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


__lowercase										= (
    '''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 lowerCamelCase  (       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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       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 lowerCamelCase  (       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     )






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    if (stress, tangential_force, area).count(0     ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''     )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''     )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''     )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''     )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





