Blame for code/mteb_lite_release.py

#!/usr/bin/env python
"""Build the public, ID-only MTEB-lite release folder (Hugging Face dataset repo ADIP-QCRI/MTEB-lite).

  python scripts/mteb_lite_release.py build      [--out outputs/hf_upload/MTEB-lite]   specs, manifests, docs, code
  python scripts/mteb_lite_release.py roundtrip  [--jobs 16]   reconstruct every task with mteb_lite.py from the
                                                 original datasets and compare with our local pools (texts must match)
  python scripts/mteb_lite_release.py stats      texts / tokens / est. encode minutes per config (Qwen3 tokenizer)

Configs: eng (data/mteb_lite_v2, K=100), eng-super (data/mteb_lite_super_v2, K=20); ara / ara-super slot in the
same way (CONFIGS) once the Arabic selection exists. No text from any dataset is written: only ids, row indices,
qrels / label weights needed for scoring, selection weights and calibration coefficients.
"""
from __future__ import annotations

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

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

REPO = L.REPO
OUT = REPO / "outputs" / "hf_upload" / "MTEB-lite"
ALL_CONFIGS = {  # config -> (suite, selection dir, validation report stem, selection version)
    "eng@v2": ("eng", REPO / "data" / "mteb_lite_v2", REPO / "outputs" / "mteb_lite" / "selection_report_v2", "v2"),
    "eng-super@v2": ("eng", REPO / "data" / "mteb_lite_super_v2", REPO / "outputs" / "mteb_lite" / "selection_report_v2_super", "v2"),
    "eng@v3": ("eng", REPO / "data" / "mteb_lite_v3", REPO / "outputs" / "mteb_lite" / "selection_report_v3", "v3"),
    "eng-super@v3": ("eng", REPO / "data" / "mteb_lite_super_v3", REPO / "outputs" / "mteb_lite" / "selection_report_v3_super", "v3"),
    "ara@v3": ("ara", REPO / "data" / "mteb_lite_ar", REPO / "outputs" / "mteb_lite_ar" / "selection_report", "v3"),
    "ara-super@v3": ("ara", REPO / "data" / "mteb_lite_ar_super", REPO / "outputs" / "mteb_lite_ar" / "selection_report_super", "v3"),
    "ara@v4": ("ara", REPO / "data" / "mteb_lite_ar_v4", REPO / "outputs" / "mteb_lite_ar" / "selection_report_v4", "v4"),
    "ara-super@v4": ("ara", REPO / "data" / "mteb_lite_ar_super_v4", REPO / "outputs" / "mteb_lite_ar" / "selection_report_v4_super", "v4"),
}
CONFIGS: dict = {}     # config -> (suite, selection dir, report stem, version); set by set_configs()


def set_configs(names: list[str], versions: dict) -> None:
    """versions: {config: selection version} (default v3 for ara*, v2 for eng*)."""
    CONFIGS.clear()
    for n in names:
        v = versions.get(n, "v3" if n.startswith("ara") else "v2")
        CONFIGS[n] = ALL_CONFIGS[f"{n}@{v}"]


BENCH = {"eng": "MTEB(eng, v2)", "ara": "Arabic MMTEB (42 test tasks)"}
TOK_PER_S = 30000          # measured: Qwen3-Embedding-0.6B in our MTEB runs, 25-33k tokens/s on one GPU
CODE = ["mteb_lite_lib.py", "mteb_lite_pool.py", "mteb_lite_score.py", "mteb_lite_score_merges.py",
        "mteb_lite_select.py", "mteb_lite_select_v2.py", "mteb_lite_eval.py", "mteb_lite_public_full.py",
        "mteb_lite_score_list.sh", "mteb_lite_release.py", "mteb_lite_release_docs.py",
        "mteb_lite_release_models.py"]


def manifest_of(sel_dir: Path, suite: str) -> dict:
    return json.loads((sel_dir / L.SUITES[suite]["manifest"]).read_text())


