"""
Canonical DFA penalty trajectory: per-epoch ||h_L|| and ||g_L|| for λ ∈ {0, 1e-4, 1e-2}.
3 seeds × 3 λ × 30 epochs. Uses canonical cifar_resmlp.py DFA implementation (no clipping, mean reduction).
"""
import os, sys, json, argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision, torchvision.transforms as transforms

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models.residual_mlp import ResidualMLP


def get_data(batch_size=128):
    tv_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
    ])
    tv = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
    ])
    tr = torchvision.datasets.CIFAR10('./data', True, download=True, transform=tv_train)
    te = torchvision.datasets.CIFAR10('./data', False, download=True, transform=tv)
    return (DataLoader(tr, batch_size=batch_size, shuffle=True, num_workers=2),
            DataLoader(te, batch_size=batch_size, shuffle=False, num_workers=2))


def diagnose_quick(model, x_eval, y_eval):
    model.eval()
    x_flat = x_eval.view(x_eval.size(0), -1)
    with torch.no_grad():
        logits, hiddens = model(x_flat, return_hidden=True)
    h_L = hiddens[-1].norm(dim=-1).median().item()
    # BP grad at h_L
    h0 = model.embed(x_flat.detach())
    hs = [h0.clone().requires_grad_(True)]
    for b in model.blocks:
        hs.append(hs[-1] + b(hs[-1]))
    logits2 = model.out_head(model.out_ln(hs[-1]))
    loss = F.cross_entropy(logits2, y_eval)
    grads = torch.autograd.grad(loss, hs)
    g_L = grads[-1].norm(dim=-1).median().item()
    acc = (logits.argmax(-1) == y_eval).float().mean().item()
    model.train()
    return h_L, g_L, acc


def train_dfa_trajectory(seed, train_loader, x_eval, y_eval, device, epochs, lam):
    L, d, C = 4, 256, 10
    torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
    model = ResidualMLP(3072, d, C, L).to(device)
    Bs = [torch.randn(d, C, device=device) / np.sqrt(C) for _ in range(L)]
    block_opts = [optim.AdamW(block.parameters(), lr=1e-3, weight_decay=0.01) for block in model.blocks]
    embed_opt = optim.AdamW(model.embed.parameters(), lr=1e-3, weight_decay=0.01)
    head_opt = optim.AdamW(list(model.out_head.parameters()) + list(model.out_ln.parameters()),
                           lr=1e-3, weight_decay=0.01)
    all_sch = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=epochs) for o in block_opts]
               + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=epochs),
                  optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)])

    log = []
    h_L, g_L, acc = diagnose_quick(model, x_eval, y_eval)
    log.append({'epoch': 0, 'h_L': h_L, 'g_L': g_L, 'acc': acc})

    for epoch in range(1, epochs + 1):
        model.train()
        for x, y in train_loader:
            x = x.view(x.size(0), -1).to(device); y = y.to(device)
            batch = x.size(0)
            with torch.no_grad():
                logits, hiddens = model(x, return_hidden=True)
                e_T = logits.softmax(-1); e_T[torch.arange(batch), y] -= 1
            hL_det = hiddens[-1].detach()
            logits_out = model.out_head(model.out_ln(hL_det))
            head_opt.zero_grad(); F.cross_entropy(logits_out, y).backward(); head_opt.step()
            for l in range(L):
                h_l = hiddens[l].detach()
                a_dfa = (e_T @ Bs[l].T).detach()
                rms = (a_dfa ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
                f_l = model.blocks[l](h_l)
                local_loss = (f_l * (a_dfa / rms)).sum(dim=-1).mean()
                if lam > 0:
                    local_loss = local_loss + lam * (f_l ** 2).sum(dim=-1).mean()
                block_opts[l].zero_grad(); local_loss.backward(); block_opts[l].step()
            a_0 = (e_T @ Bs[0].T).detach()
            rms_0 = (a_0 ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
            h0 = model.embed(x)
            embed_loss = (h0 * (a_0 / rms_0)).sum(dim=-1).mean()
            embed_opt.zero_grad(); embed_loss.backward(); embed_opt.step()
        for s in all_sch: s.step()
        h_L, g_L, acc = diagnose_quick(model, x_eval, y_eval)
        log.append({'epoch': epoch, 'h_L': h_L, 'g_L': g_L, 'acc': acc})
        if epoch % 10 == 0 or epoch == epochs:
            print(f"  [lam={lam}] s={seed} ep {epoch}: ||h_L||={h_L:.3e} ||g_L||={g_L:.3e} acc={acc:.4f}", flush=True)
    return log


def main():
    p = argparse.ArgumentParser()
    p.add_argument('--output', type=str, default='results/dfa_canonical_penalty_trajectory.json')
    args = p.parse_args()

    device = torch.device('cuda:0')
    train_loader, test_loader = get_data(128)
    # Fixed 128-sample eval buffer (consistent with cifar_resmlp.py compute_diagnostics)
    xs, ys = [], []
    for x, y in test_loader:
        xs.append(x); ys.append(y)
        if sum(xb.size(0) for xb in xs) >= 128:
            break
    x_eval = torch.cat(xs)[:128].to(device)
    y_eval = torch.cat(ys)[:128].to(device)

    results = {}
    for lam in [0.0, 1e-4, 1e-2]:
        lam_key = f'lam_{lam}'
        results[lam_key] = {}
        for seed in [42, 123, 456]:
            print(f"\n=== λ={lam}, seed={seed} ===", flush=True)
            log = train_dfa_trajectory(seed, train_loader, x_eval, y_eval, device, 30, lam)
            results[lam_key][str(seed)] = log

    with open(args.output, 'w') as f:
        json.dump(results, f, indent=2)
    print(f"\nSaved: {args.output}", flush=True)


if __name__ == '__main__':
    main()