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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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# python exps/LFNA/lfna.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
from copy import deepcopy
from pathlib import Path

lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
if str(lib_dir) not in sys.path:
    sys.path.insert(0, str(lib_dir))
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
from log_utils import time_string
from log_utils import AverageMeter, convert_secs2time

from utils import split_str2indexes

from procedures.advanced_main import basic_train_fn, basic_eval_fn
from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
from datasets.synthetic_core import get_synthetic_env
from models.xcore import get_model
from xlayers import super_core, trunc_normal_


from lfna_utils import lfna_setup, train_model, TimeData

from lfna_models_v2 import HyperNet


def main(args):
    logger, env_info, model_kwargs = lfna_setup(args)
    dynamic_env = env_info["dynamic_env"]
    model = get_model(**model_kwargs)
    model = model.to(args.device)
    criterion = torch.nn.MSELoss()

    logger.log("There are {:} weights.".format(model.get_w_container().numel()))
    # meta_train_range = (dynamic_env.min_timestamp, (dynamic_env.min_timestamp + dynamic_env.max_timestamp) / 2)
    # meta_train_interval = dynamic_env.timestamp_interval

    shape_container = model.get_w_container().to_shape_container()

    # pre-train the hypernetwork
    timestamps = list(
        dynamic_env.get_timestamp(index) for index in range(len(dynamic_env) // 2)
    )

    hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, timestamps)
    hypernet = hypernet.to(args.device)

    import pdb

    pdb.set_trace()

    # task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
    total_bar = 16
    task_embeds = []
    for i in range(total_bar):
        tensor = torch.Tensor(1, args.task_dim).to(args.device)
        task_embeds.append(torch.nn.Parameter(tensor))
    for task_embed in task_embeds:
        trunc_normal_(task_embed, std=0.02)

    model.train()
    hypernet.train()

    parameters = list(hypernet.parameters()) + task_embeds
    # optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
    optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
        optimizer,
        milestones=[
            int(args.epochs * 0.8),
            int(args.epochs * 0.9),
        ],
        gamma=0.1,
    )

    # total_bar = env_info["total"] - 1
    # LFNA meta-training
    loss_meter = AverageMeter()
    per_epoch_time, start_time = AverageMeter(), time.time()
    for iepoch in range(args.epochs):

        need_time = "Time Left: {:}".format(
            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
        )
        head_str = (
            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
            + need_time
        )

        losses = []
        # for ibatch in range(args.meta_batch):
        for cur_time in range(total_bar):
            # cur_time = random.randint(0, total_bar)
            cur_task_embed = task_embeds[cur_time]
            cur_container = hypernet(cur_task_embed)
            cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
            cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
            cur_dataset = TimeData(cur_time, cur_x, cur_y)

            preds = model.forward_with_container(cur_dataset.x, cur_container)
            optimizer.zero_grad()
            loss = criterion(preds, cur_dataset.y)

            losses.append(loss)

        final_loss = torch.stack(losses).mean()
        final_loss.backward()
        optimizer.step()
        lr_scheduler.step()

        loss_meter.update(final_loss.item())
        if iepoch % 100 == 0:
            logger.log(
                head_str
                + " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
                    loss_meter.avg,
                    loss_meter.val,
                    min(lr_scheduler.get_last_lr()),
                    len(losses),
                )
            )

            save_checkpoint(
                {
                    "hypernet": hypernet.state_dict(),
                    "task_embed": task_embed,
                    "lr_scheduler": lr_scheduler.state_dict(),
                    "iepoch": iepoch,
                },
                logger.path("model"),
                logger,
            )
            loss_meter.reset()
        per_epoch_time.update(time.time() - start_time)
        start_time = time.time()

    print(model)
    print(hypernet)

    w_container_per_epoch = dict()
    for idx in range(0, total_bar):
        future_time = env_info["{:}-timestamp".format(idx)]
        future_x = env_info["{:}-x".format(idx)]
        future_y = env_info["{:}-y".format(idx)]
        future_container = hypernet(task_embeds[idx])
        w_container_per_epoch[idx] = future_container.no_grad_clone()
        with torch.no_grad():
            future_y_hat = model.forward_with_container(
                future_x, w_container_per_epoch[idx]
            )
            future_loss = criterion(future_y_hat, future_y)
        logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))

    save_checkpoint(
        {"w_container_per_epoch": w_container_per_epoch},
        logger.path(None) / "final-ckp.pth",
        logger,
    )

    logger.log("-" * 200 + "\n")
    logger.close()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(".")
    parser.add_argument(
        "--save_dir",
        type=str,
        default="./outputs/lfna-synthetic/lfna-battle",
        help="The checkpoint directory.",
    )
    parser.add_argument(
        "--env_version",
        type=str,
        required=True,
        help="The synthetic enviornment version.",
    )
    parser.add_argument(
        "--hidden_dim",
        type=int,
        required=True,
        help="The hidden dimension.",
    )
    parser.add_argument(
        "--layer_dim",
        type=int,
        required=True,
        help="The hidden dimension.",
    )
    #####
    parser.add_argument(
        "--init_lr",
        type=float,
        default=0.1,
        help="The initial learning rate for the optimizer (default is Adam)",
    )
    parser.add_argument(
        "--meta_batch",
        type=int,
        default=64,
        help="The batch size for the meta-model",
    )
    parser.add_argument(
        "--epochs",
        type=int,
        default=2000,
        help="The total number of epochs.",
    )
    parser.add_argument(
        "--device",
        type=str,
        default="cpu",
        help="",
    )
    # Random Seed
    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
    args = parser.parse_args()
    if args.rand_seed is None or args.rand_seed < 0:
        args.rand_seed = random.randint(1, 100000)
    assert args.save_dir is not None, "The save dir argument can not be None"
    args.task_dim = args.layer_dim
    args.save_dir = "{:}-{:}-d{:}".format(
        args.save_dir, args.env_version, args.hidden_dim
    )
    main(args)