#!/usr/bin/env python
"""MTEB-lite (eng), step C: choose K items per task so the subset ranks models like full MTEB(eng, v2).

Inputs: per-item scores of M models (outputs/mteb_lite/scores/<NAME>/<task>.json, mteb_lite_score.py) and
the FULL main score per task of those models, looked up by NAME in
  outputs/mteb_en/<NAME>/summary_en.json, outputs/merge_sweep/scores*/<NAME>/mteb_en/summary_en.json,
  --full-csv (columns name,task,score; score in [0,1] or [0,100]),
falling back to the model's pool-level score (flagged) if a full score is unknown.

Per task:
  * item outcome across models, discretised into --bins fixed-width bins; zero-entropy items dropped
    (STS: |percentile(cos) - percentile(gold)|, a per-pair rank error; others: their per-item score);
  * relevance = MI(item bin; model full-score rank bin) (or |Spearman| with --relevance spearman);
  * per-item tasks (retrieval, reranking, classification, summarization; task score = mean over items):
    greedy mRMR, score = relevance - mean MI with the already selected items;
  * set-metric tasks (STS Spearman, clustering V-measure, pair-classification AP): greedy forward
    selection maximising Kendall tau (+ 0.1 Pearson as tie-break) between the subset task score and the
    full task score across models, started from the top mRMR items;
  * linear calibration full ~ a + b * subset score, fitted across models.
Validation (--lomo): leave-one-model-out — select + calibrate on M-1 models, predict the held-out model's
full score from its subset score; per task and for the 41-task macro (mean over the 8 types) / mean:
Kendall tau, Pearson, MAE. Baselines: K random items, and the whole pool (both calibrated the same way).

  python scripts/mteb_lite_select.py [--models a,b,c] [--full-csv f.csv] [--K 100] [--lomo] [--jobs 64]
Writes data/mteb_lite/selection/<task>.json, data/mteb_lite/MTEB_lite_eng.json,
outputs/mteb_lite/selection_report.{md,json}.
"""
from __future__ import annotations

import argparse
import csv
import json
import sys
import time
from collections import defaultdict
from pathlib import Path

import numpy as np

HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE))
import mteb_lite_lib as L  # noqa: E402

REPO = L.REPO
SET_TYPES = L.SET_TYPES


# ----------------------------------------------------------------------------- full scores
def full_scores(names: list[str], csv_path: str | None) -> dict[str, dict[str, float]]:
    out: dict[str, dict[str, float]] = defaultdict(dict)
    for n in names:
        cands = [REPO / "outputs" / "mteb_en" / n / "summary_en.json"]
        cands += sorted((REPO / "outputs" / "merge_sweep").glob(f"scores*/{n}/mteb_en/summary_en.json"))
        for p in cands:
            if p.exists():
                for r in json.loads(p.read_text())["rows"]:
                    out[n].setdefault(r["task"], float(r["main_score"]))
    if csv_path:
        with open(csv_path) as f:
            for r in csv.DictReader(f):
                v = float(r["score"])
                out[r["name"]][r["task"]] = v / 100 if v > 1.5 else v
    return out


# ----------------------------------------------------------------------------- information measures
def discretise(Y: np.ndarray, typ: str, bins: int) -> np.ndarray:
    lo, hi = (-1.0, 1.0) if typ == "Summarization" else (0.0, 1.0)
    Z = np.clip((np.nan_to_num(Y, nan=lo) - lo) / (hi - lo), 0, 1)
    return np.minimum((Z * bins).astype(int), bins - 1)


def rank_bins(f: np.ndarray, bins: int) -> np.ndarray:
    from scipy.stats import rankdata
    b = min(bins, len(f))
    r = rankdata(f, method="ordinal") - 1
    return np.minimum((r * b / len(f)).astype(int), b - 1)


def entropy_cols(B: np.ndarray, nb: int) -> np.ndarray:
    P = np.stack([(B == k).mean(0) for k in range(nb)], 1)
    with np.errstate(divide="ignore", invalid="ignore"):
        return -np.nansum(np.where(P > 0, P * np.log(P), 0), 1)


def mi_with(B: np.ndarray, t: np.ndarray, nb: int, nt: int) -> np.ndarray:
    """MI (nats) of every column of B (models x items, ints < nb) with the vector t (ints < nt)."""
    M = B.shape[0]
    OH = np.stack([(B == k) for k in range(nb)], 2).astype(float)       # M x N x nb
    T = np.stack([(t == k) for k in range(nt)], 1).astype(float)        # M x nt
    J = np.einsum("mia,mb->iab", OH, T) / M                             # N x nb x nt
    pa, pb = J.sum(2, keepdims=True), J.sum(1, keepdims=True)
    with np.errstate(divide="ignore", invalid="ignore"):
        v = np.where(J > 0, J * np.log(J / (pa * pb)), 0)
    return v.sum((1, 2))


