




'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A:					int							=				logging.get_logger(__name__)

_A:					Optional[Any]							=				{"""vocab_file""": """sentencepiece.bpe.model"""}

_A:					int							=				{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

_A:					Dict							=				{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

_A:					Tuple							=				"""▁"""






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			str						   =       VOCAB_FILES_NAMES
    _A :			Optional[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
    _A :			Union[str, Any]						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[Any]						   =       ["""input_ids""", """attention_mask"""]



    def __init__(  self     ,			__A     ,			__A="<s>"     ,			__A="</s>"     ,			__A="</s>"     ,			__A="<s>"     ,			__A="<unk>"     ,			__A="<pad>"     ,			__A="<mask>"     ,			__A = None     ,			**__A     ,			):
     # Mask token behave like a normal word, i.e. include the space before it
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else mask_token

     __UpperCAmelCase					=   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=__A     ,			eos_token=__A     ,			unk_token=__A     ,			sep_token=__A     ,			cls_token=__A     ,			pad_token=__A     ,			mask_token=__A     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**__A     ,			)

     __UpperCAmelCase					=   vocab_file
     __UpperCAmelCase					=   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(str(__A					)					)

     __UpperCAmelCase					=   {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

     __UpperCAmelCase					=   len(self.sp_model					) - 1
     __UpperCAmelCase					=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):

     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 					__lowerCamelCase						(  self     ,			__A     ,			__A = None     ,			__A = False					):
     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=__A     ,			token_ids_a=__A     ,			already_has_special_tokens=__A					)

     if token_ids_a is None:
      return [1] + ([0] * len(__A					)) + [1]
     return [1] + ([0] * len(__A					)) + [1, 1] + ([0] * len(__A					)) + [1]



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     __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]



    @property
    def 					__lowerCamelCase						(  self					):
     return len(self.sp_model					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   {self.convert_ids_to_tokens(__A					): i for i in range(self.vocab_size					)}
     vocab.update(self.added_tokens_encoder					)
     return vocab



    def 					__lowerCamelCase						(  self     ,			__A					):
     return self.sp_model.encode(__A     ,			out_type=__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
     __UpperCAmelCase					=   self.sp_model.PieceToId(__A					)

     return spm_id if spm_id else self.unk_token_id



    def 					__lowerCamelCase						(  self     ,			__A					):
     if index in self.fairseq_ids_to_tokens:
      return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   []
     __UpperCAmelCase					=   ''
     __UpperCAmelCase					=   False
     for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
       if not prev_is_special:
        out_string += " "
       out_string += self.sp_model.decode(__A					) + token
       __UpperCAmelCase					=   True
       __UpperCAmelCase					=   []
      else:
       current_sub_tokens.append(__A					)
       __UpperCAmelCase					=   False
     out_string += self.sp_model.decode(__A					)
     return out_string.strip()



    def __getstate__(  self					):
     __UpperCAmelCase					=   self.__dict__.copy()
     __UpperCAmelCase					=   None
     return state



    def __setstate__(  self     ,			__A					):
     __UpperCAmelCase					=   d

     # for backward compatibility
     if not hasattr(self     ,			'sp_model_kwargs'					):
      __UpperCAmelCase					=   {}

     __UpperCAmelCase					=   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(self.vocab_file					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     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					) and os.path.isfile(self.vocab_file					):
      copyfile(self.vocab_file     ,			__A					)
     elif not os.path.isfile(self.vocab_file					):
      with open(__A     ,			'wb'					) as fi:
       __UpperCAmelCase					=   self.sp_model.serialized_model_proto()
       fi.write(__A					)

     return (out_vocab_file,)












'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A:					List[str]							=				{
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A:					Union[str, Any]							=				[
        """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LukeForEntityClassification""",
        """LukeForEntityPairClassification""",
        """LukeForEntitySpanClassification""",
        """LukeForMultipleChoice""",
        """LukeForQuestionAnswering""",
        """LukeForSequenceClassification""",
        """LukeForTokenClassification""",
        """LukeForMaskedLM""",
        """LukeModel""",
        """LukePreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    _A:					str							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





