



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

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


@require_sentencepiece
@require_tokenizers
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				CamembertTokenizer
       _lowerCamelCase			    =				CamembertTokenizerFast
       _lowerCamelCase			    =				True
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		"<pad>"
             UpperCAmelCase_  :	List[str]				      =		1

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	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(lowercase)     ,1004)



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		CamembertTokenizer(lowercase)
             tokenizer.save_pretrained(self.tmpdirname)
             UpperCAmelCase_  :	Union[str, Any]				      =		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

             UpperCAmelCase_  :	Any				      =		"I was born in 92000, and this is falsé."

             UpperCAmelCase_  :	Tuple				      =		tokenizer.encode(lowercase)
             UpperCAmelCase_  :	Tuple				      =		rust_tokenizer.encode(lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	List[str]				      =		tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
             UpperCAmelCase_  :	str				      =		rust_tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             # <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_  :	int				      =		tokenizer.convert_ids_to_tokens(lowercase)
             UpperCAmelCase_  :	str				      =		rust_tokenizer.tokenize(lowercase)
             self.assertListEqual(lowercase     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             if not self.test_rust_tokenizer:
                   return

             UpperCAmelCase_  :	List[Any]				      =		self.get_tokenizer()
             UpperCAmelCase_  :	Union[str, Any]				      =		self.get_rust_tokenizer()

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

             UpperCAmelCase_  :	int				      =		tokenizer.tokenize(lowercase)
             UpperCAmelCase_  :	Any				      =		rust_tokenizer.tokenize(lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	Optional[int]				      =		tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
             UpperCAmelCase_  :	List[str]				      =		rust_tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	Dict				      =		self.get_rust_tokenizer()
             UpperCAmelCase_  :	int				      =		tokenizer.encode(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		rust_tokenizer.encode(lowercase)
             self.assertListEqual(lowercase     ,lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		{"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_  :	int				      =		[
                 "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=lowercase     ,model_name="camembert-base"     ,revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"     ,sequences=lowercase     ,)





from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase = None     ,lowercase = None     ,lowercase = None     ,lowercase = None     ,lowercase = False     ,lowercase = False     ,lowercase = None     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		path_or_paths
             UpperCAmelCase_  :	Optional[int]				      =		split if split or isinstance(lowercase     ,lowercase) else "train"
             UpperCAmelCase_  :	Optional[int]				      =		features
             UpperCAmelCase_  :	Dict				      =		cache_dir
             UpperCAmelCase_  :	int				      =		keep_in_memory
             UpperCAmelCase_  :	Tuple				      =		streaming
             UpperCAmelCase_  :	Union[str, Any]				      =		num_proc
             UpperCAmelCase_  :	Union[str, Any]				      =		kwargs



       @abstractmethod
       def  A_   (     self):

             """simple docstring"""





             pass


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase = None     ,lowercase = None     ,lowercase = False     ,lowercase = False     ,lowercase = None     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		features
             UpperCAmelCase_  :	List[Any]				      =		cache_dir
             UpperCAmelCase_  :	List[str]				      =		keep_in_memory
             UpperCAmelCase_  :	Optional[int]				      =		streaming
             UpperCAmelCase_  :	Dict				      =		num_proc
             UpperCAmelCase_  :	str				      =		kwargs



       @abstractmethod
       def  A_   (     self):

             """simple docstring"""





             pass
