"""
STER-WM v3 — World-Model few-shot ER with few-shot ADAPTATION (GPU).

Story (unchanged): pretrain a source/realization-invariant identity encoder from FREE
3DBAG multi-LoD self-supervision, then adapt to the harder CityModel<->3DBAG source
gap with only K labels.

v3 method: after InfoNCE pretraining, FINE-TUNE the encoder on the K labelled Hague
pairs with a supervised contrastive loss (pull matched cand-index together, push
mismatches apart). This bridges the LoD-gap -> source-gap domain shift with few labels.

Reports at K in {5,10,20,50}:
  bar_raw_bagging              baseline
  wm_pre_cos                   frozen pretrained encoder, cosine threshold (label-light)
  wm_ft_cos                    fine-tuned encoder, cosine threshold
  wm_ft_hybrid_bagging         raw ratio + fine-tuned WM features -> Bagging  (main)
  ablation_ft_from_scratch     fine-tune from RANDOM init (no multi-LoD pretraining)
"""
import os, json, copy, numpy as np, torch, torch.nn as nn, torch.nn.functional as F
from sklearn.ensemble import BaggingClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score

NPZ = "/root/ster/data/ster_wm_vectors.npz"
OUT = "/root/ster/exp/ster_wm_results.json"
DEV = "cuda" if torch.cuda.is_available() else "cpu"
D = 32
torch.manual_seed(1); np.random.seed(1)

z = np.load(NPZ, allow_pickle=True)
lod12, lod22, candX, indexX = z["lod12_X"], z["lod22_X"], z["cand_X"], z["index_X"]
trP, trY, teP, teY = z["train_pairs"], z["train_y"], z["test_pairs"], z["test_y"]
print(f"multiLoD={lod12.shape} cand={candX.shape} index={indexX.shape} "
      f"train={trP.shape}({trY.sum()}+) test={teP.shape}({teY.sum()}+) dev={DEV}", flush=True)

scaler = StandardScaler().fit(np.vstack([lod12, lod22, candX, indexX]))
def S(x): return torch.tensor(scaler.transform(x), dtype=torch.float32, device=DEV)
L12, L22, CAND, INDEX = S(lod12), S(lod22), S(candX), S(indexX)
DIM = lod12.shape[1]


class Encoder(nn.Module):
    def __init__(s, din, d=D):
        super().__init__()
        s.net = nn.Sequential(nn.Linear(din, 64), nn.GELU(), nn.Linear(64, 64), nn.GELU(), nn.Linear(64, d))
    def forward(s, x): return F.normalize(s.net(x), dim=-1)


def info_nce(za, zb, tau=0.1):
    lg = za @ zb.t() / tau
    lab = torch.arange(za.size(0), device=za.device)
    return 0.5 * (F.cross_entropy(lg, lab) + F.cross_entropy(lg.t(), lab))


def pretrain(epochs=400, bs=256, lr=1e-3):
    enc = Encoder(DIM).to(DEV)
    opt = torch.optim.Adam(enc.parameters(), lr=lr, weight_decay=1e-5)
    n = L12.size(0)
    for ep in range(epochs):
        perm = torch.randperm(n, device=DEV)
        for i in range(0, n, bs):
            idx = perm[i:i + bs]
            if idx.numel() < 8: continue
            loss = info_nce(enc(L12[idx]), enc(L22[idx]))
            opt.zero_grad(); loss.backward(); opt.step()
    enc.eval(); return enc


def finetune(base_enc, pos_c, pos_i, steps=150, lr=3e-4):
    """Supervised contrastive fine-tune on K positive cand-index pairs (in-batch negs)."""
    enc = copy.deepcopy(base_enc).to(DEV); enc.train()
    opt = torch.optim.Adam(enc.parameters(), lr=lr, weight_decay=1e-4)
    a = CAND[pos_c]; b = INDEX[pos_i]
    if a.size(0) < 2:
        enc.eval(); return enc
    for _ in range(steps):
        loss = info_nce(enc(a), enc(b))
        opt.zero_grad(); loss.backward(); opt.step()
    enc.eval(); return enc


