

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_        =			get_tests_dir('''fixtures/test_sentencepiece.model''')
snake_case_        =			get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

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


@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					CamembertTokenizer
    __lowerCamelCase						:	Dict														=					CamembertTokenizerFast
    __lowerCamelCase						:	Optional[int]														=					True
    __lowerCamelCase						:	List[Any]														=					True





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

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





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	'<pad>'
         lowercase__   :       str				=	1

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





    def 				snake_case_    (				self):
         lowercase__   :       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 				snake_case_    (				self):
         self.assertEqual(self.get_tokenizer().vocab_size						,       1005)





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	CamembertTokenizer(a)
         tokenizer.save_pretrained(self.tmpdirname)
         lowercase__   :       Tuple				=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

         lowercase__   :       Optional[Any]				=	'I was born in 92000, and this is falsé.'

         lowercase__   :       int				=	tokenizer.encode(a)
         lowercase__   :       Optional[Any]				=	rust_tokenizer.encode(a)
         self.assertListEqual(a						,       a)

         lowercase__   :       str				=	tokenizer.encode(a						,       add_special_tokens=a)
         lowercase__   :       int				=	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__   :       Optional[int]				=	tokenizer.convert_ids_to_tokens(a)
         lowercase__   :       Tuple				=	rust_tokenizer.tokenize(a)
         self.assertListEqual(a						,       a)





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

         lowercase__   :       str				=	self.get_tokenizer()
         lowercase__   :       str				=	self.get_rust_tokenizer()

         lowercase__   :       Any				=	'I was born in 92000, and this is falsé.'

         lowercase__   :       Union[str, Any]				=	tokenizer.tokenize(a)
         lowercase__   :       Any				=	rust_tokenizer.tokenize(a)
         self.assertListEqual(a						,       a)

         lowercase__   :       str				=	tokenizer.encode(a						,       add_special_tokens=a)
         lowercase__   :       List[Any]				=	rust_tokenizer.encode(a						,       add_special_tokens=a)
         self.assertListEqual(a						,       a)

         lowercase__   :       Dict				=	self.get_rust_tokenizer()
         lowercase__   :       Any				=	tokenizer.encode(a)
         lowercase__   :       str				=	rust_tokenizer.encode(a)
         self.assertListEqual(a						,       a)





    @slow
    def 				snake_case_    (				self):
         # fmt: off
         lowercase__   :       Dict				=	{'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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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.
         lowercase__   :       Dict				=	[
             '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 Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__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						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       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)

