"""llm_moe figure — union-preserving recombination (route / max-merge) vs fusion (soup / ties).

The real-weight image of E8's *max*: keep every specialist intact and *select* (route per prompt, or
per module) instead of averaging the deltas. Two panels: (A) per-family accuracy for the base, each
specialist, the fusion merges, and the union operators — the union operators should match the best
specialist on every family (they *are* that specialist there), while fusion may dilute or compose;
(B) overall vs worst-family, fusion vs union, with the routing ceiling (moe_oracle) marked. The
suptitle reports whether union beats fusion (dilution regime) or they converge (composition regime).
Reads only the committed bundle.

Usage:  python figures/plot_llm_moe.py [results/llm_moe]
"""

from __future__ import annotations

import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np

sys.path.insert(0, str(Path(__file__).parent))
from _figlib import load_seed_bundles, savefig  # noqa: E402

_FAMS = ["lists", "strings", "arith"]
_FUSION = {"merge_soup": "fuse:soup", "merge_ties": "fuse:ties"}
_UNION = {"moe_oracle": "route:oracle", "moe_learned": "route:learned", "max_merge": "max-merge"}


def _acc(df, model, metric):
    r = df[(df["model"] == model) & (df["metric"] == metric)]["accuracy"]
    return float(r.mean()) if len(r) else float("nan")


def main(results_dir: str = "results/llm_moe") -> None:
    df, cfg = load_seed_bundles(results_dir)   # seed-mean when the bundle has s{seed}/ sub-bundles
    present = set(df["model"].unique())
    specialists = sorted(m for m in present if m.startswith("spec_"))
    fusion = [m for m in _FUSION if m in present]
    union = [m for m in _UNION if m in present]
    models = ["base"] + specialists + fusion + union
    labels = {"base": "base", **{s: s.replace("spec_", "spec:") for s in specialists},
              **_FUSION, **_UNION}
    colors = {"base": "#7f7f7f", **{s: "#1f77b4" for s in specialists},
              **{m: "#ff7f0e" for m in fusion}, **{m: "#2ca02c" for m in union}}

    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    # Panel A: per-family accuracy, grouped by model.
    ax = axes[0]
    x = np.arange(len(_FAMS))
    w = 0.8 / len(models)
    for i, mdl in enumerate(models):
        vals = [_acc(df, mdl, f) for f in _FAMS]
        ax.bar(x + (i - (len(models) - 1) / 2) * w, vals, w, label=labels[mdl], color=colors[mdl],
               alpha=0.9 if (mdl in union or mdl in fusion) else 0.65)
    ax.set_xticks(x); ax.set_xticklabels(_FAMS)
    ax.set(ylabel="accuracy", title="Per-family: fusion (orange) blends the deltas; union (green)\n"
           "keeps each specialist intact and selects — no dilution")
    ax.legend(frameon=False, fontsize=8, ncol=2)

    # Panel B: overall vs worst-family, per model.
    ax = axes[1]
    x2 = np.arange(len(models))
    for off, metric, hatch, lab in [(-0.2, "overall", "", "overall"),
                                    (0.2, "worst_family", "//", "worst family")]:
        ax.bar(x2 + off, [_acc(df, m, metric) for m in models], 0.38,
               color=[colors[m] for m in models], hatch=hatch, alpha=0.85, label=lab,
               edgecolor="white")
    ax.set_xticks(x2); ax.set_xticklabels([labels[m] for m in models], rotation=25, ha="right",
                                          fontsize=8)
    ax.set(ylabel="accuracy", title="Overall (solid) vs worst-family (hatched):\nfusion vs union "
           "recombination")
    # Mark the routing ceiling (oracle) if present.
    if "moe_oracle" in present:
        ceil = _acc(df, "moe_oracle", "overall")
        ax.axhline(ceil, ls=":", c="#2ca02c", lw=1, alpha=0.7)
    ax.legend(frameon=False, fontsize=9)

    best_fuse = max([_acc(df, m, "overall") for m in fusion], default=float("nan"))
    best_union = max([_acc(df, m, "overall") for m in union], default=float("nan"))
    router = _acc(df, "moe_learned", "router_acc") if "moe_learned" in present else float("nan")
    if best_union > best_fuse + 0.01:
        verdict = f"union {best_union:.2f} > fusion {best_fuse:.2f} overall (fusion dilutes)"
    elif best_fuse > best_union + 0.01:
        verdict = f"fusion {best_fuse:.2f} > union {best_union:.2f} overall (strong base composes)"
    else:
        verdict = f"union ≈ fusion ({best_union:.2f} vs {best_fuse:.2f}) overall"
    rtxt = f"; learned router {router:.2f}" if router == router else ""
    fig.suptitle(f"llm_moe — module-level union vs fusion recombination: {verdict}{rtxt} "
                 f"({cfg['base_model'].split('/')[-1]})", y=1.0, fontsize=12)
    fig.tight_layout()
    savefig(fig, results_dir, "llm_moe")


if __name__ == "__main__":
    main(*sys.argv[1:])