def item_outcomes(ts: L.TaskScores) -> np.ndarray:
    """Per-item outcome (models x items) in [0,1] ([-1,1] for summarization) used for entropy / MI."""
    if ts.type == "STS":
        from scipy.stats import rankdata
        pc = (rankdata(ts.Y, axis=1) - 1) / max(ts.N - 1, 1)
        pg = (rankdata(ts.gold) - 1) / max(ts.N - 1, 1)
        return 1 - np.abs(pc - pg[None])
    return ts.Y


def kendall(a, b) -> float:
    from scipy.stats import kendalltau
    a, b = np.asarray(a, float), np.asarray(b, float)
    if len(a) < 2 or np.all(a == a[0]) or np.all(b == b[0]) or np.isnan(a).any():
        return float("nan")
    return float(kendalltau(a, b)[0])


def pearson(a, b) -> float:
    a, b = np.asarray(a, float), np.asarray(b, float)
    if len(a) < 2 or a.std() == 0 or b.std() == 0 or np.isnan(a).any():
        return float("nan")
    return float(np.corrcoef(a, b)[0, 1])


def spearman_cols(Y: np.ndarray, f: np.ndarray) -> np.ndarray:
    from scipy.stats import rankdata
    R = rankdata(np.nan_to_num(Y), axis=0)
    rf = rankdata(f)
    R = R - R.mean(0)
    rf = rf - rf.mean()
    den = np.sqrt((R ** 2).sum(0) * (rf ** 2).sum())
    with np.errstate(divide="ignore", invalid="ignore"):
        return np.where(den > 0, (R * rf[:, None]).sum(0) / den, 0)


# ----------------------------------------------------------------------------- selection
def select(ts: L.TaskScores, f: np.ndarray, models: np.ndarray, K: int, bins: int, relevance: str,
           n_cand: int, seed: int = 0) -> tuple[list[int], dict]:
    """Indices (into ts.ids) of K items chosen using only `models` (rows)."""
    O = item_outcomes(ts)[models]
    fm = f[models]
    B = discretise(O, ts.type, bins)
    H = entropy_cols(B, bins)
    tb = rank_bins(fm, bins)
    nt = int(tb.max()) + 1
    mi = mi_with(B, tb, bins, nt)
    sp = np.abs(spearman_cols(O, fm))
    rel = mi if relevance == "mi" else sp
    rel = rel + 1e-3 * sp                                 # tie-break on |Spearman|
    alive = np.where(H > 1e-12)[0]
    if len(alive) < K:                                    # not enough informative items: pad with the rest
        alive = np.arange(ts.N)
    stats = {"entropy": H, "mi": mi, "spearman": sp}
    if len(alive) <= K and ts.type not in SET_TYPES:
        return sorted(alive.tolist()), stats
    # --- mRMR
    OH = np.stack([(B == k) for k in range(bins)], 2).astype(float)      # Mt x N x bins
    Mt = B.shape[0]
    red = np.zeros(ts.N)
    chosen: list[int] = []
    k_mrmr = K if ts.type not in SET_TYPES else min(10, K)
    avail = np.zeros(ts.N, bool)
    avail[alive] = True
    for step in range(min(k_mrmr, int(avail.sum()))):
        sc = rel - (red / step if step else 0)
        sc = np.where(avail, sc, -np.inf)
        j = int(np.argmax(sc))
        chosen.append(j)
        avail[j] = False
        J = np.einsum("mia,mb->iab", OH, OH[:, j, :]) / Mt                 # N x bins x bins
        pa, pb = J.sum(2, keepdims=True), J.sum(1, keepdims=True)
        with np.errstate(divide="ignore", invalid="ignore"):
            red += np.where(J > 0, J * np.log(J / (pa * pb)), 0).sum((1, 2))
    if ts.type not in SET_TYPES:
        return sorted(chosen), stats
    # --- greedy forward selection on the set metric (rank agreement with the full score)
    rng = np.random.RandomState(seed)
    pool_idx = alive if len(alive) >= K else np.arange(ts.N)
    avail = np.zeros(ts.N, bool)
    avail[pool_idx] = True
    avail[chosen] = False
    while len(chosen) < K and avail.any():
        cands = np.where(avail)[0]
        if len(cands) > n_cand:
            cands = rng.choice(cands, n_cand, replace=False)
        best, bj = -np.inf, None
        for c in cands:
            s = ts.subset(np.array(chosen + [int(c)]), models)
            if np.isnan(s).any():
                continue
            obj = np.nan_to_num(kendall(s, fm)) + 0.1 * np.nan_to_num(pearson(s, fm))
            if obj > best:
                best, bj = obj, int(c)
        if bj is None:
            bj = int(cands[0])
        chosen.append(bj)
        avail[bj] = False
    return sorted(chosen), stats


