





'''simple docstring'''



from __future__ import annotations

from math import pi




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  if (inductance, frequency, reactance).count(0       ) != 1:
    raise ValueError("One and only one argument must be 0"       )
  if inductance < 0:
    raise ValueError("Inductance cannot be negative"       )
  if frequency < 0:
    raise ValueError("Frequency cannot be negative"       )
  if reactance < 0:
    raise ValueError("Inductive reactance cannot be negative"       )
  if inductance == 0:
    return {"inductance": reactance / (2 * pi * frequency)}
  elif frequency == 0:
    return {"frequency": reactance / (2 * pi * inductance)}
  elif reactance == 0:
    return {"reactance": 2 * pi * frequency * inductance}
  else:
    raise ValueError("Exactly one argument must be 0"       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()









'''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


__magic_name__   :						List[str]    =			(
    """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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  return (preds == labels).mean()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__       )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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


