





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

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


_snake_case  :   Dict						   =  logging.get_logger(__name__)

_snake_case  :   Union[str, Any]						   =  '▁'

_snake_case  :   Dict						   =  {'vocab_file': 'prophetnet.tokenizer'}

_snake_case  :   List[str]						   =  {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

_snake_case  :   List[str]						   =  {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

_snake_case  :   Optional[int]						   =  {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}

def   a_   (			lowerCAmelCase_				:      Tuple     ):
   __lowerCAmelCase							     =     collections.OrderedDict()
   with open(lowerCAmelCase_,     'r',     encoding='utf-8'     ) as reader:
      __lowerCAmelCase							     =     reader.readlines()
   for index, token in enumerate(lowerCAmelCase_     ):
      __lowerCAmelCase							     =     token.rstrip('\n'     )
      __lowerCAmelCase							     =     index
   return vocab




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			VOCAB_FILES_NAMES
  a_      =			PRETRAINED_VOCAB_FILES_MAP
  a_      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_      =			["""input_ids""", """attention_mask"""]






  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[int]="[SEP]" ,  lowerCAmelCase_			:		int="[SEP]" ,  lowerCAmelCase_			:		Tuple="[SEP]" ,  lowerCAmelCase_			:		Any="[UNK]" ,  lowerCAmelCase_			:		Tuple="[PAD]" ,  lowerCAmelCase_			:		List[str]="[CLS]" ,  lowerCAmelCase_			:		Optional[int]="[MASK]" ,  lowerCAmelCase_			:		Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						None:
     __lowerCAmelCase							     =     {} if sp_model_kwargs is None else sp_model_kwargs

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

     try:
        import sentencepiece as spm
     except ImportError:
        logger.warning(
            'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
            ' pip install sentencepiece'					)
        raise

     __lowerCAmelCase							     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(str(lowerCAmelCase_					)					)
     __lowerCAmelCase							     =     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'

     # put special tokens and [unused] tokens into the vocab
     __lowerCAmelCase							     =     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

     for i in range(1_0					):
        __lowerCAmelCase							     =     f"""[unused{i}]"""
        __lowerCAmelCase							     =     5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     __lowerCAmelCase							     =     1_2
     __lowerCAmelCase							     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     for k in self.fairseq_tokens_to_ids.keys():
        self.unique_no_split_tokens.append(lowerCAmelCase_					)






  def __getstate__(    self			:		int					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.__dict__.copy()
     __lowerCAmelCase							     =     None
     return state






  def __setstate__(    self			:		int ,  lowerCAmelCase_			:		List[Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     d
     try:
        import sentencepiece as spm
     except ImportError:
        logger.warning(
            'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
            ' pip install sentencepiece'					)
        raise

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

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






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None ,  lowerCAmelCase_			:		bool = False					)							->						List[int]:

     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCAmelCase_ ,  token_ids_a=lowerCAmelCase_ ,  already_has_special_tokens=lowerCAmelCase_					)

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






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:
     __lowerCAmelCase							     =     [self.sep_token_id]

     if token_ids_a is None:
        return len(token_ids_a + sep					) * [0]
     return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]






  @property
  def     lowercase		(    self			:		Optional[int]					)							->						int:
     return len(self.sp_model					) + self.fairseq_offset






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     __lowerCAmelCase							     =     {self.convert_ids_to_tokens(lowerCAmelCase_					): i for i in range(self.vocab_size					)}
     vocab.update(self.added_tokens_encoder					)
     return vocab






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		str					)							->						str:
     return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						int:
     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     __lowerCAmelCase							     =     self.sp_model.PieceToId(lowerCAmelCase_					)

     # 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     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     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     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     ''.join(lowerCAmelCase_					).replace(lowerCAmelCase_ ,  ' '					).strip()
     return out_string






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[str] = None					)							->						Tuple[str]:
     if not os.path.isdir(lowerCAmelCase_					):
        logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
        return
     __lowerCAmelCase							     =     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:
           __lowerCAmelCase							     =     self.sp_model.serialized_model_proto()
           fi.write(lowerCAmelCase_					)

     return (out_vocab_file,)






