






'''simple docstring'''



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
      from scipy.stats import pearsonr, spearmanr
      from sklearn.metrics import fa_score, matthews_corrcoef


__UpperCAmelCase      :Union[str, Any]			    =       (
    'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
    'library. You can have a look at this example script for pointers: '
    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)




def      _a     (						_lowercase :     List[Any]      , _lowercase :     Tuple  ):





     '''simple docstring'''






     warnings.warn(_A      , _A  )
     requires_backends(_A      , '''sklearn'''  )
     return (preds == labels).mean()




def      _a     (						_lowercase :     List[Any]      , _lowercase :     int  ):





     '''simple docstring'''






     warnings.warn(_A      , _A  )
     requires_backends(_A      , '''sklearn'''  )
     __UpperCAmelCase     :			str  							=      simple_accuracy(_A      , _A  )
     __UpperCAmelCase     :			Optional[int]  							=      fa_score(y_true=_A      , y_pred=_A  )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }




def      _a     (						_lowercase :     Dict      , _lowercase :     str  ):





     '''simple docstring'''






     warnings.warn(_A      , _A  )
     requires_backends(_A      , '''sklearn'''  )
     __UpperCAmelCase     :			int  							=      pearsonr(_A      , _A  )[0]
     __UpperCAmelCase     :			Any  							=      spearmanr(_A      , _A  )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }




def      _a     (						_lowercase :     int      , _lowercase :     List[str]      , _lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     warnings.warn(_A      , _A  )
     requires_backends(_A      , '''sklearn'''  )
     assert len(_A  ) == len(_A  ), F'Predictions and labels have mismatched lengths {len(_A  )} and {len(_A  )}'
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(_A      , _A  )}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(_A      , _A  )}
     elif task_name == "mrpc":
          return acc_and_fa(_A      , _A  )
     elif task_name == "sts-b":
          return pearson_and_spearman(_A      , _A  )
     elif task_name == "qqp":
          return acc_and_fa(_A      , _A  )
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(_A      , _A  )}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(_A      , _A  )}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(_A      , _A  )}
     elif task_name == "rte":
          return {"acc": simple_accuracy(_A      , _A  )}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(_A      , _A  )}
     elif task_name == "hans":
          return {"acc": simple_accuracy(_A      , _A  )}
     else:
          raise KeyError(_A  )




def      _a     (						_lowercase :     Any      , _lowercase :     Any      , _lowercase :     Optional[Any]  ):





     '''simple docstring'''






     warnings.warn(_A      , _A  )
     requires_backends(_A      , '''sklearn'''  )
     if len(_A  ) != len(_A  ):
          raise ValueError(F'Predictions and labels have mismatched lengths {len(_A  )} and {len(_A  )}'  )
     if task_name == "xnli":
          return {"acc": simple_accuracy(_A      , _A  )}
     else:
          raise KeyError(_A  )



'''simple docstring'''



def      _a     (						_lowercase :     List[str]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      1
     __UpperCAmelCase     :			List[str]  							=      2
     while i * i <= n:
          __UpperCAmelCase     :			Optional[Any]  							=      0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      1
     __UpperCAmelCase     :			List[Any]  							=      1

     while True:
          i += 1
          t_num += i

          if count_divisors(_lowercase  ) > 500:
               break

     return t_num


if __name__ == "__main__":
      print(solution())