def calibrate(s: np.ndarray, f: np.ndarray) -> tuple[float, float]:
    ok = ~(np.isnan(s) | np.isnan(f))
    s, f = s[ok], f[ok]
    if len(s) >= 3 and s.std() > 1e-9:
        b, a = np.polyfit(s, f, 1)
        return float(a), float(b)
    return float(np.mean(f - s)) if len(s) else 0.0, 1.0


# ----------------------------------------------------------------------------- jobs
_CACHE: dict = {}


def task_scores(task: str, names: list[str]) -> L.TaskScores:
    key = (task, tuple(names))
    if key not in _CACHE:
        _CACHE.clear()
        res, embs = [], []
        for n in names:
            r, e = L.load_scores(n, task)
            res.append(r)
            embs.append(e)
        _CACHE[key] = L.TaskScores(task, res, embs, L.load_pool(task))
    return _CACHE[key]


def run_job(task: str, names: list[str], f: list[float], src: list[str], holdout: int | None, args) -> dict:
    ts = task_scores(task, names)
    f = np.asarray(f, float)
    M = ts.M
    # fit only on models with a real full score (pool-score fallback only if fewer than 3 have one)
    real = [m for m in range(M) if src[m] == "full" and m != holdout]
    train = np.array(real if len(real) >= 3 else [m for m in range(M) if m != holdout])
    t0 = time.time()
    sel, stats = select(ts, f, train, args.K, args.bins, args.relevance, args.n_cand)
    out = {"task": task, "holdout": holdout, "sel": sel, "seconds": time.time() - t0}
    exact = ts.type == "Clustering"
    s_all = ts.subset(np.array(sel), exact=exact)
    a, b = calibrate(s_all[train], f[train])
    out.update({"subset": s_all.tolist(), "cal": [a, b], "pred": (a + b * s_all).tolist()})
    if holdout is None:
        out["stats"] = {k: v.tolist() for k, v in stats.items()}
    else:
        rng = np.random.RandomState(1000 + holdout)
        preds = []
        for _ in range(args.n_random_baseline):
            cols = np.sort(rng.choice(ts.N, min(args.K, ts.N), replace=False))
            s = ts.subset(cols, exact=exact)
            ar, br = calibrate(s[train], f[train])
            preds.append(ar + br * s[holdout])
        out["pred_random"] = float(np.nanmean(preds))
        ap_, bp_ = calibrate(ts.pool_scores[train], f[train])
        out["pred_pool"] = float(ap_ + bp_ * ts.pool_scores[holdout])
    return out


def metrics(pred: np.ndarray, true: np.ndarray) -> dict:
    ok = ~(np.isnan(pred) | np.isnan(true))
    p, t = pred[ok], true[ok]
    return {"kendall": kendall(p, t), "pearson": pearson(p, t),
            "mae": float(np.mean(np.abs(p - t))) if len(p) else float("nan"), "n": int(ok.sum())}


def aggregate(scores: dict[str, float], types: dict[str, str]) -> tuple[float, float]:
    by = defaultdict(list)
    for t, v in scores.items():
        by[types[t]].append(100 * v)
    macro = float(np.mean([np.mean(v) for v in by.values()]))
    mean = float(np.mean([100 * v for v in scores.values()]))
    return macro, mean


