






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_		:		Any		     =			logging.get_logger(__name__)

A_		:		Tuple		     =			{'vocab_file': 'spiece.model'}

A_		:		Any		     =			{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

A_		:		Union[str, Any]		     =			{'bert_for_seq_generation': 512}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Any									=VOCAB_FILES_NAMES
       a    :		Optional[Any]									=PRETRAINED_VOCAB_FILES_MAP
       a    :		Optional[int]									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		List[int]									=[]
       a    :		List[Any]									=['''input_ids''', '''attention_mask''']





       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase="<s>"  ,      _lowerCamelCase="</s>"  ,      _lowerCamelCase="<unk>"  ,      _lowerCamelCase="<pad>"  ,      _lowerCamelCase="<::::>"  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
        UpperCamelCase_: List[str]   				=    {} if sp_model_kwargs is None else sp_model_kwargs

        # Add extra_ids to the special token list
        super().__init__(
            bos_token=_lowerCamelCase  ,      eos_token=_lowerCamelCase  ,      unk_token=_lowerCamelCase  ,      pad_token=_lowerCamelCase  ,      sep_token=_lowerCamelCase  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **_lowerCamelCase  ,      )

        UpperCamelCase_: int   				=    vocab_file

        UpperCamelCase_: List[Any]   				=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(_lowerCamelCase							)





       @property
       def       _a   ( self							):
        return self.sp_model.get_piece_size()





       def       _a   ( self							):
        UpperCamelCase_: Any   				=    {self.convert_ids_to_tokens(_lowerCamelCase							): i for i in range(self.vocab_size							)}
        vocab.update(self.added_tokens_encoder							)
        return vocab





       def __getstate__( self							):
        UpperCamelCase_: Dict   				=    self.__dict__.copy()
        UpperCamelCase_: Optional[Any]   				=    None
        return state





       def __setstate__( self  ,      _lowerCamelCase							):
        UpperCamelCase_: Optional[int]   				=    d

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

        UpperCamelCase_: Tuple   				=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(self.vocab_file							)





       def       _a   ( self  ,      _lowerCamelCase							):
        return self.sp_model.encode(_lowerCamelCase  ,      out_type=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
        return self.sp_model.piece_to_id(_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
        UpperCamelCase_: Union[str, Any]   				=    self.sp_model.IdToPiece(_lowerCamelCase							)
        return token





       def       _a   ( self  ,      _lowerCamelCase							):
        UpperCamelCase_: Optional[Any]   				=    []
        UpperCamelCase_: List[Any]   				=    ''
        for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
          out_string += self.sp_model.decode(_lowerCamelCase							) + token
          UpperCamelCase_: Tuple   				=    []
         else:
          current_sub_tokens.append(_lowerCamelCase							)
        out_string += self.sp_model.decode(_lowerCamelCase							)
        return out_string.strip()





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
        if not os.path.isdir(_lowerCamelCase							):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
         return
        UpperCamelCase_: Union[str, Any]   				=    os.path.join(
            _lowerCamelCase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

        if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							) and os.path.isfile(self.vocab_file							):
         copyfile(self.vocab_file  ,      _lowerCamelCase							)
        elif not os.path.isfile(self.vocab_file							):
         with open(_lowerCamelCase  ,      'wb'							) as fi:
          UpperCamelCase_: Tuple   				=    self.sp_model.serialized_model_proto()
          fi.write(_lowerCamelCase							)

        return (out_vocab_file,)







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @staticmethod
       @abstractmethod
       def       _a   ( _lowerCamelCase							):
         raise NotImplementedError()





       @abstractmethod
       def       _a   ( self							):
         raise NotImplementedError()