





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


_snake_case    :       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_       (					lowerCAmelCase_ :     Optional[int],      lowerCAmelCase_ :     Union[str, Any]      ):
   warnings.warn(lowerCAmelCase_,      lowerCAmelCase_      )
   requires_backends(lowerCAmelCase_,      'sklearn'      )
   return (preds == labels).mean()

def     a_       (					lowerCAmelCase_ :     str,      lowerCAmelCase_ :     List[str]      ):
   warnings.warn(lowerCAmelCase_,      lowerCAmelCase_      )
   requires_backends(lowerCAmelCase_,      'sklearn'      )
   __lowerCAmelCase				      =      simple_accuracy(lowerCAmelCase_,      lowerCAmelCase_      )
   __lowerCAmelCase				      =      fa_score(y_true=lowerCAmelCase_,      y_pred=lowerCAmelCase_      )
   return {
       "acc": acc,
       "f1": fa,
       "acc_and_f1": (acc + fa) / 2,
   }

def     a_       (					lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     Optional[int]      ):
   warnings.warn(lowerCAmelCase_,      lowerCAmelCase_      )
   requires_backends(lowerCAmelCase_,      'sklearn'      )
   __lowerCAmelCase				      =      pearsonr(lowerCAmelCase_,      lowerCAmelCase_      )[0]
   __lowerCAmelCase				      =      spearmanr(lowerCAmelCase_,      lowerCAmelCase_      )[0]
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
       "corr": (pearson_corr + spearman_corr) / 2,
   }

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




def     a_       (					lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     Union[str, Any]      ):
   warnings.warn(lowerCAmelCase_,      lowerCAmelCase_      )
   requires_backends(lowerCAmelCase_,      'sklearn'      )
   if len(lowerCAmelCase_      ) != len(lowerCAmelCase_      ):
      raise ValueError(F"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_      )} and {len(lowerCAmelCase_      )}"""      )
   if task_name == "xnli":
      return {"acc": simple_accuracy(lowerCAmelCase_,      lowerCAmelCase_      )}
   else:
      raise KeyError(lowerCAmelCase_      )








'''simple docstring'''

import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowercase__		:     str			=							'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


lowercase__		:     str			=							'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


lowercase__		:     Dict			=							R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION					)
class 							__lowerCAmelCase	(       datasets.Metric					):






       """simple docstring"""





       def snake_case__		(				self  :						Dict	)       ->	Any:

            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
                    {
                        '''predictions''': datasets.Value('''string'''	),
                        '''references''': datasets.Value('''string'''	),
                    }	)			,					homepage='''https://github.com/hendrycks/math'''			,					codebase_urls=['''https://github.com/hendrycks/math''']			,					)







       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			0.0
            for i, j in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                 n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase__			,					lowerCAmelCase__	) else 0.0
            _UpperCamelCase							       =			n_correct / len(lowerCAmelCase__	)
            return {
                "accuracy": accuracy,
            }



