"""
Frozen-blocks and shallow baselines for a small CIFAR-10 ResNet (BatchNorm,
no LayerNorm) — codex-round-10 control to test whether the DFA "active-harm"
walk-back generalizes from LN-based architectures (ViT-Mini, ResMLP) to a
BN-based residual architecture.

Conditions per seed:
  - BP shallow (num_blocks=0)
  - BP frozen-blocks (num_blocks=4 frozen)
  - BP trainable (num_blocks=4)
  - DFA shallow (num_blocks=0)
  - DFA frozen-blocks (num_blocks=4 frozen)
  - DFA trainable (num_blocks=4)

If DFA-trainable < DFA-shallow on ResNet too → claim becomes "FA fails to train
deep blocks across multiple residual architectures including BN-based" — much
harder to dismiss as LN-specific.
If DFA-trainable ≈ or > DFA-shallow on ResNet → "harmful mode is specific to LN
normalization or terminal-LN architectures" — narrower but still useful claim.

Usage:
    CUDA_VISIBLE_DEVICES=2 python experiments/resnet_frozen_blocks_baseline.py --seed 42
"""
import sys, os, argparse
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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
import torchvision.transforms as transforms
import numpy as np

from models.small_resnet import SmallResNet


def get_loaders(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 evaluate(model, loader, dev):
    model.eval()
    n = c = 0
    with torch.no_grad():
        for x, y in loader:
            x, y = x.to(dev), y.to(dev)
            preds = model(x).argmax(-1)
            c += (preds == y).sum().item()
            n += x.size(0)
    return c / n


def freeze_blocks(model):
    for p in model.blocks.parameters():
        p.requires_grad_(False)
    # Also keep BN running stats frozen by setting to eval()
    for m in model.blocks.modules():
        if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
            m.eval()


def train_bp(model, train_loader, test_loader, dev, epochs, lr, wd, label, blocks_frozen=False):
    opt = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, weight_decay=wd)
    sch = optim.lr_scheduler.CosineAnnealingLR(opt, T_max=epochs)
    for ep in range(1, epochs + 1):
        model.train()
        if blocks_frozen:
            for m in model.blocks.modules():
                if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
                    m.eval()  # keep BN stats frozen
        for x, y in train_loader:
            x, y = x.to(dev), y.to(dev)
            loss = F.cross_entropy(model(x), y)
            opt.zero_grad(); loss.backward(); opt.step()
        sch.step()
        if ep % 10 == 0 or ep == 1 or ep == epochs:
            acc = evaluate(model, test_loader, dev)
            print(f"  [{label}] ep {ep}: test_acc={acc:.4f}", flush=True)
    return model


def train_dfa(model, train_loader, test_loader, dev, epochs, lr, wd, label, blocks_frozen=False):
    """DFA on the BN-ResNet:
    - head trained with true CE on the pooled hidden state
    - stem (conv + BN) trained via DFA-style local loss with random feedback
    - blocks (if any) skipped (frozen for blocks_frozen=True; for trainable case, the
      naive analog would be DFA-style local loss per block, but this script focuses on
      the frozen/shallow comparison; for trainable comparison use the existing ResMLP
      experiment as the analogous "trainable" since they share the same ad-hoc DFA pattern).
    For this experiment we focus on the frozen and shallow conditions.
    """
    d_hidden = model.d_hidden
    L = max(model.num_blocks, 1)
    C = 10
    Bs = [torch.randn(d_hidden, C, device=dev) / np.sqrt(C) for _ in range(L)]

    stem_params = list(model.stem_conv.parameters()) + list(model.stem_bn.parameters())
    stem_opt = optim.AdamW(stem_params, lr=lr, weight_decay=wd)
    head_opt = optim.AdamW(model.out_head.parameters(), lr=lr, weight_decay=wd)
    sch1 = optim.lr_scheduler.CosineAnnealingLR(stem_opt, T_max=epochs)
    sch2 = optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)

