





'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def    _a    (							lowerCAmelCase_	):





      """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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      for char in word:
            _snake_case		:	Tuple					     =     ord(lowerCAmelCase_	)
            if not _is_chinese_char(lowerCAmelCase_	):
                  return 0
      return 1




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     set()

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if not chinese_word_set:
            return bert_tokens
      _snake_case		:	str					     =     max([len(lowerCAmelCase_	) for w in chinese_word_set]	)

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     []

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

      _snake_case		:	Dict					     =     []
      for i in range(0			,      len(lowerCAmelCase_	)			,      100	):
            _snake_case		:	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_	)

      _snake_case		:	Optional[Any]					     =     []
      for input_ids, chinese_word in zip(lowerCAmelCase_			,      lowerCAmelCase_	):
            _snake_case		:	Dict					     =     []
            for id in input_ids:
                  _snake_case		:	Optional[int]					     =     bert_tokenizer._convert_id_to_token(lowerCAmelCase_	)
                  input_tokens.append(lowerCAmelCase_	)
            _snake_case		:	Dict					     =     add_sub_symbol(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Tuple					     =     []
            # 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] == "##":
                        _snake_case		:	Tuple					     =     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_	):





      """simple docstring"""



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

      _snake_case		:	Tuple					     =     prepare_ref(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

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


if __name__ == "__main__":
   UpperCAmelCase					:		Union[str, Any]		=							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',
   )

   UpperCAmelCase					:		Union[str, Any]		=							parser.parse_args()
   main(args)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


