


'''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


_lowercase    :    List[str]			     =  get_tests_dir("""fixtures/test_sentencepiece.model""")
_lowercase    :    Optional[int]			     =  get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_lowercase    :    Union[str, Any]			     =  """pt""" if is_torch_available() else """tf"""






@require_sentencepiece
@require_tokenizers
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[int]							= CamembertTokenizer
				__magic_name__       :   Dict							= CamembertTokenizerFast
				__magic_name__       :   int							= True
				__magic_name__       :   Optional[int]							= True




				def   a__(	self    :				Optional[Any]   )->  Any:

										"""simple docstring"""




										super().setUp()

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




				def   a__(	self    :				Dict   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			'''<pad>'''
										UpperCAmelCase         =			1

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




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			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   a__(	self    :				Optional[Any]   )->  Optional[Any]:

										"""simple docstring"""




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




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

										"""simple docstring"""




										UpperCAmelCase         =			CamembertTokenizer(lowerCAmelCase   )
										tokenizer.save_pretrained(self.tmpdirname   )
										UpperCAmelCase         =			CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

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

										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										UpperCAmelCase         =			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)
										UpperCAmelCase         =			tokenizer.convert_ids_to_tokens(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.tokenize(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )




				def   a__(	self    :				Union[str, Any]   )->  List[Any]:

										"""simple docstring"""




										if not self.test_rust_tokenizer:
																return

										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			self.get_rust_tokenizer()

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

										UpperCAmelCase         =			tokenizer.tokenize(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.tokenize(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			self.get_rust_tokenizer()
										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )





				@slow
				def   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			{'''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         =			[
										    '''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   ,					)









'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )





