from joblib import Parallel, delayed

from evaluation.metrics import *


def evaluation_metrics(y, y_):
    if len(y.shape) == len(y_.shape) == 1 and len(np.unique(y)) > 2:  # single-label
        raise NotImplementedError()  # return f1_score(y,y_,average='macro'), f1_score(y,y_,average='micro')
    else:  # the metrics I implemented assume multiclass multilabel classification as binary classifiers
        return macroF1(y, y_), microF1(y, y_), macroK(y, y_), microK(y, y_)
        # return macroF1(y, y_), microF1(y, y_), macroK(y, y_), macroAcc(y, y_)


def evaluate(ly_true, ly_pred, metrics=evaluation_metrics, n_jobs=-1):
    if n_jobs == 1:
        return {lang: metrics(ly_true[lang], ly_pred[lang]) for lang in ly_true.keys()}
    else:
        langs = list(ly_true.keys())
        evals = Parallel(n_jobs=n_jobs)(
            delayed(metrics)(ly_true[lang], ly_pred[lang]) for lang in langs
        )
        return {lang: evals[i] for i, lang in enumerate(langs)}


def log_eval(l_eval, phase="training", verbose=True):
    if verbose:
        print(f"\n[Results {phase}]")
    metrics = []
    for lang in l_eval.keys():
        macrof1, microf1, macrok, microk = l_eval[lang]
        metrics.append([macrof1, microf1, macrok, microk])
        if phase != "validation":
            print(f"Lang {lang}: macro-F1 = {macrof1:.3f} micro-F1 = {microf1:.3f}")
    averages = np.mean(np.array(metrics), axis=0)
    if verbose:
        print(
            "Averages: MF1, mF1, MK, mK",
            np.round(averages, 3),
            "\n",
        )
    return averages