





'''simple docstring'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

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




class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		ArgumentParser					)   ->					Tuple:

            '''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       :		str	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		bool	,			lowerCAmelCase__       :		bool					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	model
            _UpperCamelCase       =	cache
            _UpperCamelCase       =	force
            _UpperCamelCase       =	trust_remote_code







       def snake_case__						(							self       :		Dict					)   ->					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					)










'''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__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

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





       def snake_case__						(							self       :		Optional[int]					)   ->					List[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 snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''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 snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''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 snake_case__						(							self       :		Optional[int]					)   ->					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 snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''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__	,			)



