





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
				__A       :					Optional[Any]     					=     None


__A       :					Dict     					=     logging.get_logger(__name__)

__A       :					Optional[Any]     					=     {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__A       :					int     					=     {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

__A       :					List[Any]     					=     {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}

class 			A_ (a_    ):
	UpperCAmelCase__      =    VOCAB_FILES_NAMES
	UpperCAmelCase__      =    PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__      =    ['''input_ids''', '''attention_mask''']
	UpperCAmelCase__      =    XLMRobertaTokenizer
	def __init__(					self							,      _A=None							,      _A=None							,      _A="<s>"							,      _A="</s>"							,      _A="</s>"							,      _A="<s>"							,      _A="<unk>"							,      _A="<pad>"							,      _A="<mask>"							,      **_A							,      ):






				'''simple docstring'''







				UpperCAmelCase     	=       AddedToken(_A							,      lstrip=_A							,      rstrip=_A	) if isinstance(_A							,      _A	) else mask_token

				super().__init__(
				    _A							,      tokenizer_file=_A							,      bos_token=_A							,      eos_token=_A							,      sep_token=_A							,      cls_token=_A							,      unk_token=_A							,      pad_token=_A							,      mask_token=_A							,      **_A							,      )

				UpperCAmelCase     	=       vocab_file
				UpperCAmelCase     	=       False if not self.vocab_file else True
	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase     	=       [self.cls_token_id]
				UpperCAmelCase     	=       [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep
	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				UpperCAmelCase     	=       [self.sep_token_id]
				UpperCAmelCase     	=       [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep	) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''	)

				if not os.path.isdir(_A	):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""	)
							return
				UpperCAmelCase     	=       os.path.join(
				    _A							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

				if os.path.abspath(self.vocab_file	) != os.path.abspath(_A	):
							copyfile(self.vocab_file							,      _A	)

				return (out_vocab_file,)










from __future__ import annotations





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       bool:







			'''simple docstring'''

			UpperCAmelCase     	=       42
			UpperCAmelCase     	=       list(UpperCamelCase__				)
			UpperCAmelCase	,    UpperCAmelCase     	=       _vector[:-1], int(_vector[-1]				)
			UpperCAmelCase     	=       [int(UpperCamelCase__				) for digit in __vector]

			vector.reverse()
			for i, digit in enumerate(UpperCamelCase__				):
						if i & 1 == 0:
									UpperCAmelCase     	=       digit * 2
									if doubled > 9:
												doubled -= 9
									check_digit += doubled
						else:
									check_digit += digit

			return check_digit % 10 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				assert is_luhn("79927398713")
				assert not is_luhn("79927398714")



