"""per-class gene knockouts: zero each candidate gene, remeasure prototype cosine."""
from __future__ import annotations
from pathlib import Path
import warnings, json, pickle, sys, numpy as np, pandas as pd
warnings.filterwarnings("ignore")
import torch
import anndata as ad
from pathlib import Path as _P_root
ROOT = _P_root(__file__).resolve().parents[2]
ROOT_STR = str(ROOT)
sys.path.insert(0, ROOT_STR)
from panda import PANDAEncoder

CKPT_ROOT = Path(f"{ROOT_STR}/checkpoints")
CORP_ROOT = Path(f"{ROOT_STR}/data/corpus")
OUT = Path(f"{ROOT_STR}/discovery"); OUT.mkdir(exist_ok=True)
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
TOP_KO_GENES = 100  # only test top-100 attributed genes for KO


def compute_ko(sys):
    print(f"\n===== {sys} =====", flush=True)
    stats = np.load(CORP_ROOT / sys / "harmonized/corpus_stats.npz", allow_pickle=True)
    hvgs = [str(g) for g in stats["shared_hvgs"]]
    pca = pickle.load(open(CORP_ROOT / sys / "harmonized/pca_basis.pkl", "rb"))
    mu = np.asarray(stats["mean"], dtype=np.float32)
    sig = np.asarray(stats["std"], dtype=np.float32)

    ck = torch.load(CKPT_ROOT / sys / "marker" / "panda_final.pt", map_location=DEVICE, weights_only=False)
    classes = ck["classes"]
    model = PANDAEncoder(variant="marker", n_pca=50, n_markers=len(ck.get("marker_genes",[])), n_classes=len(classes), n_sub=3,
                         n_datasets=len(ck["datasets"])).to(DEVICE).eval()
    model.load_state_dict(ck["model"])
    protos = torch.from_numpy(ck["prototypes"]).to(DEVICE)
    protos = protos / (protos.norm(dim=1, keepdim=True) + 1e-8)

    import scipy.sparse as sp
    import scanpy as sc
    corp = ad.read_h5ad(CORP_ROOT / sys / "harmonized/corpus.h5ad")
    label_key = "canonical_label" if "canonical_label" in corp.obs else "cell_type"
    y = corp.obs[label_key].astype(str).values
    hvg2i = {g: i for i, g in enumerate(hvgs)}
    common = [g for g in corp.var_names.astype(str) if g in hvg2i]
    a = corp[:, common].copy()
    sc.pp.normalize_total(a, target_sum=1e4); sc.pp.log1p(a)
    X = a.X.toarray().astype(np.float32) if sp.issparse(a.X) else a.X.astype(np.float32)
    Xf = np.zeros((corp.n_obs, len(hvgs)), dtype=np.float32)
    cols = np.array([hvg2i[g] for g in common])
    Xf[:, cols] = X

    att = np.load(OUT / f"80_{sys}_gene_attribution_full.npy")  # (K, G)

    rows = []
    for ci, cls_name in enumerate(classes):
        mask = y == cls_name
        if mask.sum() == 0:
            print(f"[!!] {cls_name}: no cells in corpus", flush=True)
            continue
        x_gene_log_mean = Xf[mask].mean(axis=0)
        x_z = np.clip((x_gene_log_mean - mu) / sig, -10, 10)
        x_pca0 = pca.transform(x_z.reshape(1, -1))[0].astype(np.float32)

        with torch.no_grad():
            xt = torch.from_numpy(x_pca0).unsqueeze(0).to(DEVICE)
            aux = torch.zeros(1, 2, device=DEVICE)
            out = model(xt, aux, lam_dann=0.0)
            z0 = out["z"]
            s0 = float((z0 * protos[ci].unsqueeze(0)).sum())

        att_c = att[ci]
        cand_idx = np.argsort(-np.abs(att_c))[:TOP_KO_GENES]

        # zero each gene in log space, reproject; z is clipped so recompute per KO
        deltas = []
        for gi in cand_idx:
            x_gene_ko = x_gene_log_mean.copy()
            x_gene_ko[gi] = 0.0
            x_z_ko = np.clip((x_gene_ko - mu) / sig, -10, 10)
            x_pca_ko = pca.transform(x_z_ko.reshape(1, -1))[0].astype(np.float32)
            with torch.no_grad():
                xtko = torch.from_numpy(x_pca_ko).unsqueeze(0).to(DEVICE)
                zko = model(xtko, aux, lam_dann=0.0)["z"]
                s_ko = float((zko * protos[ci].unsqueeze(0)).sum())
            deltas.append(s0 - s_ko)  # positive delta = drop when KO'd

        deltas = np.array(deltas)
        rank = np.argsort(-deltas)
        top30 = rank[:30]
        rows.append({
            "class": cls_name,
            "baseline_cos": s0,
            "n_cells_class": int(mask.sum()),
            "top_essential_genes": ",".join([hvgs[cand_idx[r]] for r in top30[:20]]),
            "top_essential_deltas": ",".join([f"{deltas[r]:+.4f}" for r in top30[:20]]),
            "top_essential_baseline_expression": ",".join([f"{x_gene_log_mean[cand_idx[r]]:.2f}" for r in top30[:20]]),
        })
        print(f"[{cls_name}] baseline_cos={s0:.4f}  top-5 essentials: "
              f"{', '.join([f'{hvgs[cand_idx[r]]}(Δ{deltas[r]:+.3f})' for r in top30[:5]])}",
              flush=True)

    df = pd.DataFrame(rows)
    df.to_csv(OUT / f"81_{sys}_ko_essentials.csv", index=False)
    print(f"[wrote] {OUT}/81_{sys}_ko_essentials.csv", flush=True)


for sys in ["pan_skin", "hematopoiesis", "pancreas"]:
    try:
        compute_ko(sys)
    except Exception as e:
        import traceback; traceback.print_exc()
        print(f"[!] {sys} failed: {e}", flush=True)
print("\n=== DONE ===", flush=True)