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--scores-dir", default=str(L.SCORES))
    ap.add_argument("--models", default=None, help="comma list of score dirs (default: all)")
    ap.add_argument("--tasks", default=None)
    ap.add_argument("--full-csv", default=None)
    ap.add_argument("--K", type=int, default=100)
    ap.add_argument("--bins", type=int, default=5)
    ap.add_argument("--relevance", choices=["mi", "spearman"], default="mi")
    ap.add_argument("--n-cand", type=int, default=200, help="set-metric greedy: candidates tried per step")
    ap.add_argument("--n-random-baseline", type=int, default=5)
    ap.add_argument("--lomo", action="store_true")
    ap.add_argument("--jobs", type=int, default=1)
    ap.add_argument("--out", default=str(L.LITE))
    ap.add_argument("--report", default=str(REPO / "outputs" / "mteb_lite" / "selection_report"))
    args = ap.parse_args()
    L.SCORES = Path(args.scores_dir)
    names = sorted(args.models.split(",")) if args.models else sorted(
        p.name for p in L.SCORES.iterdir() if p.is_dir())
    types = {n: L.get_task(n).metadata.type for n in L.task_names()}
    tasks = args.tasks.split(",") if args.tasks else [t for t in L.task_names() if L.pool_path(t).exists()]
    fulls = full_scores(names, args.full_csv)
    jobs, info = [], {}
    for t in tasks:
        have = [n for n in names if (L.SCORES / n / f"{t}.json").exists()]
        if len(have) < 3:
            print(f"[select] {t}: only {len(have)} models scored - skipped", flush=True)
            continue
        f, src = [], []
        for n in have:
            if t in fulls.get(n, {}):
                f.append(fulls[n][t])
                src.append("full")
            else:
                f.append(json.loads((L.SCORES / n / f"{t}.json").read_text())["pool_score"])
                src.append("pool")
        info[t] = {"names": have, "f": f, "src": src}
        jobs.append((t, have, f, src, None))
        if args.lomo and src.count("full") >= 4:
            jobs += [(t, have, f, src, m) for m in range(len(have)) if src[m] == "full"]
    print(f"[select] {len(info)} tasks, {len(names)} models, {len(jobs)} jobs", flush=True)
    results = []
    if args.jobs > 1:
        from concurrent.futures import ProcessPoolExecutor
        jobs.sort(key=lambda j: (types[j[0]] not in SET_TYPES, j[0]))
        with ProcessPoolExecutor(args.jobs) as ex:
            futs = [ex.submit(run_job, *j, args) for j in jobs]
            for fu in futs:
                results.append(fu.result())
    else:
        results = [run_job(*j, args) for j in jobs]
    final = {r["task"]: r for r in results if r["holdout"] is None}
    lomo = defaultdict(dict)
    for r in results:
        if r["holdout"] is not None:
            lomo[r["task"]][r["holdout"]] = r

    out = Path(args.out)
    (out / "selection").mkdir(parents=True, exist_ok=True)
    manifest = {"benchmark": "MTEB-lite (eng)", "source_benchmark": L.BENCHMARK, "K": args.K,
                "models": names, "method": {"bins": args.bins, "relevance": args.relevance,
                                            "per_item": "mRMR", "set_metric": "greedy Kendall tau"},
                "tasks": {}}
    report = {"per_task": {}, "aggregate": {}, "args": vars(args)}
    for t, r in final.items():
        ts = task_scores(t, info[t]["names"])
        ids = [ts.ids[j] for j in r["sel"]]
        st = r["stats"]
        sel_doc = {"task": t, "type": types[t], "K": len(ids), "ids": ids, "calibration": r["cal"],
                   "n_pool_items": ts.N, "n_informative": int(np.sum(np.array(st["entropy"]) > 1e-12)),
                   "items": [{"id": ts.ids[j], "entropy": st["entropy"][j], "mi": st["mi"][j],
                              "abs_spearman": st["spearman"][j]} for j in r["sel"]],
                   "models": info[t]["names"], "full_score_source": info[t]["src"]}
        (out / "selection" / f"{t}.json").write_text(json.dumps(sel_doc, indent=1))
        manifest["tasks"][t] = {"type": types[t], "main_score": L.get_task(t).metadata.main_score,
                                "ids": ids, "calibration": {"a": r["cal"][0], "b": r["cal"][1]},
                                "subset_metric": "re-clustered V-measure" if types[t] == "Clustering" else
                                ("Spearman(cos, gold)" if types[t] == "STS" else
                                 ("weighted AP(cos)" if types[t] == "PairClassification" else
                                  f"mean {L.ITEM_KEY[types[t]]}"))}
        f = np.array(info[t]["f"])
        real = np.array([x == "full" for x in info[t]["src"]])
        row = {"type": types[t], "n_models": len(f), "n_full": int(real.sum()),
               "in_sample": metrics(np.array(r["pred"])[real], f[real]) if real.sum() >= 2 else {},
               "subset_vs_full_raw": {"kendall": kendall(np.array(r["subset"])[real], f[real]),
                                      "pearson": pearson(np.array(r["subset"])[real], f[real])},
               "pool_vs_full_raw": {"kendall": kendall(ts.pool_scores[real], f[real]),
                                    "pearson": pearson(ts.pool_scores[real], f[real])}}
        if lomo.get(t):
            hs = sorted(lomo[t])
            pt = np.array([lomo[t][m]["pred"][m] for m in hs])
            tt = f[hs]
            row["lomo"] = metrics(pt, tt)
            row["lomo_random"] = metrics(np.array([lomo[t][m]["pred_random"] for m in hs]), tt)
            row["lomo_pool"] = metrics(np.array([lomo[t][m]["pred_pool"] for m in hs]), tt)
        report["per_task"][t] = row
    (out / "MTEB_lite_eng.json").write_text(json.dumps(manifest, indent=1))

