"""Train the local-learning baseline 'zoo' and report test accuracy, for the
comparison table alongside SDIL/DFA/BP. FA and EP are the working ones; PEPITA
and FF are included but currently undertuned.
Usage: python experiments/zoo.py --dataset mnist --methods fa,ep --epochs 10
"""
import argparse
import json
import os
import sys
import time
import torch

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from sdil.local_baselines import FANet, PEPITANet, FFNet, EPNet
from sdil.baselines import evaluate
from sdil.data import get_dataset, onehot


def run_method(m, args, device):
    tr, te, n_in, n_out = get_dataset(args.dataset, args.batch_size, device=device)
    sizes = [n_in] + [args.width] * args.depth + [10]
    t0 = time.time()
    if m == "fa":
        net = FANet(sizes, act="tanh", device=device, seed=args.seed)
        for ep in range(args.epochs):
            for x, y in tr:
                net.fa_step(x, y, onehot(y, 10, device=device), args.eta, 0.9)
        acc = evaluate(net, te)[0]
    elif m == "pepita":
        net = PEPITANet(sizes, act="tanh", device=device, seed=args.seed, f_scale=0.5)
        for ep in range(args.epochs):
            for x, y in tr:
                net.pepita_step(x, y, onehot(y, 10, device=device), args.eta, 0.9)
        acc = evaluate(net, te)[0]
    elif m == "ff":
        net = FFNet(sizes, act="relu", device=device, seed=args.seed, threshold=2.0, overlay_val=10.0)
        for ep in range(args.epochs):
            for x, y in tr:
                net.train_step(x, y, args.eta)
        acc = net.evaluate(te)[0]
    elif m == "ep":
        net = EPNet([n_in, args.width, 10], device=device, seed=args.seed,
                    beta=0.5, dt=0.5, T_free=20, T_nudge=8)
        for ep in range(args.epochs):
            for x, y in tr:
                net.train_step(x, y, onehot(y, 10, device=device), args.eta)
        acc = net.evaluate(te)[0]
    else:
        raise ValueError(m)
    return acc, time.time() - t0


def main():
    p = argparse.ArgumentParser()
    p.add_argument("--dataset", default="mnist")
    p.add_argument("--methods", default="fa,ep,pepita,ff")
    p.add_argument("--depth", type=int, default=2)
    p.add_argument("--width", type=int, default=500)
    p.add_argument("--epochs", type=int, default=10)
    p.add_argument("--batch_size", type=int, default=64)
    p.add_argument("--eta", type=float, default=0.1)
    p.add_argument("--seed", type=int, default=0)
    p.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
    p.add_argument("--outdir", default="results")
    p.add_argument("--tag", default="zoo")
    args = p.parse_args()

    # sensible per-method default LRs if the shared one is off
    lrs = {"fa": 0.05, "ep": 0.1, "pepita": 0.05, "ff": 0.03}
    out = {"args": vars(args), "acc": {}}
    for m in args.methods.split(","):
        args.eta = lrs.get(m, args.eta)
        acc, secs = run_method(m, args, args.device)
        out["acc"][m] = acc
        print(f"[{args.dataset}] {m}: test_acc {acc:.4f} ({secs:.0f}s)", flush=True)
    os.makedirs(args.outdir, exist_ok=True)
    with open(os.path.join(args.outdir, f"{args.tag}.json"), "w") as f:
        json.dump(out, f)
    print(f"saved -> {args.outdir}/{args.tag}.json", flush=True)


if __name__ == "__main__":
    main()