def task_release_spec(task: str, doc: dict, pool: dict, suite: str) -> dict:
    """ID-only description of one task's selected items (+ everything scoring needs)."""
    t = L.get_task(task)
    rp = L.restrict_pool(pool, doc["ids"])
    ty = pool["type"]
    spec = {"task": task, "type": ty, "main_score": pool["main_score"], "eval_split": pool["eval_split"],
            "hf_subsets": pool["hf_subset"] if isinstance(pool["hf_subset"], list) else [pool["hf_subset"]],
            "dataset": {"path": t.metadata.dataset.get("path"), "revision": t.metadata.dataset.get("revision")},
            "K": len(doc["ids"]), "ids": doc["ids"],
            "estimator": {"mean": "weighted_mean", "pirt": "pirt", "gp": "gp"}.get(doc["estimator"], "set_metric"),
            "calibration": doc["calibration"], "item_key": L.ITEM_KEY[ty]}
    if suite == "eng":
        spec["benchmark"] = L.BENCHMARK
    else:
        spec["hf_subsets_filter"] = spec["hf_subsets"]
    if doc["estimator"] in ("mean", "pirt", "gp"):
        spec["weights"] = doc["weights"]
        if doc["estimator"] != "mean":      # p-IRT: item ids + item parameters of the whole candidate pool
            spec["irt"] = {"pool_ids": doc["pool_ids"], "pool_weights": doc["pool_weights"],
                           "item_model": doc["item_model"], "theta_scale": doc.get("theta_scale")}
    elif doc["estimator"] != "set_metric":
        raise ValueError(f"{task}: estimator {doc['estimator']} not supported by the release loader")
    if ty == "Reranking" and "ndcg" in str(pool["main_score"]).lower():
        spec["item_key"] = "ndcg10"
    if ty == "Retrieval":
        spec["candidates"] = rp["candidates"]
        spec["qrels"] = rp["qrels"]
        spec["ignore_identical_ids"] = rp.get("ignore_identical_ids", False)
        # content hashes: some mteb loaders number queries / docs via enumerate(set(...)) (e.g. MintakaRetrieval), so
        # their ids change with PYTHONHASHSEED; the loader maps texts by hash and keeps these ids (qrels stay valid)
        import hashlib
        h = lambda x: hashlib.sha1(x.encode("utf-8")).hexdigest()[:16]  # noqa: E731
        spec["text_sha1"] = {"queries": {q: h(v) for q, v in rp["queries"].items()},
                             "docs": {d: h(v) for d, v in rp["corpus"].items()}}
        if "random_docs_by_subset" in rp:
            spec["random_docs_by_subset"] = rp["random_docs_by_subset"]
            spec["query_subset"] = rp["query_subset"]
        else:
            spec["random_docs"] = rp["random_docs"]
    elif ty == "Classification":
        if "groups" in pool:
            groups = {k: {"train_rows": g["train"]["orig_index"], "experiments": g["experiments"]}
                      for k, g in pool["groups"].items()}
        else:
            groups = {"": {"train_rows": pool["train"]["orig_index"], "experiments": pool["experiments"]}}
        spec.update({"groups": groups, "samples_per_label": pool.get("samples_per_label"),
                     "n_experiments": pool.get("n_experiments"), "undersampling_seed": 42,
                     "logreg": {"random_state": 42, "max_iter": 100}})
    elif ty == "PairClassification":
        spec["label_weights"] = pool.get("label_weights")
    elif ty == "Clustering":
        spec["n_levels"] = pool["n_levels"]
        spec["max_depth"] = getattr(t, "max_depth", None)
        spec["multi_set"] = any("subset" in x for x in pool["items"])
        spec["kmeans"] = {"runs": L.KMEANS_RUNS, "random_state": "run index", "n_init": 1, "max_iter": 100}
    elif ty == "BitextMining":
        tgt = {x["id"]: x["target"] for x in rp["items"]}
        spec["targets"] = [tgt[i] for i in doc["ids"]]                 # aligned with spec["ids"]
        spec["bitext_candidates"] = rp["bitext_candidates"]
        spec["parallel"] = pool.get("parallel", False)
    return spec


