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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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# python exps/LFNA/lfna-v1.py
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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


class LFNAmlp:
    """A LFNA meta-model that uses the MLP as delta-net."""

    def __init__(self, obs_dim, hidden_sizes, act_name):
        self.delta_net = super_core.SuperSequential(
            super_core.SuperLinear(obs_dim, hidden_sizes[0]),
            super_core.super_name2activation[act_name](),
            super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
            super_core.super_name2activation[act_name](),
            super_core.SuperLinear(hidden_sizes[1], 1),
        )
        self.meta_optimizer = torch.optim.Adam(
            self.delta_net.parameters(), lr=0.01, amsgrad=True
        )

    def adapt(self, model, criterion, w_container, xs, ys):
        containers = [w_container]
        for idx, (x, y) in enumerate(zip(xs, ys)):
            y_hat = model.forward_with_container(x, containers[-1])
            loss = criterion(y_hat, y)
            gradients = torch.autograd.grad(loss, containers[-1].tensors)
            with torch.no_grad():
                flatten_w = containers[-1].flatten().view(-1, 1)
                flatten_g = containers[-1].flatten(gradients).view(-1, 1)
                input_statistics = torch.tensor([x.mean(), x.std()]).view(1, 2)
                input_statistics = input_statistics.expand(flatten_w.numel(), -1)
            delta_inputs = torch.cat((flatten_w, flatten_g, input_statistics), dim=-1)
            delta = self.delta_net(delta_inputs).view(-1)
            # delta = torch.clamp(delta, -0.5, 0.5)
            unflatten_delta = containers[-1].unflatten(delta)
            future_container = containers[-1].additive(unflatten_delta)
            containers.append(future_container)
        # containers = containers[1:]
        meta_loss = []
        for idx, (x, y) in enumerate(zip(xs, ys)):
            if idx == 0:
                continue
            current_container = containers[idx]
            y_hat = model.forward_with_container(x, current_container)
            loss = criterion(y_hat, y)
            meta_loss.append(loss)
        meta_loss = sum(meta_loss)
        meta_loss.backward()
        self.meta_optimizer.step()

    def zero_grad(self):
        self.meta_optimizer.zero_grad()
        self.delta_net.zero_grad()


class Population:
    """A population used to maintain models at different timestamps."""

    def __init__(self):
        self._time2model = dict()
        self._time2score = dict()  # higher is better

    def append(self, timestamp, model, score):
        if timestamp in self._time2model:
            raise ValueError("This timestamp has been added.")
        self._time2model[timestamp] = model
        self._time2score[timestamp] = score

    def query(self, timestamp):
        closet_timestamp = None
        for xtime, model in self._time2model.items():
            if (
                closet_timestamp is None
                or timestamp - closet_timestamp >= timestamp - xtime
            ):
                closet_timestamp = xtime
        return self._time2model[closet_timestamp], closet_timestamp


def main(args):
    prepare_seed(args.rand_seed)
    logger = prepare_logger(args)

    cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
    if cache_path.exists():
        env_info = torch.load(cache_path)
    else:
        env_info = dict()
        dynamic_env = get_synthetic_env()
        env_info["total"] = len(dynamic_env)
        for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
            env_info["{:}-timestamp".format(idx)] = timestamp
            env_info["{:}-x".format(idx)] = _allx
            env_info["{:}-y".format(idx)] = _ally
        env_info["dynamic_env"] = dynamic_env
        torch.save(env_info, cache_path)

    total_time = env_info["total"]
    for i in range(total_time):
        for xkey in ("timestamp", "x", "y"):
            nkey = "{:}-{:}".format(i, xkey)
            assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
    train_time_bar = total_time // 2
    base_model = get_model(
        dict(model_type="simple_mlp"),
        act_cls="leaky_relu",
        norm_cls="simple_learn_norm",
        mean=0,
        std=1,
        input_dim=1,
        output_dim=1,
    )

    w_container = base_model.named_parameters_buffers()
    criterion = torch.nn.MSELoss()
    print("There are {:} weights.".format(w_container.numel()))

    adaptor = LFNAmlp(4, (50, 20), "leaky_relu")

    pool = Population()
    pool.append(0, w_container)

    # LFNA meta-training
    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)
        )
        logger.log(
            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
            + need_time
        )

        for ibatch in range(args.meta_batch):
            sampled_timestamp = random.randint(0, train_time_bar)
            query_w_container, query_timestamp = pool.query(sampled_timestamp)
            # def adapt(self, model, w_container, xs, ys):
            xs, ys = [], []
            for it in range(sampled_timestamp, sampled_timestamp + args.max_seq):
                xs.append(env_info["{:}-x".format(it)])
                ys.append(env_info["{:}-y".format(it)])
            adaptor.adapt(base_model, criterion, query_w_container, xs, ys)
            import pdb

            pdb.set_trace()
        print("-")
        logger.log("")

        per_timestamp_time.update(time.time() - start_time)
        start_time = time.time()

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


if __name__ == "__main__":
    parser = argparse.ArgumentParser("Use the data in the past.")
    parser.add_argument(
        "--save_dir",
        type=str,
        default="./outputs/lfna-synthetic/lfna-v1",
        help="The checkpoint directory.",
    )
    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=2,
        help="The batch size for the meta-model",
    )
    parser.add_argument(
        "--epochs",
        type=int,
        default=1000,
        help="The total number of epochs.",
    )
    parser.add_argument(
        "--max_seq",
        type=int,
        default=5,
        help="The maximum length of the sequence.",
    )
    parser.add_argument(
        "--workers",
        type=int,
        default=4,
        help="The number of data loading workers (default: 4)",
    )
    # 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"
    main(args)