


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

from ltp import LTP

from transformers import BertTokenizer






def 		lowercase							(     _snake_case						:      List[Any]     )     ->Tuple:
	"""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 		lowercase							(     _snake_case						:      str     )     ->Optional[int]:
	"""simple docstring"""





	for char in word:
		__snake_case	:						Union[str, Any]						=				ord(__a     )
		if not _is_chinese_char(__a     ):
			return 0
	return 1






def 		lowercase							(     _snake_case						:      List[str]     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						Dict						=				set()

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






def 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      set()     )     ->List[str]:
	"""simple docstring"""





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

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






def 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      LTP     ,   _snake_case						:      BertTokenizer     )     ->Union[str, Any]:
	"""simple docstring"""





	__snake_case	:						int						=				[]

	for i in range(0     ,   len(__a     )     ,   100     ):
		__snake_case	:						Union[str, Any]						=				ltp_tokenizer.seg(lines[i : i + 100]     )[0]
		__snake_case	:						Optional[Any]						=				[get_chinese_word(__a     ) for r in res]
		ltp_res.extend(__a     )
	assert len(__a     ) == len(__a     )

	__snake_case	:						str						=				[]
	for i in range(0     ,   len(__a     )     ,   100     ):
		__snake_case	:						List[str]						=				bert_tokenizer(lines[i : i + 100]     ,   add_special_tokens=__a     ,   truncation=__a     ,   max_length=512     )
		bert_res.extend(res['''input_ids''']     )
	assert len(__a     ) == len(__a     )

	__snake_case	:						List[str]						=				[]
	for input_ids, chinese_word in zip(__a     ,   __a     ):
		__snake_case	:						int						=				[]
		for id in input_ids:
			__snake_case	:						Optional[int]						=				bert_tokenizer._convert_id_to_token(__a     )
			input_tokens.append(__a     )
		__snake_case	:						List[str]						=				add_sub_symbol(__a     ,   __a     )
		__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(__a     ):
			if token[:2] == "##":
				__snake_case	:						str						=				token[2:]
				# save chinese tokens' pos
				if len(__a     ) == 1 and _is_chinese_char(ord(__a     )     ):
					ref_id.append(__a     )
		ref_ids.append(__a     )

	assert len(__a     ) == len(__a     )

	return ref_ids






def 		lowercase							(     _snake_case						:      Optional[Any]     )     ->Any:
	"""simple docstring"""





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

	__snake_case	:						int						=				prepare_ref(__a     ,   __a     ,   __a     )

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Dict									=				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""")

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







'''simple docstring'''







def 					UpperCAmelCase_		(__a      :    list ,	__a      :    list ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   len(__a )
   _a				:	int			=   [[0] * n for i in range(__a )]
   for i in range(__a ):
      _a				:	Tuple			=   y_points[i]

   for i in range(2 ,	__a ):
      for j in range(__a ,	__a ):
         _a				:	Tuple			=   (
             (xa - x_points[j - i + 1]) * q[j][i - 1]
             - (xa - x_points[j]) * q[j - 1][i - 1]
         ) / (x_points[j] - x_points[j - i + 1])

   return [q[n - 1][n - 1], q]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