    for ep in range(1, epochs + 1):
        model.train()
        if blocks_frozen:
            for m in model.blocks.modules():
                if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
                    m.eval()
        for x, y in train_loader:
            x, y = x.to(dev), y.to(dev)
            with torch.no_grad():
                logits, hi = model(x, return_hidden=True)
                e_T = logits.softmax(-1); e_T[torch.arange(x.size(0)), y] -= 1
            hL_det = hi[-1].detach()  # (B, d_hidden, 32, 32)
            # Head update via true CE on pooled cls
            h_pool = F.adaptive_avg_pool2d(hL_det, 1).flatten(1)
            head_opt.zero_grad()
            F.cross_entropy(model.out_head(h_pool), y).backward()
            head_opt.step()
            # Stem update via DFA local loss
            a0 = (e_T @ Bs[0].T).detach()  # (B, d_hidden)
            rms = (a0 ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
            h0 = model.stem(x)  # (B, d_hidden, 32, 32)
            # Broadcast credit across spatial positions: (B, d, 1, 1) -> (B, d, H, W)
            a0_b = (a0 / rms).unsqueeze(-1).unsqueeze(-1).expand_as(h0)
            stem_loss = (h0 * a0_b).sum(dim=1).mean()  # average over batch and spatial
            stem_opt.zero_grad()
            stem_loss.backward()
            stem_opt.step()
        sch1.step(); sch2.step()
        if ep % 10 == 0 or ep == 1 or ep == epochs:
            acc = evaluate(model, test_loader, dev)
            print(f"  [{label}] ep {ep}: test_acc={acc:.4f}", flush=True)
    return model


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--epochs', type=int, default=60)
    parser.add_argument('--lr', type=float, default=1e-3)
    parser.add_argument('--wd', type=float, default=0.01)
    parser.add_argument('--d_hidden', type=int, default=64)
    args = parser.parse_args()

    dev = torch.device('cuda:0')
    print(f"Device: {dev}, seed={args.seed}, epochs={args.epochs}", flush=True)
    train_loader, test_loader = get_loaders(batch_size=128)

    results = {}
    C = 10

    # Trainable BP (full 4-block ResNet)
    print(f"\n=== BP trainable (SmallResNet num_blocks=4), seed={args.seed} ===", flush=True)
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    m = SmallResNet(d_hidden=args.d_hidden, num_classes=C, num_blocks=4).to(dev)
    print(f"  n_params: {sum(p.numel() for p in m.parameters())} ({sum(p.numel() for p in m.parameters() if p.requires_grad)} trainable)", flush=True)
    train_bp(m, train_loader, test_loader, dev, args.epochs, args.lr, args.wd, 'BP-trainable')
    results['bp_trainable'] = evaluate(m, test_loader, dev)
    print(f"FINAL BP-trainable: {results['bp_trainable']:.4f}", flush=True)

    # Trainable DFA — block-level DFA on ResNet (each block as a unit)
    print(f"\n=== DFA trainable (SmallResNet num_blocks=4 block-level DFA), seed={args.seed} ===", flush=True)
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    m = SmallResNet(d_hidden=args.d_hidden, num_classes=C, num_blocks=4).to(dev)
    # We use the same approach as ViT/ResMLP: stem trained with DFA, blocks trained
    # with their own DFA-style local loss per block, head with true CE.
    # For simplicity reuse train_dfa logic but extend it to also train blocks.
    # Since this script focuses on frozen/shallow control, we'll do trainable in a
    # separate inner loop here.
    d_hidden = m.d_hidden; L = m.num_blocks
    Bs = [torch.randn(d_hidden, C, device=dev) / np.sqrt(C) for _ in range(L)]
    block_opts = [optim.AdamW(b.parameters(), lr=args.lr, weight_decay=args.wd) for b in m.blocks]
    stem_params = list(m.stem_conv.parameters()) + list(m.stem_bn.parameters())
    stem_opt = optim.AdamW(stem_params, lr=args.lr, weight_decay=args.wd)
    head_opt = optim.AdamW(m.out_head.parameters(), lr=args.lr, weight_decay=args.wd)
    all_sch = [optim.lr_scheduler.CosineAnnealingLR(o, T_max=args.epochs) for o in block_opts] + \
              [optim.lr_scheduler.CosineAnnealingLR(stem_opt, T_max=args.epochs),
               optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=args.epochs)]
    for ep in range(1, args.epochs + 1):
        m.train()
        for x, y in train_loader:
            x, y = x.to(dev), y.to(dev)
            with torch.no_grad():
                logits, hi = m(x, return_hidden=True)
                e_T = logits.softmax(-1); e_T[torch.arange(x.size(0)), y] -= 1
            hL_det = hi[-1].detach()
            h_pool = F.adaptive_avg_pool2d(hL_det, 1).flatten(1)
            head_opt.zero_grad()
            F.cross_entropy(m.out_head(h_pool), y).backward()
            head_opt.step()
            for l in range(L):
                h_l = hi[l].detach()
                a_l = (e_T @ Bs[l].T).detach()
                rms = (a_l ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
                a_l_norm = (a_l / rms).unsqueeze(-1).unsqueeze(-1).expand_as(h_l)
                f_l = m.blocks[l](h_l)
                local_loss = (f_l * a_l_norm).sum(dim=1).mean()
                block_opts[l].zero_grad(); local_loss.backward()
                torch.nn.utils.clip_grad_norm_(m.blocks[l].parameters(), 1.0)
                block_opts[l].step()
            a_0 = (e_T @ Bs[0].T).detach()
            rms_0 = (a_0 ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
            h0 = m.stem(x)
            a_0_b = (a_0 / rms_0).unsqueeze(-1).unsqueeze(-1).expand_as(h0)
            stem_loss = (h0 * a_0_b).sum(dim=1).mean()
            stem_opt.zero_grad(); stem_loss.backward(); stem_opt.step()
        for s in all_sch: s.step()
        if ep % 10 == 0 or ep == 1 or ep == args.epochs:
            acc = evaluate(m, test_loader, dev)
            print(f"  [DFA-trainable] ep {ep}: test_acc={acc:.4f}", flush=True)
    results['dfa_trainable'] = evaluate(m, test_loader, dev)
    print(f"FINAL DFA-trainable: {results['dfa_trainable']:.4f}", flush=True)

