"""
Audit table on the d=512 ResMLP variant. The main paper uses d=256;
the d=512 set provides a width-control. If the protocol's verdicts
generalize across width, it's a meaningful generalization claim.

Run:
    CUDA_VISIBLE_DEVICES=2 python -m protocol.examples.audit_d512
"""
import os
import sys
import json

import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

REPO_ROOT = os.path.dirname(
    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
)
sys.path.insert(0, REPO_ROOT)

from models.residual_mlp import ResidualMLP  # noqa: E402
from protocol import diagnose  # noqa: E402

D512_CKPT_DIR = os.path.join(REPO_ROOT, "results/confirmatory/cifar_d512")
OUT_DIR = os.path.join(REPO_ROOT, "results/protocol_audit")
os.makedirs(OUT_DIR, exist_ok=True)


def load_eval_batches(n_batches=10, batch_size=128, device="cuda:0"):
    tv = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
    ])
    te = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=tv)
    loader = DataLoader(te, batch_size=batch_size, shuffle=False, num_workers=0)
    batches = []
    for x, y in loader:
        x = x.view(x.size(0), -1).to(device)
        y = y.to(device)
        batches.append((x, y))
        if len(batches) >= n_batches:
            break
    return batches


def evaluate(model, device):
    tv = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
    ])
    te = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=tv)
    loader = DataLoader(te, batch_size=256, shuffle=False, num_workers=0)
    model.eval()
    correct = total = 0
    with torch.no_grad():
        for x, y in loader:
            x = x.view(x.size(0), -1).to(device)
            y = y.to(device)
            preds = model(x).argmax(-1)
            correct += (preds == y).sum().item()
            total += x.size(0)
    return correct / total


def load_d512(method, seed, device):
    path = os.path.join(D512_CKPT_DIR, f"{method}_s{seed}.pt")
    sd = torch.load(path, map_location=device, weights_only=False)
    if isinstance(sd, dict) and "state_dict" in sd:
        sd = sd["state_dict"]
    model = ResidualMLP(3072, 512, 10, 4).to(device)
    model.load_state_dict(sd)
    return model


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    eval_batches = load_eval_batches(n_batches=10, batch_size=128, device=device)
    methods = ["bp", "dfa", "state_bridge", "credit_bridge"]
    rows = []
    for seed in [42, 123, 456]:
        for method in methods:
            try:
                model = load_d512(method, seed, device)
            except FileNotFoundError:
                print(f"  SKIPPED: {method}_s{seed} not found")
                continue
            acc = evaluate(model, device)
            report = diagnose(
                model=model,
                eval_batches=eval_batches,
                headline_acc=acc,
                frozen_baseline_acc=None,
                method_name=method.upper(),
                notes=f"4-block d=512 ResMLP, CIFAR-10, seed {seed}",
            )
            rows.append({
                "method": method,
                "seed": seed,
                "acc": acc,
                "h_L": report.residual_norms[-1],
                "g_L": report.bp_grad_norms[-1],
                "stability": report.cross_batch_stability,
                "max_per_block": report.max_per_block_growth,
                "verdict": report.verdict,
            })

    print("=" * 110)
    print("d=512 ResMLP audit (3 seeds)")
    print("=" * 110)
    print(f"{'method':<16}{'seed':>6}{'acc':>8}{'||h_L||':>14}{'||g_L||':>14}"
          f"{'max/block':>12}{'stab':>10}  verdict")
    print("-" * 110)
    for r in rows:
        print(
            f"{r['method']:<16}{r['seed']:>6}{r['acc']:>8.4f}{r['h_L']:>14.3e}"
            f"{r['g_L']:>14.3e}{r['max_per_block']:>12.2e}{r['stability']:>10.3f}  "
            f"{r['verdict'][:60]}"
        )

    out_path = os.path.join(OUT_DIR, "audit_d512_3seed.json")
    with open(out_path, "w") as f:
        json.dump(rows, f, indent=2)
    print(f"\nSaved {out_path}")


if __name__ == "__main__":
    main()