import os
from operator import itemgetter

import joblib
import numpy as np
from sklearn.utils import class_weight


def exists_or_make_path(p):
    if not os.path.exists(p):
        os.makedirs(p)


def get_custom_class_weights(client, server):
    client_class_weight = class_weight.compute_class_weight('balanced', np.unique(client), client)
    server_class_weight = class_weight.compute_class_weight('balanced', np.unique(server), server)
    return {
        "client": client_class_weight,
        "server": server_class_weight
    }


def get_custom_sample_weights(client, server):
    return class_weight.compute_sample_weight("balanced", np.vstack((client, server)).T)


def load_ordered_hyperband_results(path):
    results = joblib.load(path)
    return sorted(results, itemgetter("loss"))