





'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):





   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 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple ):





   for char in word:
      lowercase_			:List[Any]    			= ord(__snake_case )
      if not _is_chinese_char(__snake_case ):
         return 0
   return 1
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Dict ):
   lowercase_			:List[Any]    			= set()

   for token in tokens:
      lowercase_			:Any    			= len(__snake_case ) > 1 and is_chinese(__snake_case )
      if chinese_word:
         word_set.add(__snake_case )
   lowercase_			:List[str]    			= list(__snake_case )
   return word_list
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Dict	,__lowerCamelCase					:					Optional[int] ):





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

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

   for i in range(0	,len(__snake_case )	,1_00 ):
      lowercase_			:Dict    			= ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
      lowercase_			:Any    			= [get_chinese_word(__snake_case ) for r in res]
      ltp_res.extend(__snake_case )
   assert len(__snake_case ) == len(__snake_case )

   lowercase_			:Optional[int]    			= []
   for i in range(0	,len(__snake_case )	,1_00 ):
      lowercase_			:Union[str, Any]    			= bert_tokenizer(lines[i : i + 1_00]	,add_special_tokens=__snake_case	,truncation=__snake_case	,max_length=5_12 )
      bert_res.extend(res["input_ids"] )
   assert len(__snake_case ) == len(__snake_case )

   lowercase_			:List[Any]    			= []
   for input_ids, chinese_word in zip(__snake_case	,__snake_case ):
      lowercase_			:str    			= []
      for id in input_ids:
         lowercase_			:Optional[Any]    			= bert_tokenizer._convert_id_to_token(__snake_case )
         input_tokens.append(__snake_case )
      lowercase_			:Optional[int]    			= add_sub_symbol(__snake_case	,__snake_case )
      lowercase_			:List[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] == "##":
            lowercase_			:Optional[Any]    			= 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 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):





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

   lowercase_			:Optional[Any]    			= prepare_ref(__snake_case	,__snake_case	,__snake_case )

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


if __name__ == "__main__":
 lowerCAmelCase					:     Optional[Any]												=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					:     Union[str, Any]												=parser.parse_args()
 main(args)




"""simple docstring"""





from sklearn.metrics import matthews_corrcoef

import datasets


_a														=		"""
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

_a														=		"""
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

_a														=		"""\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,     _KWARGS_DESCRIPTION	)
class     _UpperCAmelCase(    datasets.Metric	):



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int32'''),
							            '''references''': datasets.Value('''int32'''),
							        })				,		reference_urls=[
							        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
							    ]				,		)




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None)       ->       Dict:






							'''simple docstring'''
							return {
							    "matthews_correlation": float(matthews_corrcoef(__a				,		__a				,		sample_weight=__a)),
							}






