import argparse


def add_model_config_args(parser: argparse.ArgumentParser):
    """Model arguments"""

    group = parser.add_argument_group("model", "model configuration")
    group.add_argument("--model-config", type=str, help="model configuration file")
    group.add_argument("--vocab", type=str, default=None, help="model vocabulary file")
    return parser


def add_training_args(parser: argparse.ArgumentParser):
    """Training arguments."""

    group = parser.add_argument_group("train", "training configurations")
    group.add_argument("--platform-config", type=str, default="platform_config.json", help="Path to platform config")
    group.add_argument("--dataset", type=str, default="dataset.json", help="Path to dataset")
    group.add_argument("--val-dataset", type=str, default="dataset.json", help="Path to val dataset")
    group.add_argument(
        "--load",
        type=str,
        default=None,
        help="Path to a directory containing a model checkpoint.",
    )

    group.add_argument(
        "--load-grad",
        action="store_true",
        default=False,
        help="Load the gradient states",
    )

    group.add_argument(
        "--load-start-step",
        action="store_true",
        default=False,
        help="Load the step state from checkpoints",
    )

    group.add_argument(
        "--save",
        type=str,
        default=None,
        help="Output directory to save checkpoints to.",
    )
    group.add_argument(
        "--save-name",
        type=str,
        default=None,
        help="Output filename to save checkpoints to.",
    )
    group.add_argument(
        "--save-model",
        type=str,
        default=None,
        help="Output directory to save model to.",
    )

    group.add_argument(
        "--tensorboard",
        type=str,
        default=None,
        help="tensorboard directory",
    )

    group.add_argument("--inspect-iters", type=int, default=1000, help="number of inspecting")
    group.add_argument("--batch-size", type=int, default=32, help="Data Loader batch size")
    group.add_argument("--clip-grad", type=float, default=1.0, help="gradient clipping")
    group.add_argument(
        "--train-iters",
        type=int,
        default=1000000,
        help="total number of iterations to train over all training runs",
    )
    group.add_argument("--max-length", type=int, default=512, help="max length of input")

    group.add_argument("--seed", type=int, default=1234, help="random seed for reproducibility")

    # Learning rate.
    group.add_argument("--lr", type=float, default=1.0e-4, help="initial learning rate")
    group.add_argument("--weight-decay", type=float, default=1.0e-2, help="weight decay rate")
    group.add_argument("--loss-scale", type=float, default=65536, help="loss scale")
    group.add_argument("--max-loss-scale", type=float, default=float("inf"), help="loss scale")
    group.add_argument("--min-loss-scale", type=float, default=1, help="loss scale")
    group.add_argument("--loss-scale-steps", type=float, default=1024, help="loss scale")

    group.add_argument(
        "--warmup-iters",
        type=float,
        default=0.01,
        help="percentage of data to warmup on (.01 = 1% of all " "training iters). Default 0.01",
    )
    group.add_argument(
        "--lr-decay-style",
        type=str,
        default="noam",
        choices=["constant", "linear", "cosine", "exponential", "noam"],
        help="learning rate decay function",
    )
    group.add_argument("--lr-decay-iters", type=int, default=None, help="lr decay steps")
    group.add_argument("--start-step", type=int, default=0, help="step to start or continue training")
    group.add_argument("--concat-data", action="store_true", help="whether we concatenate the dialogues")
    group.add_argument("--offload", action="store_true", help="whether we use offload_adam")
    group.add_argument("--new-bmt", action="store_true", help="new bmt without ckpt")
    group.add_argument("--flash", default="none", choices=["none", "1d", "triton", "cuda"])
    group.add_argument("--tp", default=1, type=int, help="whether we use tensor parallelism")
    group.add_argument("--bf16", action="store_true", help="whether we use bf16")
    group.add_argument("--gradient-accumulation-steps", type=int, default=1, help="gradient accumulation steps")
    return parser


def add_pretrain_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("pretrain", "pretrain configurations")
    group.add_argument(
        "--save-iters",
        type=int,
        default=1000,
        help="number of iterations between saves",
    )
    group.add_argument(
        "--log-dir",
        type=str,
        default=None,
        help="log directory",
    )
    group.add_argument(
        "--worker-name",
        type=str,
        default=None,
        help="worker name",
    )
    return parser


def add_finetune_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("finetune", "finetune configurations")
    group.add_argument("--epoch", type=int, default=1, help="number of training epochs")
    group.add_argument("--task-name", type=str, default="task", help="name of training task")
    group.add_argument("--save-epochs", type=int, default=1, help="number of training epochs between saves")
    group.add_argument("--save-steps", type=int, default=0, help="number of training steps between saves")
    group.add_argument(
        "--drop-last",
        action="store_true",
        default=False,
        help="drop data from each epoch that cannot be formed into a complete batch at the end",
    )
    group.add_argument("--delta-tuning", action="store_true", default=False)
    group.add_argument("--each-epoch-save", default=False)
    group.add_argument("--train-task-id", type=int, default=-1)
    return parser