# ----------------------------------------------------------------------------- build
def build(out: Path) -> None:
    out.mkdir(parents=True, exist_ok=True)
    for cfg, (suite, sel_dir, rep, ver) in CONFIGS.items():
        L.configure(suite)
        man = manifest_of(sel_dir, suite)
        d = out / cfg
        if d.exists():
            shutil.rmtree(d)
        d.mkdir(parents=True)
        tasks, cal_rows = {}, []
        fulls = full_scores_of(suite)
        for task, m in man["tasks"].items():
            doc = json.loads((sel_dir / m["selection"]).read_text())
            spec = task_release_spec(task, doc, L.load_pool(task), suite)
            fit = calib_fit(task, doc, suite, fulls)
            spec["calibration"] = {**spec["calibration"], **fit}
            (d / f"{task}.json").write_text(json.dumps(spec))
            tasks[task] = {"type": spec["type"], "main_score": spec["main_score"], "K": spec["K"],
                           "estimator": spec["estimator"], "calibration": spec["calibration"],
                           "dataset": spec["dataset"], "eval_split": spec["eval_split"], "hf_subsets": spec["hf_subsets"]}
            cal_rows.append({"task": task, "task_type": spec["type"], "main_score": spec["main_score"],
                             "estimator": spec["estimator"], "K": spec["K"], "a": spec["calibration"]["a"],
                             "b": spec["calibration"]["b"], "n_fit_models": fit["n_fit"], "fit_pearson_r": fit["fit_r"],
                             "fit_residual_sd": fit["fit_resid_sd"],
                             "fitted_on": f"models.csv rows with role_{cfg} = selection+calibration+LOMO-held-out "
                                          "(those with a full score for this task)"})
        with open(d / "calibration.csv", "w", newline="") as f:
            w = csv.DictWriter(f, fieldnames=list(cal_rows[0]))
            w.writeheader()
            w.writerows(cal_rows)
        import mteb
        manifest = {"name": "MTEB-lite", "config": cfg, "benchmark": BENCH[suite],
                    "source_benchmark": L.BENCHMARK if suite == "eng" else "MMTEB Arabic subsets",
                    "mteb_version": mteb.__version__, "K": man["K"], "K_set": man.get("K_set", man["K"]),
                    "n_tasks": len(tasks), "aggregate": {"macro": "mean over task types of the mean over tasks (x100)",
                                                         "mean": "mean over tasks (x100)"},
                    "method": man["method"], "selection_version": ver,
                    "n_reference_models": len(man["reference_models"]), "reference_models": man["reference_models"],
                    "calibration": "full_estimate = a + b * subset_statistic per task (see calibration.csv)",
                    "tasks": tasks}
        (d / "manifest.json").write_text(json.dumps(manifest, indent=1))
        vd = out / "validation"
        vd.mkdir(exist_ok=True)
        for ext in (".md", ".json"):        # no internal absolute paths in the public copies
            txt = rep.with_suffix(ext).read_text().replace(str(REPO) + "/", "").replace(str(REPO), ".")
            (vd / f"{cfg}{ext}").write_text(txt)
        print(f"[release] {cfg}: {len(tasks)} tasks -> {d}")
    shutil.copy(HERE / "mteb_lite_hf" / "mteb_lite.py", out / "mteb_lite.py")
    cd = out / "code"
    if cd.exists():
        shutil.rmtree(cd)
    cd.mkdir()
    for f in CODE:
        shutil.copy(HERE / f, cd / f)
    write_models_csv(out)
    write_docs(out)
    for pc in out.rglob("__pycache__"):
        shutil.rmtree(pc, ignore_errors=True)


# ----------------------------------------------------------------------------- calibration / models.csv
_FULLS: dict = {}


def full_scores_of(suite: str) -> dict:
    """{model: {task: full main score}} of every scored model of the suite (as the selection used them)."""
    if suite not in _FULLS:
        import mteb_lite_select as S1
        import mteb_lite_select_v2 as S2
        L.configure(suite)
        names = sorted(p.name for p in L.SCORES.iterdir() if p.is_dir())
        csvp = str(L.LITE / "public_full.csv")
        _FULLS[suite] = S1.full_scores(names, csvp) if suite == "eng" else S2.ara_full_scores(names, csvp)
    return _FULLS[suite]


