





from scipy.stats import spearmanr

import datasets


_snake_case  :   Union[str, Any]						   =  '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

_snake_case  :   List[str]						   =  '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

_snake_case  :   str						   =  R'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'



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

  """simple docstring"""


  def     lowercase		(    self			:		List[str]					)							->						Tuple:
     return datasets.MetricInfo(
         description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
             {
                 'predictions': datasets.Value('float'					),
                 'references': datasets.Value('float'					),
             }					) ,  reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] ,  )






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Union[str, Any]=False					)							->						str:
     __lowerCAmelCase							     =     spearmanr(lowerCAmelCase_ ,  lowerCAmelCase_					)
     if return_pvalue:
        return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
     else:
        return {"spearmanr": results[0]}








import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def   a_   (			lowerCAmelCase_				:      Optional[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 >= 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   a_   (			lowerCAmelCase_				:      str     ):
   # word like '180' or '身高' or '神'
   for char in word:
      __lowerCAmelCase							     =     ord(lowerCAmelCase_     )
      if not _is_chinese_char(lowerCAmelCase_     ):
         return 0
   return 1

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     set()

   for token in tokens:
      __lowerCAmelCase							     =     len(lowerCAmelCase_     ) > 1 and is_chinese(lowerCAmelCase_     )
      if chinese_word:
         word_set.add(lowerCAmelCase_     )
   __lowerCAmelCase							     =     list(lowerCAmelCase_     )
   return word_list

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      set()     ):
   if not chinese_word_set:
      return bert_tokens
   __lowerCAmelCase							     =     max([len(lowerCAmelCase_     ) for w in chinese_word_set]     )

   __lowerCAmelCase							     =     bert_tokens
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0, len(lowerCAmelCase_     )
   while start < end:
      __lowerCAmelCase							     =     True
      if is_chinese(bert_word[start]     ):
         __lowerCAmelCase							     =     min(end - start,     lowerCAmelCase_     )
         for i in range(lowerCAmelCase_,     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   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      LTP,     lowerCAmelCase_				:      BertTokenizer     ):
   __lowerCAmelCase							     =     []

   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     ltp_tokenizer.pipeline(lines[i : i + 100],     tasks=['cws']     ).cws
      __lowerCAmelCase							     =     [get_chinese_word(lowerCAmelCase_     ) for r in res]
      ltp_res.extend(lowerCAmelCase_     )
   assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

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

   __lowerCAmelCase							     =     []
   for input_ids, chinese_word in zip(lowerCAmelCase_,     lowerCAmelCase_     ):
      __lowerCAmelCase							     =     []
      for id in input_ids:
         __lowerCAmelCase							     =     bert_tokenizer._convert_id_to_token(lowerCAmelCase_     )
         input_tokens.append(lowerCAmelCase_     )
      __lowerCAmelCase							     =     add_sub_symbol(lowerCAmelCase_,     lowerCAmelCase_     )
      __lowerCAmelCase							     =     []
      # 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							     =     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   a_   (			lowerCAmelCase_				:      int     ):
   # 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(lowerCAmelCase_     ) > 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(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   int						   =  argparse.ArgumentParser(description='prepare_chinese_ref')
     parser.add_argument(
         '--file_name',
         required=False,
         type=str,
         default='./resources/chinese-demo.txt',
         help='file need process, same as training data in lm',
     )
     parser.add_argument(
         '--ltp',
         required=False,
         type=str,
         default='./resources/ltp',
         help='resources for LTP tokenizer, usually a path',
     )
     parser.add_argument(
         '--bert',
         required=False,
         type=str,
         default='./resources/robert',
         help='resources for Bert tokenizer',
     )
     parser.add_argument(
         '--save_path',
         required=False,
         type=str,
         default='./resources/ref.txt',
         help='path to save res',
     )

     _snake_case  :   List[str]						   =  parser.parse_args()
     main(args)

