
'''simple docstring'''







import os
import unicodedata
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 SPIECE_UNDERLINE, logging


__lowerCAmelCase   =       logging.get_logger(__name__)

__lowerCAmelCase   =       {"""vocab_file""": """spiece.model"""}

__lowerCAmelCase   =       {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}







class UpperCAmelCase__      (					lowercase__      ):





  """simple docstring"""

  def __init__(						self :	Dict				,_a :	Any				,_a :	Union[str, Any]=False				,_a :	List[Any]=True				,_a :	Any=False				,_a :	List[str]="<s>"				,_a :	Optional[Any]="</s>"				,_a :	Optional[int]="<unk>"				,_a :	Optional[int]="<sep>"				,_a :	Optional[int]="<pad>"				,_a :	Tuple="<cls>"				,_a :	List[str]="<mask>"				,_a :	str=["<eop>", "<eod>"]				,_a :	Any = None				,**_a :	Tuple				,):


     '''simple docstring'''






     _a							:      Any     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else mask_token

     _a							:      Tuple     =  {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=__a				,remove_space=__a				,keep_accents=__a				,bos_token=__a				,eos_token=__a				,unk_token=__a				,sep_token=__a				,pad_token=__a				,cls_token=__a				,mask_token=__a				,additional_special_tokens=__a				,sp_model_kwargs=self.sp_model_kwargs				,**__a				,)

     _a							:      Any     =  3

     _a							:      Any     =  do_lower_case
     _a							:      Optional[int]     =  remove_space
     _a							:      List[str]     =  keep_accents
     _a							:      str     =  vocab_file

     _a							:      List[str]     =  spm.SentencePieceProcessor(**self.sp_model_kwargs			)
     self.sp_model.Load(__a			)

     try:
        import jieba
     except ModuleNotFoundError as error:
        raise error.__class__(
            'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
            'See https://pypi.org/project/jieba/ for installation.'			)
     _a							:      List[Any]     =  jieba
     _a							:      Optional[int]     =  str.maketrans(' \n'				,'\u2582\u2583'			)


  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def     __lowercase						(						self :	Union[str, Any]			):


     '''simple docstring'''






     return len(self.sp_model			)


  def     __lowercase						(						self :	Any			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  {self.convert_ids_to_tokens(__a			): i for i in range(self.vocab_size			)}
     vocab.update(self.added_tokens_encoder			)
     return vocab


  def __getstate__(						self :	Union[str, Any]			):


     '''simple docstring'''






     _a							:      Optional[Any]     =  self.__dict__.copy()
     _a							:      Any     =  None
     return state


  def __setstate__(						self :	str				,_a :	Optional[Any]			):


     '''simple docstring'''






     _a							:      str     =  d

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

     _a							:      Dict     =  spm.SentencePieceProcessor(**self.sp_model_kwargs			)
     self.sp_model.Load(self.vocab_file			)


  def     __lowercase						(						self :	Any				,_a :	Any			):


     '''simple docstring'''






     if self.remove_space:
        _a							:      Any     =  ' '.join(inputs.strip().split()			)
     else:
        _a							:      int     =  inputs
     _a							:      List[str]     =  outputs.replace('``'				,'"'			).replace('\'\''				,'"'			)

     if not self.keep_accents:
        _a							:      Optional[int]     =  unicodedata.normalize('NFKD'				,__a			)
        _a							:      Dict     =  ''.join([c for c in outputs if not unicodedata.combining(__a			)]			)
     if self.do_lower_case:
        _a							:      Union[str, Any]     =  outputs.lower()

     return outputs


  def     __lowercase						(						self :	int				,_a :	Tuple			):


     '''simple docstring'''






     _a							:      int     =  self.preprocess_text(__a			)
     _a							:      Any     =  self.sp_model.encode(__a				,out_type=__a			)
     _a							:      Dict     =  []
     for piece in pieces:
        if len(__a			) > 1 and piece[-1] == str(','			) and piece[-2].isdigit():
           _a							:      List[Any]     =  self.sp_model.EncodeAsPieces(piece[:-1].replace(__a				,''			)			)
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
              if len(cur_pieces[0]			) == 1:
                 _a							:      Any     =  cur_pieces[1:]
              else:
                 _a							:      List[str]     =  cur_pieces[0][1:]
           cur_pieces.append(piece[-1]			)
           new_pieces.extend(__a			)
        else:
           new_pieces.append(__a			)

     return new_pieces


  def     __lowercase						(						self :	Dict				,_a :	Union[str, Any]			):


     '''simple docstring'''






     return self.sp_model.PieceToId(__a			)


  def     __lowercase						(						self :	Optional[int]				,_a :	List[str]			):


     '''simple docstring'''






     return self.sp_model.IdToPiece(__a			)


  def     __lowercase						(						self :	str				,_a :	Dict			):


     '''simple docstring'''






     _a							:      Dict     =  ''.join(__a			).replace(__a				,' '			).strip()
     return out_string


  def     __lowercase						(						self :	Dict				,_a :	str				,_a :	str = None			):


     '''simple docstring'''






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


  def     __lowercase						(						self :	str				,_a :	List[str]				,_a :	Optional[Any] = None				,_a :	List[str] = 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 not None:
        return ([0] * len(__a			)) + [1] + ([0] * len(__a			)) + [1, 1]
     return ([0] * len(__a			)) + [1, 1]


  def     __lowercase						(						self :	Optional[Any]				,_a :	int				,_a :	Any = None			):


     '''simple docstring'''






     _a							:      Dict     =  [self.sep_token_id]
     _a							:      Optional[int]     =  [2]

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


  def     __lowercase						(						self :	Optional[int]				,_a :	Union[str, Any]				,_a :	Any = None			):


     '''simple docstring'''






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

     return (out_vocab_file,)


  def     __lowercase						(						self :	Tuple				,*_a :	List[str]				,**_a :	Any			):


     '''simple docstring'''






     _a							:      Tuple     =  super()._decode(*__a				,**__a			)
     _a							:      int     =  text.replace(' '				,''			).replace('\u2582'				,' '			).replace('\u2583'				,'\n'			)
     return text





"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if (
					    (cp >= 0x4E00 and cp <= 0x9FFF)
					    or (cp >= 0x3400 and cp <= 0x4DBF)  #
					    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
					    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
					    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
					    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
					    or (cp >= 0xF900 and cp <= 0xFAFF)
					    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
					):  #
										return True

					return False



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					if not chinese_word_set:
										return bert_tokens
					_UpperCamelCase									=    max([len(__snake_case      ) for w in chinese_word_set]      )

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_UpperCamelCase									=    [get_chinese_word(__snake_case      ) for r in res]
										ltp_res.extend(__snake_case      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    bert_tokenizer(lines[i : i + 1_00],	add_special_tokens=__snake_case,	truncation=__snake_case,	max_length=5_12      )
										bert_res.extend(res['''input_ids''']      )
					assert len(__snake_case      ) == len(__snake_case      )

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

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





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

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

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


if __name__ == "__main__":
				_a								=						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""",
				)

				_a								=						parser.parse_args()
				main(args)