def calib_fit(task: str, doc: dict, suite: str, fulls: dict) -> dict:
    """Goodness of the per-task linear calibration on the reference models it was fitted on."""
    import numpy as np
    import mteb_lite_select_v2 as S2
    L.configure(suite)
    names = [n for n in doc["reference_models"] if task in fulls.get(n, {})]
    res, embs = [], []
    for n in names:
        r, e = L.load_scores(n, task)
        res.append(r)
        embs.append(e)
    pool = L.load_pool(task)
    ts = L.TaskScores(task, res, embs, pool)
    f = np.array([fulls[n][task] for n in names])
    cols = np.array([ts.ids.index(i) for i in doc["ids"]])
    if doc["estimator"] == "set_metric":
        est = ts.subset(cols, exact=ts.type == "Clustering")
    else:
        Y = np.where(np.isnan(ts.Y), np.nan_to_num(np.nanmean(ts.Y, 0))[None], ts.Y)
        w = np.array(doc["weights"])
        est = (Y[:, cols] * w[None]).sum(1)
        if doc["estimator"] in ("pirt", "gp"):
            model = {k: (np.array(v) if isinstance(v, list) else v) for k, v in doc["item_model"].items()}
            u = np.array(doc["pool_weights"])
            ppos = {i: j for j, i in enumerate(doc["pool_ids"])}
            sel = np.array([ppos[i] for i in doc["ids"]])
            rest = np.ones(len(u), bool)
            rest[sel] = False
            pir = []
            for m in range(len(names)):
                th = S2.map_theta(model, Y[m, cols], sel)
                pir.append((u[sel] * Y[m, cols]).sum() + (u[rest] * S2.predict_items(model, th)[rest]).sum())
            pir = np.array(pir)
            est = pir if doc["estimator"] == "pirt" else 0.5 * (est + pir)
    a, b = doc["calibration"]["a"], doc["calibration"]["b"]
    pred = a + b * est
    ok = ~np.isnan(pred)
    r = float(np.corrcoef(pred[ok], f[ok])[0, 1]) if ok.sum() > 2 and pred[ok].std() > 0 else float("nan")
    return {"n_fit": int(ok.sum()), "fit_r": r, "fit_resid_sd": float(np.std(f[ok] - pred[ok]))}


def write_models_csv(out: Path) -> list[dict]:
    import mteb_lite_release_models as RM
    import mteb_lite_select_v2 as S2
    roles = {}
    for cfg, (suite, sel_dir, rep, ver) in CONFIGS.items():
        r = json.loads(rep.with_suffix(".json").read_text())
        combo = r["choice"].get("combo")
        agg = set((r["aggregate"]["family"].get(combo) or {}).get("models", []))
        roles[cfg] = (suite, set(r["reference"]), agg)
    suites = [su for su in ("eng", "ara") if L.SUITES[su]["out"].joinpath("scores").exists()]   # all scored suites
    scored = {}
    for su in suites:
        L.configure(su)
        scored[su] = {p.name for p in L.SCORES.iterdir() if p.is_dir()}
    fulls = {su: full_scores_of(su) for su in suites}
    names = sorted(set().union(*scored.values()))
    rows = []
    for n in names:
        d = RM.describe(n)
        row = {"internal_id": n, "display_name": d["display_name"], "family": d["family"], "type": d["type"],
               "merge_recipe": d["recipe"],
               "suites_scored": "/".join(su for su in suites if n in scored[su])}
        for su in suites:
            nt = len(fulls[su].get(n, {})) if n in scored[su] else 0
            row[f"full_score_source_{su}"] = ("" if not nt else ("official MTEB results" if d["type"] == "public"
                                                                 else "our runs (mteb 1.39.7)"))
            row[f"tasks_with_full_score_{su}"] = nt
        for cfg, (su, ref, agg) in roles.items():
            if n in ref:
                role = "selection+calibration+LOMO-held-out" + ("+aggregate-validation" if n in agg else "")
            elif n in scored[su] and fulls[su].get(n):
                role = "not used (family cap: <=3 per family)" if S2.family(n) in {S2.family(m) for m in ref} \
                    else "not used"
            elif n in scored[su]:
                role = "not used (no full scores)"
            else:
                role = "-"
            row[f"role_{cfg}"] = role
        rows.append(row)
    with open(out / "models.csv", "w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=list(rows[0]))
        w.writeheader()
        w.writerows(rows)
    print(f"[release] models.csv: {len(rows)} models")
    return rows