    # BP shallow
    print(f"\n=== BP shallow (SmallResNet num_blocks=0), seed={args.seed} ===", flush=True)
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    m = SmallResNet(d_hidden=args.d_hidden, num_classes=C, num_blocks=0).to(dev)
    print(f"  n_params: {sum(p.numel() for p in m.parameters())} ({sum(p.numel() for p in m.parameters() if p.requires_grad)} trainable)", flush=True)
    train_bp(m, train_loader, test_loader, dev, args.epochs, args.lr, args.wd, 'BP-shallow')
    results['bp_shallow'] = evaluate(m, test_loader, dev)
    print(f"FINAL BP-shallow: {results['bp_shallow']:.4f}", flush=True)

    # BP frozen-blocks
    print(f"\n=== BP frozen-blocks (SmallResNet num_blocks=4 frozen), seed={args.seed} ===", flush=True)
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    m = SmallResNet(d_hidden=args.d_hidden, num_classes=C, num_blocks=4).to(dev)
    freeze_blocks(m)
    print(f"  n_params: {sum(p.numel() for p in m.parameters())} ({sum(p.numel() for p in m.parameters() if p.requires_grad)} trainable)", flush=True)
    train_bp(m, train_loader, test_loader, dev, args.epochs, args.lr, args.wd, 'BP-frozen', blocks_frozen=True)
    results['bp_frozen'] = evaluate(m, test_loader, dev)
    print(f"FINAL BP-frozen-blocks: {results['bp_frozen']:.4f}", flush=True)

    # DFA shallow
    print(f"\n=== DFA shallow (SmallResNet num_blocks=0), seed={args.seed} ===", flush=True)
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    m = SmallResNet(d_hidden=args.d_hidden, num_classes=C, num_blocks=0).to(dev)
    train_dfa(m, train_loader, test_loader, dev, args.epochs, args.lr, args.wd, 'DFA-shallow')
    results['dfa_shallow'] = evaluate(m, test_loader, dev)
    print(f"FINAL DFA-shallow: {results['dfa_shallow']:.4f}", flush=True)

    # DFA frozen-blocks
    print(f"\n=== DFA frozen-blocks (SmallResNet num_blocks=4 frozen), seed={args.seed} ===", flush=True)
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    m = SmallResNet(d_hidden=args.d_hidden, num_classes=C, num_blocks=4).to(dev)
    freeze_blocks(m)
    train_dfa(m, train_loader, test_loader, dev, args.epochs, args.lr, args.wd, 'DFA-frozen', blocks_frozen=True)
    results['dfa_frozen'] = evaluate(m, test_loader, dev)
    print(f"FINAL DFA-frozen-blocks: {results['dfa_frozen']:.4f}", flush=True)

    print(f"\n=== Small ResNet (BatchNorm) frozen/shallow baseline summary, seed={args.seed} ===")
    for k, v in results.items():
        print(f"  {k}: {v:.4f}")
    print(f"\nKey gaps (DFA):")
    if 'dfa_shallow' in results and 'dfa_trainable' in results:
        print(f"  DFA-shallow ({results['dfa_shallow']:.4f}) - DFA-trainable ({results['dfa_trainable']:.4f}) = {results['dfa_shallow']-results['dfa_trainable']:+.4f}")
    if 'dfa_frozen' in results and 'dfa_trainable' in results:
        print(f"  DFA-frozen ({results['dfa_frozen']:.4f}) - DFA-trainable ({results['dfa_trainable']:.4f}) = {results['dfa_frozen']-results['dfa_trainable']:+.4f}")


if __name__ == '__main__':
    main()