake_case_          =						tokenizer.tokenize(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.tokenize(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )

														snake_case_          =						tokenizer.encode(UpperCAmelCase_						,    add_special_tokens=UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.encode(UpperCAmelCase_						,    add_special_tokens=UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )

														snake_case_          =						self.get_rust_tokenizer()
														snake_case_          =						tokenizer.encode(UpperCAmelCase_    )
														snake_case_          =						rust_tokenizer.encode(UpperCAmelCase_    )
														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



							@slow
							def 						lowerCAmelCase    (			self							: List[str]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						{"""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.
														snake_case_          =						[
														    """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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



														super().__init__(pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_						,    **UpperCAmelCase_    )

														snake_case_          =						max_position_embeddings
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						type_vocab_size
														snake_case_          =						layer_norm_eps

														# Character config:
														snake_case_          =						downsampling_rate
														snake_case_          =						upsampling_kernel_size
														snake_case_          =						num_hash_functions
														snake_case_          =						num_hash_buckets
														snake_case_          =						local_transformer_stride