# ----------------------------------------------------------------------------- stats / roundtrip
def stats() -> dict:
    from transformers import AutoTokenizer
    tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-0.6B")
    res = {}
    for cfg, (suite, sel_dir, _, _v) in CONFIGS.items():
        L.configure(suite)
        man = manifest_of(sel_dir, suite)
        cap = L.SUITES[suite]["max_length"] or 8192
        n_items = n_texts = n_tok = 0
        per = {}
        for task, m in man["tasks"].items():
            doc = json.loads((sel_dir / m["selection"]).read_text())
            rp = L.restrict_pool(L.load_pool(task), doc["ids"])
            texts = list(dict.fromkeys(L.texts_to_encode(rp)))
            k = 0
            for s in range(0, len(texts), 4096):
                enc = tok([t for t, _ in texts[s:s + 4096]], add_special_tokens=False)["input_ids"]
                k += sum(min(len(e), cap) + (20 if r != "document" else 1) for e, (_, r) in zip(enc, texts[s:s + 4096]))
            per[task] = {"items": len(doc["ids"]), "texts": len(texts), "tokens": k}
            n_items += len(doc["ids"])
            n_texts += len(texts)
            n_tok += k
        res[cfg] = {"items": n_items, "texts": n_texts, "tokens": n_tok, "minutes_0.6B": n_tok / TOK_PER_S / 60,
                    "per_task": per}
        print(f"[release] {cfg}: {n_items} items, {n_texts} texts, {n_tok / 1e6:.2f}M tokens, "
              f"~{n_tok / TOK_PER_S / 60:.1f} min / 0.6B model")
    (OUT / "stats.json").write_text(json.dumps(res, indent=1)) if OUT.exists() else None
    return res


def canon(p: dict) -> dict:
    t = p["type"]
    if t == "Retrieval":
        return {"queries": p["queries"], "corpus": p["corpus"], "candidates": p["candidates"], "qrels": p["qrels"],
                "random": p.get("random_docs", p.get("random_docs_by_subset"))}
    c = {"items": [{k: ([str(v) for v in x[k]] if k == "labels" else (str(x[k]) if k == "label" else x[k]))
                    for k in sorted(x)} for x in sorted(p["items"], key=lambda x: x["id"])]}
    if t == "Classification":
        groups = p["groups"] if "groups" in p else {"": {"train": p["train"], "experiments": p["experiments"]}}
        c["train"] = {k: [list(g["train"]["text"]), [str(v) for v in g["train"]["label"]], g["experiments"]]
                      for k, g in groups.items()}
    if t == "BitextMining":
        c["texts"] = {k: p["bitext_texts"][k] for k in sorted(p["bitext_texts"])}
        c["cands"] = p["bitext_candidates"]
    return c


def roundtrip_one(cfg: str, task: str, out: str) -> dict:
    sys.path.insert(0, out)
    import mteb_lite as ML
    ML.HERE = Path(out)
    suite, sel_dir, _, _v = CONFIGS[cfg]
    L.configure(suite)
    spec = json.loads((Path(out) / cfg / f"{task}.json").read_text())
    rp = L.restrict_pool(L.load_pool(task), spec["ids"])
    try:
        got = ML.load_task(cfg, task, spec)
    except Exception as e:
        import traceback
        return {"cfg": cfg, "task": task, "ok": False, "error": f"{type(e).__name__}: {e}", "tb": traceback.format_exc()[-800:]}
    a, b = canon(rp), canon(got)
    bad = []
    for k in a:
        if a[k] != b.get(k):
            if isinstance(a[k], dict):
                diff = [x for x in a[k] if a[k][x] != b.get(k, {}).get(x)]
                bad.append(f"{k}: {len(diff)} differ, e.g. {diff[:3]}")
            elif isinstance(a[k], list):
                diff = [i for i, (x, y) in enumerate(zip(a[k], b.get(k, []))) if x != y]
                bad.append(f"{k}: {len(diff)} differ (len {len(a[k])} vs {len(b.get(k, []))}), e.g. {diff[:3]}")
            else:
                bad.append(k)
    n = len(rp["queries"]) if rp["type"] == "Retrieval" else len(rp["items"])
    return {"cfg": cfg, "task": task, "ok": not bad, "n": n, "mismatch": bad}


