import numpy as np
from sklearn.metrics import confusion_matrix
import pdb
import torch


def softmax(x, dim=1):
    """Compute softmax values for each sets of scores in x."""
    e_x = np.exp(x - np.max(x, axis=dim, keepdims=True))
    return e_x / e_x.sum(axis=dim, keepdims=True)


def mean_class_accuracy(scores, labels):
    pred = np.argmax(scores, axis=1)
    cf = confusion_matrix(labels, pred).astype(float)

    cls_cnt = cf.sum(axis=1)
    cls_hit = np.diag(cf)

    return np.mean(cls_hit/cls_cnt)


def top_k_acc(score, lb_set, k=3):
    idx = np.argsort(score)[-k:]
    return len(lb_set.intersection(idx)), len(lb_set)


def top_k_hit(score, lb_set, k=3):
    idx = np.argsort(score)[-k:]
    return len(lb_set.intersection(idx)) > 0, 1


def top_k_accuracy(scores, labels, k=(1,)):
    res = []
    for kk in k:
        hits = []
        for x, y in zip(scores, labels):
            y = [y] if isinstance(y, int) else y
            hits.append(top_k_hit(x, set(y), k=kk)[0])
        res.append(np.mean(hits))
    return res
