


"""simple docstring"""



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__snake_case       =       """\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"""

__snake_case       =       """\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"""

__snake_case       =       """\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"""







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Any					)      ->		Tuple:


    """simple docstring"""





    return float((preds == labels).mean()					)







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Tuple					)      ->		Optional[Any]:


    """simple docstring"""





    snake_case       : str    =       simple_accuracy(a_		,				a_					)
    snake_case       : Optional[Any]    =       float(fa_score(y_true=a_		,				y_pred=a_					)					)
    return {
        "accuracy": acc,
        "f1": fa,
    }







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    Union[str, Any]					)      ->		str:


    """simple docstring"""





    snake_case       : int    =       float(pearsonr(a_		,				a_					)[0]					)
    snake_case       : Dict    =       float(spearmanr(a_		,				a_					)[0]					)
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
    }







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







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

       '''simple docstring'''




       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
           raise KeyError(
               "You should supply a configuration name selected in "
               "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
               "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"		)
       return datasets.MetricInfo(
           description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(
               {
                   "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"		),
                   "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"		),
               }		)		,      codebase_urls=[]		,      reference_urls=[]		,      format="numpy"		,      )







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       if self.config_name == "cola":
           return {"matthews_correlation": matthews_corrcoef(__lowerCAmelCase		,      __lowerCAmelCase		)}
       elif self.config_name == "stsb":
           return pearson_and_spearman(__lowerCAmelCase		,      __lowerCAmelCase		)
       elif self.config_name in ["mrpc", "qqp"]:
           return acc_and_fa(__lowerCAmelCase		,      __lowerCAmelCase		)
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
           return {"accuracy": simple_accuracy(__lowerCAmelCase		,      __lowerCAmelCase		)}
       else:
           raise KeyError(
               "You should supply a configuration name selected in "
               "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
               "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"		)













'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