def roundtrip(out: Path, jobs: int, cfgs: list[str]) -> None:
    from concurrent.futures import ProcessPoolExecutor
    work = []
    for cfg in cfgs:
        man = json.loads((out / cfg / "manifest.json").read_text())
        work += [(cfg, t, str(out)) for t in man["tasks"]]
    res = []
    with ProcessPoolExecutor(jobs) as ex:
        for r in ex.map(roundtrip_one, *zip(*work)):
            res.append(r)
            print(f"[roundtrip] {r['cfg']:10s} {r['task']:42s} {'OK' if r['ok'] else 'MISMATCH'} "
                  f"{r.get('n', '')} {r.get('mismatch') or r.get('error') or ''}", flush=True)
    bad = [r for r in res if not r["ok"]]
    (out.parent / "MTEB-lite_roundtrip.json").write_text(json.dumps(res, indent=1))
    print(f"[roundtrip] {len(res) - len(bad)}/{len(res)} tasks identical; mismatches: {len(bad)}")


# ----------------------------------------------------------------------------- docs
NOTES: dict = {}


def write_docs(out: Path) -> None:
    from mteb_lite_release_docs import readme, selection_md
    st = json.loads((out / "stats.json").read_text()) if (out / "stats.json").exists() else {}
    st = {k: v for k, v in st.items() if k in CONFIGS}
    reps = {cfg: json.loads(r.with_suffix(".json").read_text()) for cfg, (_, _, r, _v) in CONFIGS.items()}
    mans = {cfg: json.loads((out / cfg / "manifest.json").read_text()) for cfg in CONFIGS}
    models = list(csv.DictReader(open(out / "models.csv")))
    cal = {}
    for cfg in CONFIGS:
        rows = list(csv.DictReader(open(out / cfg / "calibration.csv")))
        for r in rows:
            for k in ("fit_pearson_r", "fit_residual_sd"):
                r[k] = float(r[k])
        cal[cfg] = rows
    notes = dict(NOTES)
    if any(c.startswith("ara") for c in CONFIGS):
        notes.setdefault("ara", ARA_NOTE)
    (out / "README.md").write_text(readme(st, reps, mans, models, cal, notes))
    (out / "SELECTION.md").write_text(selection_md(reps, mans))


ARA_NOTE = """
**Arabic configs.** These are the 42 Arabic MMTEB test tasks (7 bitext mining, 14 classification, 2 clustering, 2 pair
classification, 2 reranking, 11 retrieval, 4 STS). Each task is restricted to its Arabic hf_subsets (Arabic, Arabic dialects,
and Arabic-English pairs) and to the split our Arabic MMTEB evaluation uses, and the task score is the mean over subsets.
Texts are capped at 512 tokens. The MIRACLReranking pool was cut to 300 queries (encode cost). The full-task scores of the
8 multilingual public reference models (multilingual-e5 small / base / large, bge-m3, LaBSE, embeddinggemma-300m,
paraphrase-multilingual MiniLM-L12 / mpnet-base) are our own complete runs of all 42 tasks with the same subsets and splits;
the English-only public models enter only the tasks with official full scores. Only `ara-super` (K=20) is released: it beats
uniform random K under family-level leave-one-model-out (macro tau 0.732 vs 0.711, mean tau 0.772 vs 0.764). The K=100 Arabic
selection does not yet beat random K (macro tau 0.769 vs 0.778) and is withheld until it does.
"""


def main() -> None:
    global OUT
    ap = argparse.ArgumentParser()
    ap.add_argument("cmd", choices=["build", "roundtrip", "stats", "docs"])
    ap.add_argument("--out", default=str(OUT))
    ap.add_argument("--jobs", type=int, default=8)
    ap.add_argument("--configs", default="eng,eng-super", help="any of eng,eng-super,ara,ara-super")
    ap.add_argument("--versions", default="eng=v2,eng-super=v3",
                    help="selection version per config (eng / eng-super: v2 | v3; ara*: v3 | v4)")
    ap.add_argument("--eng-note", default="", help="README sentence on which English selection version is used and why")
    args = ap.parse_args()
    if args.eng_note:
        NOTES["eng"] = args.eng_note.replace("\\`", "`")
    set_configs(args.configs.split(","), dict(kv.split("=") for kv in args.versions.split(",") if kv))
    OUT = Path(args.out)
    if args.cmd == "build":
        build(OUT)
    elif args.cmd == "stats":
        stats()
        write_docs(OUT)
    elif args.cmd == "docs":
        write_docs(OUT)
    else:
        roundtrip(OUT, args.jobs, list(CONFIGS))


if __name__ == "__main__":
    main()