






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def 	lowerCamelCase   (		a_      )      ->     List[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 >= 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 	lowerCamelCase   (		a_      )      ->     Any:
							# word like '180' or '身高' or '神'
							for char in word:
														lowerCAmelCase_									=	ord(_UpperCamelCase      )
														if not _is_chinese_char(_UpperCamelCase      ):
																					return 0
							return 1
def 	lowerCamelCase   (		a_      )      ->     Any:
							lowerCAmelCase_									=	set()

							for token in tokens:
														lowerCAmelCase_									=	len(_UpperCamelCase      ) > 1 and is_chinese(_UpperCamelCase      )
														if chinese_word:
																					word_set.add(_UpperCamelCase      )
							lowerCAmelCase_									=	list(_UpperCamelCase      )
							return word_list
def 	lowerCamelCase   (		a_					,		a_      )      ->     Union[str, Any]:
							if not chinese_word_set:
														return bert_tokens
							lowerCAmelCase_									=	max([len(_UpperCamelCase      ) for w in chinese_word_set]      )

							lowerCAmelCase_									=	bert_tokens
							lowerCAmelCase_		,  lowerCAmelCase_									=	0, len(_UpperCamelCase      )
							while start < end:
														lowerCAmelCase_									=	True
														if is_chinese(bert_word[start]      ):
																					lowerCAmelCase_									=	min(end - start					,		_UpperCamelCase      )
																					for i in range(_UpperCamelCase					,		1					,		-1      ):
																												lowerCAmelCase_									=	''.join(bert_word[start : start + i]      )
																												if whole_word in chinese_word_set:
																																			for j in range(start + 1					,		start + i      ):
																																										lowerCAmelCase_									=	'##' + bert_word[j]
																																			lowerCAmelCase_									=	start + i
																																			lowerCAmelCase_									=	False
																																			break
														if single_word:
																					start += 1
							return bert_word
def 	lowerCamelCase   (		a_					,		a_					,		a_      )      ->     Dict:
							lowerCAmelCase_									=	[]

							for i in range(0					,		len(_UpperCamelCase      )					,		100      ):
														lowerCAmelCase_									=	ltp_tokenizer.seg(lines[i : i + 100]      )[0]
														lowerCAmelCase_									=	[get_chinese_word(_UpperCamelCase      ) for r in res]
														ltp_res.extend(_UpperCamelCase      )
							assert len(_UpperCamelCase      ) == len(_UpperCamelCase      )

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

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

							assert len(_UpperCamelCase      ) == len(_UpperCamelCase      )

							return ref_ids
def 	lowerCamelCase   (		a_      )      ->     Union[str, Any]:
							# 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_									=	f.readlines()
							lowerCAmelCase_									=	[line.strip() for line in data if len(_UpperCamelCase      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
							lowerCAmelCase_									=	LTP(args.ltp      )  # faster in GPU device
							lowerCAmelCase_									=	BertTokenizer.from_pretrained(args.bert      )

							lowerCAmelCase_									=	prepare_ref(_UpperCamelCase					,		_UpperCamelCase					,		_UpperCamelCase      )

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


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)








import math





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * power_factor





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