@torch.no_grad()
def embed(enc, X): return enc(X).cpu().numpy()


def raw_feats(P):
    a, b = candX[P[:, 0]], indexX[P[:, 1]]
    with np.errstate(divide='ignore', invalid='ignore'):
        r = np.where(b != 0, a / b, 1000.0)
    return np.clip(np.round(r, 3), None, 1000.0)


def wm_feats(Ec, Ei, P):
    a, b = Ec[P[:, 0]], Ei[P[:, 1]]
    cos = np.sum(a * b, 1, keepdims=True)
    l2 = -np.linalg.norm(a - b, axis=1, keepdims=True)
    return np.concatenate([cos, l2], 1), cos.ravel()


print("\nPretraining WM encoder (InfoNCE on multi-LoD)...", flush=True)
enc_pre = pretrain()
Ec_pre, Ei_pre = embed(enc_pre, CAND), embed(enc_pre, INDEX)
_, cos_pre_te = wm_feats(Ec_pre, Ei_pre, teP)
_, cos_pre_tr = wm_feats(Ec_pre, Ei_pre, trP)
raw_tr, raw_te = raw_feats(trP), raw_feats(teP)

Ks, NDRAW = [5, 10, 20, 50], 15
pos = np.where(trY == 1)[0]; neg = np.where(trY == 0)[0]
report = {}
for K in Ks:
    acc = {k: [] for k in ["bar_raw_bagging", "wm_pre_cos", "wm_ft_cos",
                           "wm_ft_hybrid_bagging", "ablation_ft_from_scratch"]}
    for rep in range(NDRAW):
        r = np.random.RandomState(300 + rep)
        pidx = r.choice(pos, K, replace=False); nidx = r.choice(neg, min(2 * K, len(neg)), replace=False)
        idx = np.concatenate([pidx, nidx]); y = trY[idx]
        # baseline
        acc["bar_raw_bagging"].append(f1_score(teY, BaggingClassifier(50, random_state=1).fit(raw_tr[idx], y).predict(raw_te), zero_division=0))
        # pretrained cosine threshold
        thr = np.median([np.quantile(cos_pre_tr[idx][y == 1], .2) if (y == 1).any() else .5,
                         np.quantile(cos_pre_tr[idx][y == 0], .8) if (y == 0).any() else .5])
        acc["wm_pre_cos"].append(f1_score(teY, (cos_pre_te >= thr).astype(int), zero_division=0))
        # fine-tune on the K positive pairs
        pc, pi = trP[pidx][:, 0], trP[pidx][:, 1]
        enc_ft = finetune(enc_pre, pc, pi)
        Ec, Ei = embed(enc_ft, CAND), embed(enc_ft, INDEX)
        wmf_tr, cos_tr = wm_feats(Ec, Ei, trP); wmf_te, cos_te = wm_feats(Ec, Ei, teP)
        thr2 = np.median([np.quantile(cos_tr[idx][y == 1], .2) if (y == 1).any() else .5,
                          np.quantile(cos_tr[idx][y == 0], .8) if (y == 0).any() else .5])
        acc["wm_ft_cos"].append(f1_score(teY, (cos_te >= thr2).astype(int), zero_division=0))
        hyb_tr = np.concatenate([raw_tr, wmf_tr], 1); hyb_te = np.concatenate([raw_te, wmf_te], 1)
        acc["wm_ft_hybrid_bagging"].append(f1_score(teY, BaggingClassifier(50, random_state=1).fit(hyb_tr[idx], y).predict(hyb_te), zero_division=0))
        # ablation: fine-tune from scratch (no pretraining)
        enc_sc = finetune(Encoder(DIM).to(DEV), pc, pi)
        Esc_c, Esc_i = embed(enc_sc, CAND), embed(enc_sc, INDEX)
        _, cos_sc_te = wm_feats(Esc_c, Esc_i, teP); _, cos_sc_tr = wm_feats(Esc_c, Esc_i, trP)
        thr3 = np.median([np.quantile(cos_sc_tr[idx][y == 1], .2) if (y == 1).any() else .5,
                          np.quantile(cos_sc_tr[idx][y == 0], .8) if (y == 0).any() else .5])
        acc["ablation_ft_from_scratch"].append(f1_score(teY, (cos_sc_te >= thr3).astype(int), zero_division=0))
    report[K] = {k: dict(f1=round(float(np.mean(v)), 4), std=round(float(np.std(v)), 4)) for k, v in acc.items()}
    print(f"\n=== K={K} ===", flush=True)
    for k, s in sorted(report[K].items(), key=lambda kv: -kv[1]['f1']):
        print(f"   {k:28s} F1={s['f1']:.4f} ±{s['std']:.4f}", flush=True)

os.makedirs(os.path.dirname(OUT), exist_ok=True)
json.dump(report, open(OUT, "w"), indent=2)
print("\nSaved ->", OUT, flush=True)