import argparse


def str2bool(v):
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("Boolean value expected.")


def get_args():
    """
    Returns a namedtuple with arguments extracted from the command line.
    :return: A namedtuple with arguments
    """
    parser = argparse.ArgumentParser(
        description="Welcome to the MLP course's Pytorch training and inference helper script"
    )

    parser.add_argument(
        "--batch_size",
        nargs="?",
        type=int,
        default=100,
        help="Batch_size for experiment",
    )
    parser.add_argument(
        "--continue_from_epoch",
        nargs="?",
        type=int,
        default=-1,
        help="Epoch you want to continue training from while restarting an experiment",
    )
    parser.add_argument(
        "--seed",
        nargs="?",
        type=int,
        default=7112018,
        help="Seed to use for random number generator for experiment",
    )
    parser.add_argument(
        "--image_num_channels",
        nargs="?",
        type=int,
        default=3,
        help="The channel dimensionality of our image-data",
    )
    parser.add_argument(
        "--image_height", nargs="?", type=int, default=32, help="Height of image data"
    )
    parser.add_argument(
        "--image_width", nargs="?", type=int, default=32, help="Width of image data"
    )
    parser.add_argument(
        "--num_stages",
        nargs="?",
        type=int,
        default=3,
        help="Number of convolutional stages in the network. A stage is considered a sequence of "
        "convolutional layers where the input volume remains the same in the spacial dimension and"
        " is always terminated by a dimensionality reduction stage",
    )
    parser.add_argument(
        "--num_blocks_per_stage",
        nargs="?",
        type=int,
        default=5,
        help="Number of convolutional blocks in each stage, not including the reduction stage."
        " A convolutional block is made up of two convolutional layers activated using the "
        " leaky-relu non-linearity",
    )
    parser.add_argument(
        "--num_filters",
        nargs="?",
        type=int,
        default=16,
        help="Number of convolutional filters per convolutional layer in the network (excluding "
        "dimensionality reduction layers)",
    )
    parser.add_argument(
        "--num_epochs",
        nargs="?",
        type=int,
        default=100,
        help="Total number of epochs for model training",
    )
    parser.add_argument(
        "--num_classes",
        nargs="?",
        type=int,
        default=100,
        help="Number of classes in the dataset",
    )
    parser.add_argument(
        "--experiment_name",
        nargs="?",
        type=str,
        default="exp_1",
        help="Experiment name - to be used for building the experiment folder",
    )
    parser.add_argument(
        "--use_gpu",
        nargs="?",
        type=str2bool,
        default=True,
        help="A flag indicating whether we will use GPU acceleration or not",
    )
    parser.add_argument(
        "--weight_decay_coefficient",
        nargs="?",
        type=float,
        default=0,
        help="Weight decay to use for Adam",
    )
    parser.add_argument(
        "--block_type",
        type=str,
        default="conv_block",
        help="Type of convolutional blocks to use in our network "
        "(This argument will be useful in running experiments to debug your network)",
    )
    args = parser.parse_args()
    print(args)
    return args