
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


lowercase__	:Any										=					get_tests_dir("fixtures/test_sentencepiece.model")
lowercase__	:Union[str, Any]										=					get_tests_dir("fixtures/test_sentencepiece_bpe.model")

lowercase__	:Any										=					"""pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class        lowercase	(      snake_case_ ,						unittest.TestCase	):
       lowercase_ : Dict       =CamembertTokenizer
       lowercase_ : Dict       =CamembertTokenizerFast
       lowercase_ : Any       =True
       lowercase_ : List[str]       =True




       def 				A__   (       self):
            super().setUp()

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




       def 				A__   (       self):
            lowercase					=		'''<pad>'''
            lowercase					=		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):
            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(A__)     ,1_0_0_4)




       def 				A__   (       self):
            self.assertEqual(self.get_tokenizer().vocab_size     ,1_0_0_5)




       def 				A__   (       self):
            lowercase					=		CamembertTokenizer(A__)
            tokenizer.save_pretrained(self.tmpdirname)
            lowercase					=		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

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

            lowercase					=		tokenizer.encode(A__)
            lowercase					=		rust_tokenizer.encode(A__)
            self.assertListEqual(A__     ,A__)

            lowercase					=		tokenizer.encode(A__     ,add_special_tokens=A__)
            lowercase					=		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)
            lowercase					=		tokenizer.convert_ids_to_tokens(A__)
            lowercase					=		rust_tokenizer.tokenize(A__)
            self.assertListEqual(A__     ,A__)




       def 				A__   (       self):
            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(A__)
            lowercase					=		rust_tokenizer.tokenize(A__)
            self.assertListEqual(A__     ,A__)

            lowercase					=		tokenizer.encode(A__     ,add_special_tokens=A__)
            lowercase					=		rust_tokenizer.encode(A__     ,add_special_tokens=A__)
            self.assertListEqual(A__     ,A__)

            lowercase					=		self.get_rust_tokenizer()
            lowercase					=		tokenizer.encode(A__)
            lowercase					=		rust_tokenizer.encode(A__)
            self.assertListEqual(A__     ,A__)





       @slow
       def 				A__   (       self):
            # fmt: off
            lowercase					=		{'''input_ids''': [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 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=A__     ,model_name='''camembert-base'''     ,revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''     ,sequences=A__     ,)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




