"""Parsers for pretraining embedding and training agent."""
import argparse


def get_args(description: str = "YouTube") -> argparse.Namespace:
    """
    Retrieve parsed arguments as a Namespace.

    Parameters
    ----------
    description : str
        Description given to ArgumentParser.

    Returns
    -------
    args : argparse.Namespace
        Namespace with arguments specified.

    """
    parser = argparse.ArgumentParser(description)
    # Hyperparameters for pretraining embedding
    # TODO Find correct lambda value from paper
    parser.add_argument(
        "--cmc-lambda",
        action="store",
        dest="CMC_LAMBDA",
        default=1,
        type=float,
        help="Weight for combining TDC and CMC loss. Defaults to 1.",
    )
    parser.add_argument(
        "--lr",
        action="store",
        dest="LR",
        default=1e-4,
        type=float,
        help="Learning rate for Adam optimizer. Defaults to 1e-4.",
    )
    parser.add_argument(
        "--batch-size",
        action="store",
        dest="BATCH_SIZE",
        default=32,
        type=int,
        help="Batch size for pretraining embedding. Defaults to 32.",
    )
    parser.add_argument(
        "--nb-steps",
        action="store",
        dest="NB_STEPS",
        default=200000,
        type=int,
        help="Number of training steps for embedding. Defaults to 200000.",
    )

    # Misc. arguments for pretraining embedding
    parser.add_argument(
        "--save-interval",
        action="store",
        dest="SAVE_INTERVAL",
        default=10000,
        type=int,
        help="Interval for saving models during pretraining. Defaults to 10000.",
    )
    parser.add_argument(
        "--tsne-interval",
        action="store",
        dest="TSNE_INTERVAL",
        default=1000,
        type=int,
        help="Interval for plotting t-SNE during pretraining. Defaults to 1000.",
    )

    # Hyperparameters for training DQN agent
    parser.add_argument(
        "--ckpt-freq",
        action="store",
        dest="CKPT_FREQ",
        default=16,
        type=int,
        help="Frequency of checkpoints (N) selected from embedding. Defaults to 16.",
    )
    parser.add_argument(
        "--ckpt-horizon",
        action="store",
        dest="CKPT_HORIZON",
        default=1,
        type=int,
        help=" Horizon(Δt) for checkpoints. Defaults to 1.",
    )
    parser.add_argument(
        "--imitation-cutoff",
        action="store",
        dest="IMITATION_CUTOFF",
        default=0.5,
        type=float,
        help="Cutoff (α) for giving imitation reward. Defaults to 0.5.",
    )

    args = parser.parse_args()

    return args

"""Parsers for pretraining embedding and training agent."""
import argparse


def get_args(description: str = "YouTube") -> argparse.Namespace:
    """
    Retrieve parsed arguments as a Namespace.

    Parameters
    ----------
    description : str
        Description given to ArgumentParser.

    Returns
    -------
    args : argparse.Namespace
        Namespace with arguments specified.

    """
    parser = argparse.ArgumentParser(description)
    # Hyperparameters for pretraining embedding
    # TODO Find correct lambda value from paper
    parser.add_argument(
        "--cmc-lambda",
        action="store",
        dest="CMC_LAMBDA",
        default=1,
        type=float,
        help="Weight for combining TDC and CMC loss. Defaults to 1.",
    )
    parser.add_argument(
        "--lr",
        action="store",
        dest="LR",
        default=1e-4,
        type=float,
        help="Learning rate for Adam optimizer. Defaults to 1e-4.",
    )
    parser.add_argument(
        "--batch-size",
        action="store",
        dest="BATCH_SIZE",
        default=32,
        type=int,
        help="Batch size for pretraining embedding. Defaults to 32.",
    )
    parser.add_argument(
        "--nb-steps",
        action="store",
        dest="NB_STEPS",
        default=200000,
        type=int,
        help="Number of training steps for embedding. Defaults to 200000.",
    )

    # Misc. arguments for pretraining embedding
    parser.add_argument(
        "--save-interval",
        action="store",
        dest="SAVE_INTERVAL",
        default=10000,
        type=int,
        help="Interval for saving models during pretraining. Defaults to 10000.",
    )
    parser.add_argument(
        "--tsne-interval",
        action="store",
        dest="TSNE_INTERVAL",
        default=1000,
        type=int,
        help="Interval for plotting t-SNE during pretraining. Defaults to 1000.",
    )

    # Hyperparameters for training DQN agent
    parser.add_argument(
        "--ckpt-freq",
        action="store",
        dest="CKPT_FREQ",
        default=16,
        type=int,
        help="Frequency of checkpoints (N) selected from embedding. Defaults to 16.",
    )
    parser.add_argument(
        "--ckpt-horizon",
        action="store",
        dest="CKPT_HORIZON",
        default=1,
        type=int,
        help=" Horizon(Δt) for checkpoints. Defaults to 1.",
    )
    parser.add_argument(
        "--imitation-cutoff",
        action="store",
        dest="IMITATION_CUTOFF",
        default=0.5,
        type=float,
        help="Cutoff (α) for giving imitation reward. Defaults to 0.5.",
    )

    args = parser.parse_args()

    return args

Parsers for pretraining embedding and training agent. Retrieve parsed arguments as a Namespace.      Parameters     ----------     description : str         Description given to ArgumentParser.      Returns     -------     args : argparse.Namespace         Namespace with arguments specified. # Hyperparameters for pretraining embedding # TODO Find correct lambda value from paper # Misc. arguments for pretraining embedding # Hyperparameters for training DQN agent