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

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


@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						List[str]      =				CamembertTokenizer
    a__     :						int      =				CamembertTokenizerFast
    a__     :						Optional[int]      =				True
    a__     :						str      =				True







    def       UpperCamelCase__       (     self)	->     Dict:
        super().setUp()

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







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		'''<pad>'''
        __UpperCamelCase					:Optional[int]        =		1

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







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:List[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(__lowercase)			,			1_004)







    def       UpperCamelCase__       (     self)	->     List[str]:
        self.assertEqual(self.get_tokenizer().vocab_size			,			1_005)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:Dict        =		CamembertTokenizer(__lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        __UpperCamelCase					:Tuple        =		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        __UpperCamelCase					:Tuple        =		'''I was born in 92000, and this is falsé.'''

        __UpperCamelCase					:Optional[Any]        =		tokenizer.encode(__lowercase)
        __UpperCamelCase					:List[str]        =		rust_tokenizer.encode(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					: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					:List[Any]        =		tokenizer.convert_ids_to_tokens(__lowercase)
        __UpperCamelCase					:Dict        =		rust_tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        if not self.test_rust_tokenizer:
            return

        __UpperCamelCase					:List[Any]        =		self.get_tokenizer()
        __UpperCamelCase					:str        =		self.get_rust_tokenizer()

        __UpperCamelCase					:Dict        =		'''I was born in 92000, and this is falsé.'''

        __UpperCamelCase					:Any        =		tokenizer.tokenize(__lowercase)
        __UpperCamelCase					:Tuple        =		rust_tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:int        =		tokenizer.encode(__lowercase			,			add_special_tokens=__lowercase)
        __UpperCamelCase					:List[Any]        =		rust_tokenizer.encode(__lowercase			,			add_special_tokens=__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:List[Any]        =		self.get_rust_tokenizer()
        __UpperCamelCase					:Optional[int]        =		tokenizer.encode(__lowercase)
        __UpperCamelCase					:Any        =		rust_tokenizer.encode(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)







    @slow
    def       UpperCamelCase__       (     self)	->     Tuple:
        # fmt: off
        __UpperCamelCase					:List[str]        =		{'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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					: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=__lowercase			,			model_name='''camembert-base'''			,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''			,			sequences=__lowercase			,			)






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		ArgumentParser(
        description=(
            '''PyTorch TPU distributed training launch '''
            '''helper utility that will spawn up '''
            '''multiple distributed processes'''
        )     )

    # Optional arguments for the launch helper
    parser.add_argument('''--num_cores'''  ,      type=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_SNAKE_CASE  ,      help=(
            '''The full path to the single TPU training '''
            '''program/script to be launched in parallel, '''
            '''followed by all the arguments for the '''
            '''training script'''
        )  ,      )

    # rest from the training program
    parser.add_argument('''training_script_args'''  ,      nargs=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores     )]
    xmp.spawn(mod._mp_fn  ,      args=()  ,      nprocs=args.num_cores     )


if __name__ == "__main__":
    main()





