






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

from ltp import LTP

from transformers import BertTokenizer





def 				lowercase__		(					lowerCamelCase       ):
				# 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 >= 0x20000 and cp <= 0x2A6DF)  #
				    or (cp >= 0x2A700 and cp <= 0x2B73F)  #
				    or (cp >= 0x2B740 and cp <= 0x2B81F)  #
				    or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
				    or (cp >= 0xF900 and cp <= 0xFAFF)
				    or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
				):  #
								return True

				return False





def 				lowercase__		(					lowerCamelCase       ):
				# word like '180' or '身高' or '神'
				for char in word:
								_SCREAMING_SNAKE_CASE   :		str       		=    ord(_snake_case       )
								if not _is_chinese_char(_snake_case       ):
												return 0
				return 1





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Any       		=    set()

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





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				if not chinese_word_set:
								return bert_tokens
				_SCREAMING_SNAKE_CASE   :		str       		=    max([len(_snake_case       ) for w in chinese_word_set]       )

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





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    []

				for i in range(0,						len(_snake_case       ),						100       ):
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    ltp_tokenizer.seg(lines[i : i + 100]       )[0]
								_SCREAMING_SNAKE_CASE   :		int       		=    [get_chinese_word(_snake_case       ) for r in res]
								ltp_res.extend(_snake_case       )
				assert len(_snake_case       ) == len(_snake_case       )

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

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

				assert len(_snake_case       ) == len(_snake_case       )

				return ref_ids





def 				lowercase__		(					lowerCamelCase       ):
				# 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:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    f.readlines()
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [line.strip() for line in data if len(_snake_case       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    LTP(args.ltp       )  # faster in GPU device
				_SCREAMING_SNAKE_CASE   :		Dict       		=    BertTokenizer.from_pretrained(args.bert       )

				_SCREAMING_SNAKE_CASE   :		int       		=    prepare_ref(_snake_case,						_snake_case,						_snake_case       )

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


if __name__ == "__main__":
			lowerCAmelCase__    						=			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')

			lowerCAmelCase__    						=			parser.parse_args()
			main(args)






from __future__ import annotations

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:str				)  ->     set[str]:
   _A  ,    _A      =       set(_snake_case				), [start]

   while stack:
      _A      =       stack.pop()
      explored.add(_snake_case				)
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]				):
         if adj not in explored:
            stack.append(_snake_case				)
   return explored


UpperCAmelCase_		 =					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, """A"""))



