



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__       =			None


A__       =			logging.get_logger(__name__)

A__       =			{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

A__       =			{
    """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__       =			{
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							VOCAB_FILES_NAMES
				__lowerCamelCase					=							PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase					=							['''input_ids''', '''attention_mask''']
				__lowerCamelCase					=							XLMRobertaTokenizer




				def __init__(				self		,							_snake_case=None		,							_snake_case=None		,							_snake_case="<s>"		,							_snake_case="</s>"		,							_snake_case="</s>"		,							_snake_case="<s>"		,							_snake_case="<unk>"		,							_snake_case="<pad>"		,							_snake_case="<mask>"		,							**_snake_case		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case						) if isinstance(_snake_case		,							_snake_case						) else mask_token

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

									_lowerCAmelCase         =       vocab_file
									_lowerCAmelCase         =       False if not self.vocab_file else True




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_lowerCAmelCase         =       [self.cls_token_id]
									_lowerCAmelCase         =       [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





									_lowerCAmelCase         =       [self.sep_token_id]
									_lowerCAmelCase         =       [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 				snake_case  (				self		,							_snake_case		,							_snake_case = 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(_snake_case						):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory.'						)
														return
									_lowerCAmelCase         =       os.path.join(
									    _snake_case		,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

									return (out_vocab_file,)








def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       str(snake_case     ).strip()
					if not bin_string:
										raise ValueError("""Empty string was passed to the function"""     )
					_lowerCAmelCase         =       bin_string[0] == """-"""
					if is_negative:
										_lowerCAmelCase         =       bin_string[1:]
					if not all(char in """01""" for char in bin_string     ):
										raise ValueError("""Non-binary value was passed to the function"""     )
					_lowerCAmelCase         =       0
					for char in bin_string:
										_lowerCAmelCase         =       2 * decimal_number + int(snake_case     )
					return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()



