


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


_snake_case					:		str      =    get_tests_dir("fixtures/test_sentencepiece.model")
_snake_case					:		Optional[Any]      =    get_tests_dir("fixtures/test_sentencepiece_bpe.model")

_snake_case					:		str      =    """pt""" if is_torch_available() else """tf"""

@require_sentencepiece
@require_tokenizers
class     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  CamembertTokenizer
							__UpperCAmelCase   :							Optional[int]					  =  CamembertTokenizerFast
							__UpperCAmelCase   :							str					  =  True
							__UpperCAmelCase   :							Dict					  =  True

							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														super().setUp()

														# We have a SentencePiece fixture for testing
														__snake_case		:      Dict										=       CamembertTokenizer(lowerCamelCase    )
														tokenizer.save_pretrained(self.tmpdirname    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[Any]:
														__snake_case		:      List[str]										=       "<pad>"
														__snake_case		:      Any										=       1

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

							def    __snake_case (							self	:  Dict    )     ->   Tuple:
														__snake_case		:      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(lowerCamelCase    )   ,     1004    )

							def    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														self.assertEqual(self.get_tokenizer().vocab_size   ,     1005    )

							def    __snake_case (							self	:  Dict    )     ->   Optional[int]:
														__snake_case		:      int										=       CamembertTokenizer(lowerCamelCase    )
														tokenizer.save_pretrained(self.tmpdirname    )
														__snake_case		:      Any										=       CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

														__snake_case		:      Union[str, Any]										=       "I was born in 92000, and this is falsé."

														__snake_case		:      Optional[Any]										=       tokenizer.encode(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       rust_tokenizer.encode(lowerCamelCase    )
														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )

														__snake_case		:      int										=       tokenizer.encode(lowerCamelCase   ,     add_special_tokens=lowerCamelCase    )
														__snake_case		:      Optional[int]										=       rust_tokenizer.encode(lowerCamelCase   ,     add_special_tokens=lowerCamelCase    )
														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )

														# <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		:      Optional[Any]										=       tokenizer.convert_ids_to_tokens(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       rust_tokenizer.tokenize(lowerCamelCase    )
														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )

							def    __snake_case (							self	:  List[str]    )     ->   str:




														if not self.test_rust_tokenizer:
																					return

														__snake_case		:      Optional[int]										=       self.get_tokenizer()
														__snake_case		:      Any										=       self.get_rust_tokenizer()

														__snake_case		:      Tuple										=       "I was born in 92000, and this is falsé."

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

														__snake_case		:      Optional[Any]										=       tokenizer.encode(lowerCamelCase   ,     add_special_tokens=lowerCamelCase    )
														__snake_case		:      List[Any]										=       rust_tokenizer.encode(lowerCamelCase   ,     add_special_tokens=lowerCamelCase    )
														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )

														__snake_case		:      List[Any]										=       self.get_rust_tokenizer()
														__snake_case		:      Any										=       tokenizer.encode(lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       rust_tokenizer.encode(lowerCamelCase    )
														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )






							@slow
							def    __snake_case (							self	:  Union[str, Any]    )     ->   List[Any]:
														__snake_case		:      Any										=       {"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.
														__snake_case		:      Tuple										=       [
														    "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=lowerCamelCase   ,     model_name="camembert-base"   ,     revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"   ,     sequences=lowerCamelCase   ,     )



from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  ["speech"]

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

