


import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    set()
SCREAMING_SNAKE_CASE__				:    Optional[int]         =    get_logger(__name__)




def       a__						(		snake_case__				:       Optional[str] = None    ):

  def decorator(snake_case__				:       Callable    ):
    global _emitted_deprecation_warnings

    if inspect.isclass(snake_case__    ):
      _UpperCAmelCase		:       int       =    deprecated_class_or_function.__init__
      _UpperCAmelCase		:       Tuple       =    deprecated_class_or_function.__name__
    else:
      _UpperCAmelCase		:       List[str]       =    deprecated_class_or_function
      _UpperCAmelCase		:       Union[str, Any]       =    deprecated_function.__name__
      # Support deprecating __init__ class method: class name instead
      _UpperCAmelCase		:       Optional[int]       =    name if name != """__init__""" else deprecated_function.__qualname__.split("""."""    )[-2]

    _UpperCAmelCase		:       List[str]       =    (
        f'''{name} is deprecated and will be removed in the next major version of datasets.''' + f''' {help_message}'''
        if help_message
        else """"""
    )

    @wraps(snake_case__    )
    def wrapper(*snake_case__				:       List[Any]	,      **snake_case__				:       Optional[int]    ):
      _UpperCAmelCase		:       Any       =    hash(snake_case__    )
      if func_hash not in _emitted_deprecation_warnings:
        warnings.warn(snake_case__	,      category=snake_case__	,      stacklevel=2    )
        _emitted_deprecation_warnings.add(snake_case__    )
      return deprecated_function(*snake_case__	,      **snake_case__    )

    _UpperCAmelCase		:       Optional[int]       =    """deprecated"""

    if inspect.isclass(snake_case__    ):
      _UpperCAmelCase		:       List[Any]       =    wrapper
      return deprecated_class_or_function
    else:
      return wrapper

  return decorator


class 		_SCREAMING_SNAKE_CASE    (	enum.EnumMeta    ):


     def __getattribute__(     cls       ,				A_						):
       _UpperCAmelCase		:       Union[str, Any]       =    super().__getattribute__(A_						)
       if isinstance(A_       ,				enum.Enum						) and obj._on_access:
         obj._on_access()
       return obj


     def __getitem__(     cls       ,				A_						):
       _UpperCAmelCase		:       Dict       =    super().__getitem__(A_						)
       if member._on_access:
         member._on_access()
       return member




     def __call__(     cls       ,				A_       ,				A_=None       ,				*, A_=None       ,				A_=None       ,				A_=None       ,				A_=1						):
       _UpperCAmelCase		:       Tuple       =    super().__call__(A_       ,				A_       ,				module=A_       ,				qualname=A_       ,				type=A_       ,				start=A_						)
       if isinstance(A_       ,				enum.Enum						) and obj._on_access:
         obj._on_access()
       return obj


class 		_SCREAMING_SNAKE_CASE    (	enum.Enum       ,  metaclass=A    ):


     def __new__(     cls       ,				A_						):
       _UpperCAmelCase		:       int       =    object.__new__(cls						)
       _UpperCAmelCase		:       int       =    value
       _UpperCAmelCase		:       Union[str, Any]       =    member.deprecate
       return member


     @property
     def 					__snake_case(     self						):
       return ""




     def 					__snake_case(     self						):
       _UpperCAmelCase		:       str       =    F''' {self.help_message}''' if self.help_message else """"""
       warnings.warn(
           F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
           + help_message       ,				A_       ,				stacklevel=3       ,				)









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:
 SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    None


SCREAMING_SNAKE_CASE__				:    Dict         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				:    List[Any]         =    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__				:    Any         =    {
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE__				:    Optional[int]         =    {
    '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 		_SCREAMING_SNAKE_CASE    (	A    ):
     __SCREAMING_SNAKE_CASE    				=						VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE    				=						PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE    				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE    				=						['''input_ids''', '''attention_mask''']
     __SCREAMING_SNAKE_CASE    				=						XLMRobertaTokenizer


     def __init__(     self       ,				A_=None       ,				A_=None       ,				A_="<s>"       ,				A_="</s>"       ,				A_="</s>"       ,				A_="<s>"       ,				A_="<unk>"       ,				A_="<pad>"       ,				A_="<mask>"       ,				**A_       ,				):
       # Mask token behave like a normal word, i.e. include the space before it
       _UpperCAmelCase		:       int       =    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		:       Union[str, Any]       =    vocab_file
       _UpperCAmelCase		:       List[str]       =    False if not self.vocab_file else True


     def 					__snake_case(     self       ,				A_       ,				A_ = None						):

       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _UpperCAmelCase		:       Optional[Any]       =    [self.cls_token_id]
       _UpperCAmelCase		:       Union[str, Any]       =    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 					__snake_case(     self       ,				A_       ,				A_ = None						):
       _UpperCAmelCase		:       Tuple       =    [self.sep_token_id]
       _UpperCAmelCase		:       Optional[int]       =    [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       ,				A_       ,				A_ = None						):
       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		:       List[str]       =    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,)





