"""
Smoke test for the FA Diagnostic Protocol reference implementation.

Loads BP-trained and DFA-trained ResMLP checkpoints from
`results/confirmatory/checkpoints_A2/` and applies the protocol to each.
The protocol should:
  - return verdict="trustworthy" on the BP checkpoint (residual norms bounded,
    BP grad ~5e-5 well above floor, low cross-batch direction stability),
  - flag the DFA checkpoint as "needs walk-back" (residual stream exploded
    by ~10^7×, BP grad at ~5e-10, drift-dominated direction).

Run with:
    CUDA_VISIBLE_DEVICES=2 python -m protocol.smoke_test
"""
import os
import sys

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

# Make `protocol` and `models` importable when invoked from repo root
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

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


REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CHECKPOINT_DIR = os.path.join(REPO_ROOT, "results/confirmatory/checkpoints_A2")
EP_CHECKPOINT_DIR = os.path.join(REPO_ROOT, "results/ep_baseline")


def load_eval_batches(n_batches: int = 4, batch_size: int = 1024, 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, loader, device):
    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_model(method: str, seed: int, device):
    if method == "ep":
        path = os.path.join(EP_CHECKPOINT_DIR, f"ep_s{seed}.pt")
    else:
        path = os.path.join(CHECKPOINT_DIR, f"{method}_s{seed}.pt")
    ckpt = torch.load(path, map_location=device, weights_only=False)
    # Try several common checkpoint formats
    if isinstance(ckpt, dict) and "state_dict" in ckpt:
        sd = ckpt["state_dict"]
    elif isinstance(ckpt, dict) and "model" in ckpt:
        sd = ckpt["model"]
    elif isinstance(ckpt, dict):
        sd = ckpt
    else:
        sd = ckpt.state_dict()
    model = ResidualMLP(input_dim=3072, d_hidden=256, num_classes=10, num_blocks=4).to(device)
    model.load_state_dict(sd)
    return model


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(f"Device: {device}")

    # Cross-batch stability is sensitive to batch size: with smaller batches
    # the per-sample noise component dominates the batch-mean direction on
    # healthy networks, giving the cleanest separation from the drift-
    # dominated failure mode (~0.10 healthy vs ~0.95 drift at N=128).
    eval_batches = load_eval_batches(n_batches=10, batch_size=128, device=device)

    # Build a single test loader for headline acc
    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)
    test_loader = DataLoader(te, batch_size=256, shuffle=False, num_workers=0)

    # BP: trustworthy. DFA: walked back. EP: trustworthy (the internal control
    # — same architecture/metric/dataset, but EP doesn't blow up the residual
    # stream, so the diagnostic protocol passes for EP even though EP's
    # accuracy is also low. This is the paper's central comparison.)
    for method, expected in [
        ("bp", "trustworthy"),
        ("dfa", "needs walk-back"),
        ("ep", "trustworthy"),
    ]:
        print()
        print(f"### {method.upper()} (seed 42)")
        model = load_model(method, 42, device)
        acc = evaluate(model, test_loader, device)
        report = diagnose(
            model=model,
            eval_batches=eval_batches,
            headline_acc=acc,
            frozen_baseline_acc=None,
            method_name=method.upper(),
            notes="4-block d=256 ResMLP, CIFAR-10",
        )
        print(report)
        # Sanity check
        verdict = report.verdict
        if expected == "trustworthy" and verdict != "trustworthy":
            print(f"!! UNEXPECTED: BP should be trustworthy, got '{verdict}'")
        elif expected == "needs walk-back" and not verdict.startswith("needs walk-back"):
            print(f"!! UNEXPECTED: DFA should need walk-back, got '{verdict}'")
        else:
            print(f"OK: verdict matches expectation ('{expected}')")


if __name__ == "__main__":
    main()