"""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


_a				:		Optional[int]	   =       get_tests_dir('fixtures/test_sentencepiece.model')
_a				:		int	   =       get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_a				:		Union[str, Any]	   =       'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      str		 =		CamembertTokenizer
     _UpperCamelCase							:      int		 =		CamembertTokenizerFast
     _UpperCamelCase							:      Optional[Any]		 =		True
     _UpperCamelCase							:      Dict		 =		True






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

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:	Tuple        =					CamembertTokenizer(a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					"""<pad>"""
       _lowerCAmelCase						:	int        =					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  ):
       _lowerCAmelCase						:	str        =					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    __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size    ,					1005  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					CamembertTokenizer(a__  )
       tokenizer.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:	Dict        =					CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

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

       _lowerCAmelCase						:	Union[str, Any]        =					tokenizer.encode(a__  )
       _lowerCAmelCase						:	str        =					rust_tokenizer.encode(a__  )
       self.assertListEqual(a__    ,					a__  )

       _lowerCAmelCase						:	List[Any]        =					tokenizer.encode(a__    ,					add_special_tokens=a__  )
       _lowerCAmelCase						:	Optional[Any]        =					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)
       _lowerCAmelCase						:	List[Any]        =					tokenizer.convert_ids_to_tokens(a__  )
       _lowerCAmelCase						:	str        =					rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__    ,					a__  )






     def    __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase						:	List[str]        =					self.get_tokenizer()
       _lowerCAmelCase						:	int        =					self.get_rust_tokenizer()

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

       _lowerCAmelCase						:	Any        =					tokenizer.tokenize(a__  )
       _lowerCAmelCase						:	List[str]        =					rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__    ,					a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					tokenizer.encode(a__    ,					add_special_tokens=a__  )
       _lowerCAmelCase						:	List[Any]        =					rust_tokenizer.encode(a__    ,					add_special_tokens=a__  )
       self.assertListEqual(a__    ,					a__  )

       _lowerCAmelCase						:	str        =					self.get_rust_tokenizer()
       _lowerCAmelCase						:	Any        =					tokenizer.encode(a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					rust_tokenizer.encode(a__  )
       self.assertListEqual(a__    ,					a__  )






     @slow
     def    __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:	int        =					{"""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.
       _lowerCAmelCase						:	Union[str, Any]        =					[
           """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__    ,					)





"""simple docstring"""




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = True    ,					a__ = "arrow"    ,					**a__    ,					):
       super().__init__(
           split=a__    ,					features=a__    ,					cache_dir=a__    ,					keep_in_memory=a__    ,					streaming=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					load_from_cache_file
       _lowerCAmelCase						:	str        =					file_format
       _lowerCAmelCase						:	Dict        =					Spark(
           df=a__    ,					features=a__    ,					cache_dir=a__    ,					working_dir=a__    ,					**a__    ,					)






     def    __A  (		self  ):
       if self.streaming:
         return self.builder.as_streaming_dataset(split=self.split  )
       _lowerCAmelCase						:	Dict        =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=a__    ,					file_format=self._file_format    ,					)
       return self.builder.as_dataset(split=self.split  )




