import numpy as np


# ECE from https://towardsdatascience.com/expected-calibration-error-ece-a-step-by-step-visual-explanation-with-python-code-c3e9aa12937d
def ECE(samples, true_labels, M=5):
    # Uniform M bins
    bin_boundaries = np.linspace(0, 1, M + 1)
    bin_lowers = bin_boundaries[:-1]
    bin_uppers = bin_boundaries[1:]

    # Get max probability per sample i
    confidences = samples
    predicted_label = true_labels

    # get correct/false
    accuracies = predicted_label == true_labels

    ece = np.zeros(1)

    for bin_lower, bin_upper in zip(bin_lowers, bin_uppers):
        # bin sample
        in_bin = np.logical_and(
            confidences > bin_lower.item(), confidences < bin_upper.item()
        )
        prob_in_bin = in_bin.mean()

        if prob_in_bin > 0:
            accuracy_in_bin = accuracies[in_bin].mean()
            avg_confid = confidences[in_bin].mean()
            ece += np.abs(avg_confid - accuracy_in_bin) * prob_in_bin

    return ece[0]


# Maximum Calibration error - maximum of error per bin
def MCE(samples, true_labels, M=5):
    bin_boundaries = np.linspace(0, 1, M + 1)
    bin_lowers = bin_boundaries[:-1]
    bin_uppers = bin_boundaries[1:]

    # Get max probability per sample i
    confidences = samples
    predicted_label = true_labels

    # get correct/false
    accuracies = predicted_label == true_labels

    mces = []

    for bin_lower, bin_upper in zip(bin_lowers, bin_uppers):
        # bin sample
        in_bin = np.logical_and(
            confidences > bin_lower.item(), confidences < bin_upper.item()
        )
        prob_in_bin = in_bin.mean()

        if prob_in_bin > 0:
            accuracy_in_bin = accuracies[in_bin].mean()
            avg_confid = confidences[in_bin].mean()
            mces.append(np.abs(avg_confid - accuracy_in_bin))

    return max(mces)