



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,)









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:
							UpperCamelCase__						=		None


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__						=		{
    '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'
        ),
    },
}

UpperCamelCase__						=		{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}


class 							__SCREAMING_SNAKE_CASE				(						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					,			__lowercase=None					,			__lowercase=None					,			__lowercase="<s>"					,			__lowercase="</s>"					,			__lowercase="</s>"					,			__lowercase="<s>"					,			__lowercase="<unk>"					,			__lowercase="<pad>"					,			__lowercase="<mask>"					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				AddedToken(__lowercase					,			lstrip=__lowercase					,			rstrip=__lowercase   ) if isinstance(__lowercase					,			__lowercase   ) else mask_token

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

						lowerCamelCase_				:						str				  =				vocab_file
						lowerCamelCase_				:						str				  =				False if not self.vocab_file else True

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				[self.sep_token_id]
						lowerCamelCase_				:						Dict				  =				[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					,			__lowercase					,			__lowercase = 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(__lowercase   ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory."   )
							return
						lowerCamelCase_				:						List[Any]				  =				os.path.join(
						    __lowercase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

						return (out_vocab_file,)