def add_rhlf_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("rhlf", "rhlf configurations")

    group.add_argument(
        "--load-reward",
        type=str,
        default=None,
        help="Path to reward model checkpoint.",
    )
    group.add_argument("--actor-lr", type=float, default=1.0e-5, help="actor learning rate")
    group.add_argument("--critic-lr", type=float, default=1.0e-6, help="critic learning rate")
    group.add_argument("--actor-loss-scale", type=float, default=65536, help="actor loss scale")
    group.add_argument("--critic-loss-scale", type=float, default=65536, help="critic loss scale")
    group.add_argument("--avg-reward-bias", type=float, default=0, help="reward bias")
    group.add_argument("--actor-delay-step", type=int, default=0, help="actor delay step")
    group.add_argument("--entropy-coef", type=float, default=-1.0, help="coef of policy entropy")
    ##
    return parser


def add_simple_rhlf_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("simple_rhlf", "simple rhlf configurations")
    group.add_argument("--epoch", type=int, default=1, help="number of training epochs")
    group.add_argument("--sample-batch-size", type=int, default=32, help="Data Loader sample batch size")
    group.add_argument("--load-reward", type=str, default=None, help="Path to reward model checkpoint")
    group.add_argument("--avg-reward-bias", type=float, default=0, help="reward bias")
    group.add_argument("--sample-min-length", type=int, default=20, help="sample-min-length")
    group.add_argument("--sample-max-inp-length", type=int, default=1024, help="sample-max-inp-length")
    group.add_argument("--sample-max-length", type=int, default=64, help="sample-max-length")
    group.add_argument("--sample-repetition-penalty", type=float, default=1.05, help="sample-repetition-penalty")
    group.add_argument("--sample-temperature", type=float, default=1.0, help="sample-temperature")
    group.add_argument("--encode-max-length", type=int, default=1024, help="encode-max-length")
    group.add_argument("--generate-max-length", type=int, default=64, help="generate-max-length")
    group.add_argument("--value-loss-weight", type=float, default=0.1, help="value-loss-weight")
    group.add_argument("--ptx-loss-weight", type=float, default=0.001, help="ptx-loss-weight")
    group.add_argument("--save-epochs", type=int, default=1, help="number of training epochs between saves")
    ##
    return parser


def add_feedback_learning_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("rrhf", "rrhf configurations")
    group.add_argument("--length-penalty", type=float, default=1.0, help="length_penalty")
    group.add_argument("--feedback-weight", type=float, default=1.0, help="feedback_weight")
    group.add_argument("--sample-num", type=int, default=6, help="sample_num")
    group.add_argument("--dpo-beta", type=float, default=1.0, help="dpo_beta")
    group.add_argument("--stable-alignment-margin", type=float, default=1.0, help="stable_alignment_margin")
    group.add_argument("--feedback-learning-type", type=str, default="RRHF", help="feedback_learning_type")
    group.add_argument("--save-iters", type=int, default=1000, help="number of iterations between saves")
    ##
    return parser


def add_delta_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("LoRA","LoRA configurations")
    group.add_argument("--delta-type", type=str, default=None, help="delta-tuning-type")
    group.add_argument("--lora-r", type=int, default=8, help="lora-rank")
    group.add_argument("--lora-alpha", type=int, default=8, help="lora-alpha")
    group.add_argument("--lora-dropout", type=float, default=0.0, help="lora-dropout")
    group.add_argument("--lora-layer",  nargs='+', default=['project_q','project_k'], help="lora-layer")
    group.add_argument("--save-origin-model", action="store_true", default=False)
    return parser


def add_reward_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group("reward", "reward configurations")
    group.add_argument("--load-all", type=str, default=None, help="Path to a directory containing a model checkpoint.")
    ##
    return parser


def get_args(
    pretrain: bool = False,
    finetune: bool = False,
    rhlf: bool = False,
    simple_rlhf: bool = False,
    feedback_learning: bool = False,
    reward: bool = False,
):
    parser = argparse.ArgumentParser()
    parser = add_model_config_args(parser)  # config file need to be exported with model/ckpt
    parser = add_training_args(parser)
    if pretrain:
        parser = add_pretrain_args(parser)
    if finetune:
        parser = add_finetune_args(parser)
        parser = add_delta_args(parser)
    if rhlf:
        parser = add_rhlf_args(parser)
    if simple_rlhf:
        parser = add_simple_rhlf_args(parser)
    if feedback_learning:
        parser = add_feedback_learning_args(parser)
    if reward:
        parser = add_reward_args(parser)

    args = parser.parse_args()

    return args