from trainer.Trainer import Trainer
from trainer.cdeTrainer.cdetrainer import Trainer as cdeTrainer
from trainer.DCRNN_Trainer import Trainer as DCRNN_Trainer
from trainer.PDG2SEQ_Trainer import Trainer as PDG2SEQ_Trainer
from trainer.STMLP_Trainer import Trainer as STMLP_Trainer
from trainer.E32Trainer import Trainer as EXP_Trainer


def select_trainer(
    model,
    loss,
    optimizer,
    train_loader,
    val_loader,
    test_loader,
    scaler,
    args,
    lr_scheduler,
    kwargs,
):
    model_name = args["basic"]["model"]
    match model_name:
        case "STGNCDE":
            return cdeTrainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
                kwargs[0],
                None,
            )
        case "STGNRDE":
            return cdeTrainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
                kwargs[0],
                None,
            )
        case "DCRNN":
            return DCRNN_Trainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
            )
        case "PDG2SEQ":
            return PDG2SEQ_Trainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
            )
        case "STMLP":
            return STMLP_Trainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
            )
        case "EXP":
            return EXP_Trainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
            )
        case _:
            return Trainer(
                model,
                loss,
                optimizer,
                train_loader,
                val_loader,
                test_loader,
                scaler,
                args,
                lr_scheduler,
            )