



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 					__snake_case     (      _UpperCAmelCase    ):
    # 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 >= 0X4_e00 and cp <= 0X9_fff)
        or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
        or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
        or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
        or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
        or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
        or (cp >= 0Xf_900 and cp <= 0Xf_aff)
        or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
    ):  #
        return True

    return False





def 					__snake_case     (      _UpperCAmelCase    ):
    # 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 					__snake_case     (      _UpperCAmelCase    ):
    __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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    []

    for i in range(0       ,			len(_UpperCAmelCase    )       ,			100    ):
        __a							=    ltp_tokenizer.seg(lines[i : i + 100]    )[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    )       ,			100    ):
        __a							=    bert_tokenizer(lines[i : i + 100]       ,			add_special_tokens=_UpperCAmelCase       ,			truncation=_UpperCAmelCase       ,			max_length=512    )
        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 					__snake_case     (      _UpperCAmelCase    ):
    # 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__":
 __snake_case						: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''')

 __snake_case						:int						=					parser.parse_args()
 main(args)










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


__snake_case						:Optional[Any]						=					logging.get_logger(__name__)


__snake_case						:List[Any]						=					'''▁'''

__snake_case						:List[Any]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Tuple						=					{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__snake_case						:List[Any]						=					{
    '''google/reformer-crime-and-punishment''': 52_4288,
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						VOCAB_FILES_NAMES
       UpperCamelCase__  :   str						 =						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :   Dict						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :   Optional[int]						 =						['''input_ids''', '''attention_mask''']



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Any=[]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					List[Any]					,		):


           '''simple docstring'''

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

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



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.sp_model.get_piece_size()



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



       def __getstate__(		self							:					Dict):


           '''simple docstring'''

           __a							=    self.__dict__.copy()
           __a							=    None
           return state



       def __setstate__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __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 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
               __a							=    self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           return token



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    []
           __a							=    ''''''
           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(__SCREAMING_SNAKE_CASE) + token
                   __a							=    []
               else:
                   current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a							=    os.path.join(
               __SCREAMING_SNAKE_CASE					,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

           return (out_vocab_file,)





