





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->int:
       # 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 >= 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 	UpperCamelCase__(       UpperCamelCase__			:       str  )->int:
       # word like '180' or '身高' or '神'
       for char in word:
              A__											=						ord(UpperCamelCase__  )
              if not _is_chinese_char(UpperCamelCase__  ):
                     return 0
       return 1


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str]  )->Union[str, Any]:
       A__											=						set()

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


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       set()  )->List[Any]:
       if not chinese_word_set:
              return bert_tokens
       A__											=						max([len(UpperCamelCase__  ) for w in chinese_word_set]  )

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


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       LTP ,     UpperCamelCase__			:       BertTokenizer  )->str:
       A__											=						[]

       for i in range(0 ,     len(UpperCamelCase__  ) ,     1_00  ):
              A__											=						ltp_tokenizer.seg(lines[i : i + 1_00]  )[0]
              A__											=						[get_chinese_word(UpperCamelCase__  ) for r in res]
              ltp_res.extend(UpperCamelCase__  )
       assert len(UpperCamelCase__  ) == len(UpperCamelCase__  )

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

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

       assert len(UpperCamelCase__  ) == len(UpperCamelCase__  )

       return ref_ids


def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->List[Any]:
       # 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:
              A__											=						f.readlines()
       A__											=						[line.strip() for line in data if len(UpperCamelCase__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       A__											=						LTP(args.ltp  )  # faster in GPU device
       A__											=						BertTokenizer.from_pretrained(args.bert  )

       A__											=						prepare_ref(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

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


if __name__ == "__main__":
       a__:      List[Any]									=		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')

       a__:      Tuple									=		parser.parse_args()
       main(args)












import os
import re
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__:      Union[str, Any]									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{'vocab_file': 'spiece.model'}

a__:      Tuple									=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

a__:      Any									=		{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase="<unk>",__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="<pad>",__lowerCamelCase="[SEP]",__lowerCamelCase="[MASK]",__lowerCamelCase="[CLS]",__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else bos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else eos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else unk_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else pad_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else cls_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

        A__											=						{} if sp_model_kwargs is None else sp_model_kwargs

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

        A__											=						vocab_file

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        A__											=						{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 ):
        A__											=						self.__dict__.copy()
        A__											=						None
        return state



 def __setstate__(							self,__lowerCamelCase ):
        A__											=						d

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

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



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



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.piece_to_id(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.sp_model.IdToPiece(__lowerCamelCase )
        return token



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[]
        A__											=						''''''
        A__											=						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(__lowerCamelCase ) + token
                      A__											=						True
                      A__											=						[]
               else:
                      current_sub_tokens.append(__lowerCamelCase )
                      A__											=						False
        out_string += self.sp_model.decode(__lowerCamelCase )
        return out_string.strip()



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        A__											=						kwargs.pop('''use_source_tokenizer''',__lowerCamelCase )

        A__											=						self.convert_ids_to_tokens(__lowerCamelCase,skip_special_tokens=__lowerCamelCase )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A__											=						[]
        A__											=						[]
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )
                             A__											=						[]
                      sub_texts.append(__lowerCamelCase )
               else:
                      current_sub_text.append(__lowerCamelCase )
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               A__											=						re.sub(r''' (\[(MASK|SEP)\])''',r'''\1''',''' '''.join(__lowerCamelCase ) )
        else:
               A__											=						''''''.join(__lowerCamelCase )

        A__											=						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               A__											=						self.clean_up_tokenization(__lowerCamelCase )
               return clean_text
        else:
               return text



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not os.path.isdir(__lowerCamelCase ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
               return
        A__											=						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:
                      A__											=						self.sp_model.serialized_model_proto()
                      fi.write(__lowerCamelCase )

        return (out_vocab_file,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A__											=						[self.cls_token_id]
        A__											=						[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = False ):
        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 [1] + ([0] * len(__lowerCamelCase )) + [1]
        return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[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 ) * [0] + len(token_ids_a + sep ) * [1]





