"""
Clean BP gradient check — run in independent Python process per method.
Usage: python clean_gradient_check.py --method bp --seed 42 --gpu 1
"""
import os, sys, json, argparse, numpy as np, torch, torch.nn.functional as F
from torch.utils.data import DataLoader
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models.residual_mlp import ResidualMLP
import torchvision, torchvision.transforms as transforms

def main():
    p = argparse.ArgumentParser()
    p.add_argument('--method', type=str, required=True)
    p.add_argument('--seed', type=int, default=42)
    p.add_argument('--gpu', type=int, default=1)
    p.add_argument('--output_dir', type=str, default='results/confirmatory/clean_grads')
    args = p.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)
    device = torch.device(f'cuda:{args.gpu}')

    # 1. Load eval data (256 samples, first batch, no shuffle)
    tv = transforms.Compose([transforms.ToTensor(),
        transforms.Normalize((0.4914,0.4822,0.4465),(0.2470,0.2435,0.2616))])
    tel = DataLoader(torchvision.datasets.CIFAR10('./data', False, download=True, transform=tv),
                     256, False, num_workers=0)  # num_workers=0 for determinism
    for x, y in tel:
        x = x.view(x.size(0), -1).to(device)
        y = y.to(device)
        break
    batch = x.size(0)
    print(f"[{args.method} s={args.seed}] Batch: {batch}, y[:5]={y[:5].tolist()}", flush=True)

    # 2. Create model from scratch, load checkpoint (strict=True)
    L, d, C = 4, 256, 10
    ckpt_path = f'results/confirmatory/checkpoints_A2/{args.method}_s{args.seed}.pt'
    assert os.path.exists(ckpt_path), f"Checkpoint not found: {ckpt_path}"

    model = ResidualMLP(3072, d, C, L).to(device)
    sd = torch.load(ckpt_path, map_location=device)
    model.load_state_dict(sd, strict=True)
    model.eval()

    # Verify: print first param norm and checkpoint hash
    first_param = list(model.parameters())[0]
    print(f"  First param norm: {first_param.norm().item():.6f}", flush=True)
    print(f"  Checkpoint: {ckpt_path}", flush=True)

    # 3. Method A: manual forward + autograd.grad
    h0 = model.embed(x.detach())
    hs = [h0.clone().requires_grad_(True)]
    for b in model.blocks:
        hs.append(hs[-1] + b(hs[-1]))
    lo_a = model.out_head(model.out_ln(hs[-1]))
    loss_a = F.cross_entropy(lo_a, y)
    acc_a = (lo_a.argmax(1) == y).float().mean().item()
    gs_a = torch.autograd.grad(loss_a, hs)

    print(f"  Method A (manual+autograd.grad): loss={loss_a.item():.6f} acc={acc_a:.4f}", flush=True)
    for l in range(L):
        n = gs_a[l].norm(dim=-1)
        print(f"    layer {l}: mean_norm={n.mean():.2e} median={n.median():.2e} "
              f"max={n.max():.2e} s(1e-6)={(n>1e-6).float().mean():.4f}", flush=True)

    # 4. Method B: retain_grad + backward
    model.zero_grad()
    for param in model.parameters():
        param.requires_grad_(True)
    lo_b, hi_b = model(x, return_hidden=True)
    for l in range(L + 1):
        hi_b[l].retain_grad()
    loss_b = F.cross_entropy(lo_b, y)
    acc_b = (lo_b.argmax(1) == y).float().mean().item()
    loss_b.backward()

    print(f"  Method B (retain_grad+backward): loss={loss_b.item():.6f} acc={acc_b:.4f}", flush=True)
    for l in range(L):
        if hi_b[l].grad is not None:
            n = hi_b[l].grad.norm(dim=-1)
            print(f"    layer {l}: mean_norm={n.mean():.2e} median={n.median():.2e} "
                  f"max={n.max():.2e} s(1e-6)={(n>1e-6).float().mean():.4f}", flush=True)
        else:
            print(f"    layer {l}: grad is None!", flush=True)

    # 5. Method C: full model backward (no detach)
    model.zero_grad()
    lo_c = model(x)
    loss_c = F.cross_entropy(lo_c, y)
    loss_c.backward()
    # Get embedding gradient as proxy
    embed_grad_norm = model.embed.weight.grad.norm().item() if model.embed.weight.grad is not None else 0
    print(f"  Method C (full backward): loss={loss_c.item():.6f} embed_grad_norm={embed_grad_norm:.2e}", flush=True)

    # 6. Save results
    result = {
        'method': args.method, 'seed': args.seed, 'batch_size': batch,
        'y_first5': y[:5].tolist(),
        'first_param_norm': first_param.norm().item(),
        'method_A': {
            'loss': loss_a.item(), 'acc': acc_a,
            'per_layer': [{
                'mean_norm': gs_a[l].norm(-1).mean().item(),
                'median_norm': gs_a[l].norm(-1).median().item(),
                'max_norm': gs_a[l].norm(-1).max().item(),
                's_1e6': (gs_a[l].norm(-1) > 1e-6).float().mean().item(),
            } for l in range(L)]
        },
        'method_B': {
            'loss': loss_b.item(), 'acc': acc_b,
            'per_layer': [{
                'mean_norm': hi_b[l].grad.norm(-1).mean().item() if hi_b[l].grad is not None else None,
                'median_norm': hi_b[l].grad.norm(-1).median().item() if hi_b[l].grad is not None else None,
                'max_norm': hi_b[l].grad.norm(-1).max().item() if hi_b[l].grad is not None else None,
                's_1e6': (hi_b[l].grad.norm(-1) > 1e-6).float().mean().item() if hi_b[l].grad is not None else None,
            } for l in range(L)]
        },
        'method_C_embed_grad_norm': embed_grad_norm,
    }

    out = os.path.join(args.output_dir, f'{args.method}_s{args.seed}.json')
    with open(out, 'w') as f:
        json.dump(result, f, indent=2)
    print(f"  Saved to {out}", flush=True)

if __name__ == '__main__':
    main()