

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Any:
	# 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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Optional[int]:
	# word like '180' or '身高' or '神'
	for char in word:
		__lowerCamelCase				:				int					       = ord(lowerCamelCase__			)
		if not _is_chinese_char(lowerCamelCase__			):
			return 0
	return 1






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     str:
	__lowerCamelCase				:				Any					       = set()

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






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     List[Any]:
	if not chinese_word_set:
		return bert_tokens
	__lowerCamelCase				:				List[str]					       = max([len(lowerCamelCase__			) for w in chinese_word_set]			)

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






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     str:
	__lowerCamelCase				:				int					       = []

	for i in range(0	, len(lowerCamelCase__			)	, 1_0_0			):
		__lowerCamelCase				:				int					       = ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
		__lowerCamelCase				:				Optional[int]					       = [get_chinese_word(lowerCamelCase__			) for r in res]
		ltp_res.extend(lowerCamelCase__			)
	assert len(lowerCamelCase__			) == len(lowerCamelCase__			)

	__lowerCamelCase				:				Tuple					       = []
	for i in range(0	, len(lowerCamelCase__			)	, 1_0_0			):
		__lowerCamelCase				:				List[str]					       = bert_tokenizer(lines[i : i + 1_0_0]	, add_special_tokens=lowerCamelCase__	, truncation=lowerCamelCase__	, max_length=5_1_2			)
		bert_res.extend(res['input_ids']			)
	assert len(lowerCamelCase__			) == len(lowerCamelCase__			)

	__lowerCamelCase				:				Tuple					       = []
	for input_ids, chinese_word in zip(lowerCamelCase__	, lowerCamelCase__			):
		__lowerCamelCase				:				List[Any]					       = []
		for id in input_ids:
			__lowerCamelCase				:				List[Any]					       = bert_tokenizer._convert_id_to_token(lowerCamelCase__			)
			input_tokens.append(lowerCamelCase__			)
		__lowerCamelCase				:				List[Any]					       = add_sub_symbol(lowerCamelCase__	, lowerCamelCase__			)
		__lowerCamelCase				:				Dict					       = []
		# 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] == "##":
				__lowerCamelCase				:				List[str]					       = 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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Dict:
	# 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:
		__lowerCamelCase				:				Any					       = f.readlines()
	__lowerCamelCase				:				str					       = [line.strip() for line in data if len(lowerCamelCase__			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	__lowerCamelCase				:				Any					       = LTP(args.ltp			)  # faster in GPU device
	__lowerCamelCase				:				List[str]					       = BertTokenizer.from_pretrained(args.bert			)

	__lowerCamelCase				:				List[str]					       = prepare_ref(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)

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


if __name__ == "__main__":
					a		=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		=parser.parse_args()
					main(args)







'''simple docstring'''






def 		_A     (    A__ = 10     ,				A__ = 22      ):






 """simple docstring"""



 __lowercase							  = range(1     ,				A__      )
 __lowercase							  = range(1     ,				A__      )
 return sum(
     1 for power in powers for base in bases if len(str(base**power      )      ) == power      )


if __name__ == "__main__":
 print(f'{solution(10, 22) = }')

