"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:
  # 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 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  # word like '180' or '身高' or '神'
  for char in word:
    a__:     Any										=	ord(_SCREAMING_SNAKE_CASE   )
    if not _is_chinese_char(_SCREAMING_SNAKE_CASE   ):
      return 0
  return 1







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  a__:     Tuple										=	set()

  for token in tokens:
    a__:     List[Any]										=	len(_SCREAMING_SNAKE_CASE   ) > 1 and is_chinese(_SCREAMING_SNAKE_CASE   )
    if chinese_word:
      word_set.add(_SCREAMING_SNAKE_CASE   )
  a__:     List[str]										=	list(_SCREAMING_SNAKE_CASE   )
  return word_list







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  if not chinese_word_set:
    return bert_tokens
  a__:     Tuple										=	max([len(_SCREAMING_SNAKE_CASE   ) for w in chinese_word_set]   )

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     str										=	[]

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

  a__:     Dict										=	[]
  for i in range(0		,     len(_SCREAMING_SNAKE_CASE   )		,     100   ):
    a__:     int										=	bert_tokenizer(lines[i : i + 100]		,     add_special_tokens=_SCREAMING_SNAKE_CASE		,     truncation=_SCREAMING_SNAKE_CASE		,     max_length=512   )
    bert_res.extend(res['input_ids']   )
  assert len(_SCREAMING_SNAKE_CASE   ) == len(_SCREAMING_SNAKE_CASE   )

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

  assert len(_SCREAMING_SNAKE_CASE   ) == len(_SCREAMING_SNAKE_CASE   )

  return ref_ids







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Tuple:
  # 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__:     Optional[Any]										=	f.readlines()
  a__:     Tuple										=	[line.strip() for line in data if len(_SCREAMING_SNAKE_CASE   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  a__:     Union[str, Any]										=	LTP(args.ltp   )  # faster in GPU device
  a__:     Tuple										=	BertTokenizer.from_pretrained(args.bert   )

  a__:     List[str]										=	prepare_ref(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
 lowercase__					       =	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',
 )

 lowercase__					       =	parser.parse_args()
 main(args)



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  if height >= 1:
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_disk(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  print('moving disk from'		,     _SCREAMING_SNAKE_CASE		,     'to'		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Dict										=	int(input('Height of hanoi: '   ).strip()   )
  move_tower(_SCREAMING_SNAKE_CASE		,     'A'		,     'B'		,     'C'   )


if __name__ == "__main__":
 main()