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:

     if token_ids_a is None:
        return token_ids_a + [self.sep_token_id]
     __lowerCAmelCase							     =     [self.sep_token_id]
     return token_ids_a + sep + token_ids_a + sep








import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def   a_   (			lowerCAmelCase_				:      Optional[Any]     ):
   # This defines a "chinese character" as anything in the CJK Unicode block:
   #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
   #
   # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
   # despite its name. The modern Korean Hangul alphabet is a different block,
   # as is Japanese Hiragana and Katakana. Those alphabets are used to write
   # space-separated words, so they are not treated specially and handled
   # like the all of the other languages.
   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   a_   (			lowerCAmelCase_				:      str     ):
   # word like '180' or '身高' or '神'
   for char in word:
      __lowerCAmelCase							     =     ord(lowerCAmelCase_     )
      if not _is_chinese_char(lowerCAmelCase_     ):
         return 0
   return 1

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     set()

   for token in tokens:
      __lowerCAmelCase							     =     len(lowerCAmelCase_     ) > 1 and is_chinese(lowerCAmelCase_     )
      if chinese_word:
         word_set.add(lowerCAmelCase_     )
   __lowerCAmelCase							     =     list(lowerCAmelCase_     )
   return word_list

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      set()     ):
   if not chinese_word_set:
      return bert_tokens
   __lowerCAmelCase							     =     max([len(lowerCAmelCase_     ) for w in chinese_word_set]     )

   __lowerCAmelCase							     =     bert_tokens
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0, len(lowerCAmelCase_     )
   while start < end:
      __lowerCAmelCase							     =     True
      if is_chinese(bert_word[start]     ):
         __lowerCAmelCase							     =     min(end - start,     lowerCAmelCase_     )
         for i in range(lowerCAmelCase_,     1,     -1     ):
            __lowerCAmelCase							     =     ''.join(bert_word[start : start + i]     )
            if whole_word in chinese_word_set:
               for j in range(start + 1,     start + i     ):
                  __lowerCAmelCase							     =     '##' + bert_word[j]
               __lowerCAmelCase							     =     start + i
               __lowerCAmelCase							     =     False
               break
      if single_word:
         start += 1
   return bert_word

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      LTP,     lowerCAmelCase_				:      BertTokenizer     ):
   __lowerCAmelCase							     =     []

   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     ltp_tokenizer.pipeline(lines[i : i + 100],     tasks=['cws']     ).cws
      __lowerCAmelCase							     =     [get_chinese_word(lowerCAmelCase_     ) for r in res]
      ltp_res.extend(lowerCAmelCase_     )
   assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

   __lowerCAmelCase							     =     []
   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     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							     =     []
   for input_ids, chinese_word in zip(lowerCAmelCase_,     lowerCAmelCase_     ):
      __lowerCAmelCase							     =     []
      for id in input_ids:
         __lowerCAmelCase							     =     bert_tokenizer._convert_id_to_token(lowerCAmelCase_     )
         input_tokens.append(lowerCAmelCase_     )
      __lowerCAmelCase							     =     add_sub_symbol(lowerCAmelCase_,     lowerCAmelCase_     )
      __lowerCAmelCase							     =     []
      # 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							     =     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   a_   (			lowerCAmelCase_				:      int     ):
   # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
   # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
   with open(args.file_name,     'r',     encoding='utf-8'     ) as f:
      __lowerCAmelCase							     =     f.readlines()
   __lowerCAmelCase							     =     [line.strip() for line in data if len(lowerCAmelCase_     ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   __lowerCAmelCase							     =     LTP(args.ltp     )  # faster in GPU device
   __lowerCAmelCase							     =     BertTokenizer.from_pretrained(args.bert     )

   __lowerCAmelCase							     =     prepare_ref(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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


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

     _snake_case  :   List[str]						   =  parser.parse_args()
     main(args)

