import argparse
from argparse import ArgumentParser

# Construct an argument parser
all_args = argparse.ArgumentParser()


def makeArguments(arguments: ArgumentParser) -> dict:
    """Training arguments to be passed to the model"""
    all_args.add_argument(
        "-b",
        "--Bayesian",
        action="store",
        dest="b",
        type=int,
        choices=range(1, 8),
        help="Bayesian model of size x",
    )
    all_args.add_argument(
        "-f",
        "--Frequentist",
        action="store",
        dest="f",
        type=int,
        choices=range(1, 8),
        help="Frequentist model of size x",
    )
    all_args.add_argument(
        "-E", "--EarlyStopping", action="store_true", help="Early Stopping criteria"
    )
    all_args.add_argument(
        "-e", "--EnergyBound", action="store_true", help="Energy Bound criteria"
    )
    all_args.add_argument(
        "-a", "--AccuracyBound", action="store_true", help="Accuracy Bound criteria"
    )
    all_args.add_argument(
        "-x",
        "--EfficiencyStopping",
        action="store_true",
        help="Efficiency Stopping criteria",
    )
    all_args.add_argument("-s", "--Save", action="store_true", help="Save model")
    all_args.add_argument(
        "--net_type", default="lenet", type=str, help="model = [lenet/AlexNet/3Conv3FC]"
    )
    all_args.add_argument(
        "-N",
        "--noise_type",
        default=None,
        type=str,
        help="noise = [Gaussian(m,s)/Raleigh(a,b)/Erlang(a,b)/Exponential(a)/Uniform(a,b)/Impulse(a)]",
    )
    all_args.add_argument(
        "--dataset",
        default="CIFAR10",
        type=str,
        help="dataset = [MNIST/CIFAR10/CIFAR100]",
    )
    return vars(all_args.parse_args())