    # aggregate (models with LOMO predictions on every task)
    if lomo and len(final) == len(info):
        agg = {"lomo": defaultdict(list), "lomo_random": defaultdict(list), "lomo_pool": defaultdict(list),
               "true": defaultdict(list)}
        for n in names:
            if not all(n in info[t]["names"] for t in final):
                continue
            idx = {t: info[t]["names"].index(n) for t in final}
            if not all(idx[t] in lomo[t] for t in final):
                continue
            tru = {t: info[t]["f"][idx[t]] for t in final}
            for key, fn in (("lomo", lambda t: lomo[t][idx[t]]["pred"][idx[t]]),
                            ("lomo_random", lambda t: lomo[t][idx[t]]["pred_random"]),
                            ("lomo_pool", lambda t: lomo[t][idx[t]]["pred_pool"])):
                ma, me = aggregate({t: fn(t) for t in final}, types)
                agg[key]["macro"].append(ma)
                agg[key]["mean"].append(me)
            ma, me = aggregate(tru, types)
            agg["true"]["macro"].append(ma)
            agg["true"]["mean"].append(me)
            agg["true"]["names"].append(n)
        for key in ("lomo", "lomo_random", "lomo_pool"):
            report["aggregate"][key] = {k: metrics(np.array(agg[key][k]), np.array(agg["true"][k]))
                                        for k in ("macro", "mean")} if agg["true"]["macro"] else {}
        report["aggregate"]["models"] = agg["true"]["names"]
    rp = Path(args.report)
    rp.parent.mkdir(parents=True, exist_ok=True)
    rp.with_suffix(".json").write_text(json.dumps(report, indent=1, default=float))
    rp.with_suffix(".md").write_text(render(report, manifest))
    print(render(report, manifest))


def render(report: dict, manifest: dict) -> str:
    def f3(x):
        return "-" if x is None or (isinstance(x, float) and np.isnan(x)) else f"{x:.3f}"
    lines = [f"# MTEB-lite (eng) selection report", "",
             f"K={manifest['K']} items/task, {len(manifest['models'])} models: {', '.join(manifest['models'])}", "",
             "| task | type | models (full) | LOMO tau | LOMO r | LOMO MAE | random tau | random MAE | pool tau | pool MAE | in-sample tau |",
             "|---|---|---|---|---|---|---|---|---|---|---|"]
    for t, r in report["per_task"].items():
        lo, ra, po = r.get("lomo", {}), r.get("lomo_random", {}), r.get("lomo_pool", {})
        lines.append(f"| {t} | {r['type']} | {r['n_models']} ({r['n_full']}) | {f3(lo.get('kendall'))} | "
                     f"{f3(lo.get('pearson'))} | {f3(lo.get('mae'))} | {f3(ra.get('kendall'))} | {f3(ra.get('mae'))} | "
                     f"{f3(po.get('kendall'))} | {f3(po.get('mae'))} | {f3(r['in_sample'].get('kendall'))} |")
    if report["aggregate"]:
        lines += ["", f"## {len(report['per_task'])}-task aggregate (leave-one-model-out; scores x100)", "",
                  "| estimate | macro tau | macro r | macro MAE | mean tau | mean r | mean MAE |", "|---|---|---|---|---|---|---|"]
        for key in ("lomo", "lomo_random", "lomo_pool"):
            a = report["aggregate"].get(key) or {}
            if a:
                lines.append(f"| {key} | {f3(a['macro']['kendall'])} | {f3(a['macro']['pearson'])} | "
                             f"{f3(a['macro']['mae'])} | {f3(a['mean']['kendall'])} | {f3(a['mean']['pearson'])} | "
                             f"{f3(a['mean']['mae'])} |")
    return "\n".join(lines) + "\n"


if __name__ == "__main__":
    main()