

"""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


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		"""▁"""

_lowerCAmelCase						=		{"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCAmelCase						=		{
    """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"""
        ),
    }
}

_lowerCAmelCase						=		{
    """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 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		["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    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    AddedToken(_A    ,lstrip=_A    ,rstrip=_A      ) if isinstance(_A    ,_A      ) else mask_token

      _lowerCAmelCase       :   int  =    {} 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    ,)

      _lowerCAmelCase       :   Tuple  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(str(_A      )      )
      _lowerCAmelCase       :   Optional[Any]  =    vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # Mimic fairseq token-to-id alignment for the first 4 token
      _lowerCAmelCase       :   int  =    {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      _lowerCAmelCase       :   List[str]  =    1

      _lowerCAmelCase       :   Union[str, Any]  =    len(self.sp_model      ) + self.fairseq_offset
      _lowerCAmelCase       :   Optional[int]  =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}

  def __getstate__(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.__dict__.copy()
      _lowerCAmelCase       :   List[Any]  =    None
      _lowerCAmelCase       :   str  =    self.sp_model.serialized_model_proto()
      return state

  def __setstate__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    d

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

      _lowerCAmelCase       :   Optional[Any]  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A = None    ,_A = False      ):
      '''simple docstring'''

      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      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    [self.sep_token_id]
      _lowerCAmelCase       :   List[Any]  =    [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      ):
      '''simple docstring'''

      return len(self.sp_model      ) + self.fairseq_offset + 1  # Add the <mask> token

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    {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      ):
      '''simple docstring'''

      return self.sp_model.encode(_A    ,out_type=_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
      _lowerCAmelCase       :   Any  =    self.sp_model.PieceToId(_A      )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    ''.join(_A      ).replace(_A    ,' '      ).strip()
      return out_string







  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      _lowerCAmelCase       :   Optional[Any]  =    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:
              _lowerCAmelCase       :   Tuple  =    self.sp_model.serialized_model_proto()
              fi.write(_A      )

      return (out_vocab_file,)








"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if (
        (cp >= 0X4E00 and cp <= 0X9FFF)
        or (cp >= 0X3400 and cp <= 0X4DBF)  #
        or (cp >= 0X20000 and cp <= 0X2A6DF)  #
        or (cp >= 0X2A700 and cp <= 0X2B73F)  #
        or (cp >= 0X2B740 and cp <= 0X2B81F)  #
        or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
        or (cp >= 0XF900 and cp <= 0XFAFF)
        or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
    ):  #
        return True

    return False

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for char in word:
        _lowerCAmelCase       :   Dict  =    ord(_lowerCamelCase   )
        if not _is_chinese_char(_lowerCamelCase   ):
            return 0
    return 1

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    set()

    for token in tokens:
        _lowerCAmelCase       :   Optional[int]  =    len(_lowerCamelCase   ) > 1 and is_chinese(_lowerCamelCase   )
        if chinese_word:
            word_set.add(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    list(_lowerCamelCase   )
    return word_list

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    _lowerCAmelCase       :   Optional[Any]  =    max([len(_lowerCamelCase   ) for w in chinese_word_set]   )

    _lowerCAmelCase       :   str  =    bert_tokens
    _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    0, len(_lowerCamelCase   )
    while start < end:
        _lowerCAmelCase       :   Dict  =    True
        if is_chinese(bert_word[start]   ):
            _lowerCAmelCase       :   str  =    min(end - start ,   _lowerCamelCase   )
            for i in range(_lowerCamelCase ,   1 ,   -1   ):
                _lowerCAmelCase       :   List[Any]  =    ''.join(bert_word[start : start + i]   )
                if whole_word in chinese_word_set:
                    for j in range(start + 1 ,   start + i   ):
                        _lowerCAmelCase       :   Tuple  =    '##' + bert_word[j]
                    _lowerCAmelCase       :   Optional[int]  =    start + i
                    _lowerCAmelCase       :   Any  =    False
                    break
        if single_word:
            start += 1
    return bert_word

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Tuple  =    ltp_tokenizer.seg(lines[i : i + 100]   )[0]
        _lowerCAmelCase       :   List[Any]  =    [get_chinese_word(_lowerCamelCase   ) for r in res]
        ltp_res.extend(_lowerCamelCase   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Dict  =    bert_tokenizer(lines[i : i + 100] ,   add_special_tokens=_lowerCamelCase ,   truncation=_lowerCamelCase ,   max_length=512   )
        bert_res.extend(res['input_ids']   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    _lowerCAmelCase       :   Union[str, Any]  =    []
    for input_ids, chinese_word in zip(_lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   Optional[int]  =    []
        for id in input_ids:
            _lowerCAmelCase       :   List[Any]  =    bert_tokenizer._convert_id_to_token(_lowerCamelCase   )
            input_tokens.append(_lowerCamelCase   )
        _lowerCAmelCase       :   Any  =    add_sub_symbol(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   List[str]  =    []
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(_lowerCamelCase   ):
            if token[:2] == "##":
                _lowerCAmelCase       :   List[Any]  =    token[2:]
                # save chinese tokens' pos
                if len(_lowerCamelCase   ) == 1 and _is_chinese_char(ord(_lowerCamelCase   )   ):
                    ref_id.append(_lowerCamelCase   )
        ref_ids.append(_lowerCamelCase   )

    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    return ref_ids

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    with open(args.file_name ,   'r' ,   encoding='utf-8'   ) as f:
        _lowerCAmelCase       :   int  =    f.readlines()
    _lowerCAmelCase       :   int  =    [line.strip() for line in data if len(_lowerCamelCase   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    _lowerCAmelCase       :   Dict  =    LTP(args.ltp   )  # faster in GPU device
    _lowerCAmelCase       :   Optional[int]  =    BertTokenizer.from_pretrained(args.bert   )

    _lowerCAmelCase       :   Optional[Any]  =    prepare_ref(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

    with open(args.save_path ,   'w' ,   encoding='utf-8'   ) as f:
        _lowerCAmelCase       :   Any  =    [json.dumps(_lowerCamelCase   ) + '\n' for ref in ref_ids]
        f.writelines(_lowerCamelCase   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
 )
 parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
 parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")

 _lowerCAmelCase						=		parser.parse_args()
 main(args)





