"""Floor-to-ceiling method comparison: the keynote figure.

Two stages so the data is inspectable before it's drawn:
  1. build  -> out/plots/floor_ceiling.csv   (one row per arm/anchor, with SOURCE and STATUS
                columns; every provisional/missing value is flagged, not silently filled)
  2. plot   -> out/plots/floor_ceiling.{pdf,png}

Run `uv run python -m scripts.plot_floor_ceiling` to do both; it prints a TODO/FIXME summary
of any provisional or missing cells before plotting.

THE GOAL: place each gradient-routing arm on a floor->ceiling scale so "how much of the
achievable range did it capture" is read at a glance, and show that the quarantine (knob)
is what removes the hack, not a train/test artifact.

TWO METRICS, two anchor pairs (right/down = better):
  hack removed    = (vanilla_hack - arm_hack) / vanilla_hack            1.0 = no hack
  solve recovered = (arm_solve - base_solve)   / (ceiling - base_solve) 1.0 = no-loophole ceiling

TWO VIEWS of the same arms:
  A. normalized floor->ceiling bars, HEADLINE deploy (knob-off, test n=119, recency-clean).
     Source per arm: out/runs/<run>/deploy_test.json.
  B. the KNOB effect: arrow knob-ON -> knob-OFF on the SAME held-out val split (n=32), so it
     isolates the quarantine from the train/test memorization gap. Source per arm:
     out/runs/<run>/eval_curve.jsonl, where the file's `train_*`/`deploy_*` prefixes denote
     KNOB STATE (on/off), not the problem set (always val here). L5 = mean of last 5 evals.

DATA GAPS (see STATUS column in the csv):
  - solve ceiling: provisional = paper 0.223 until job 24 (out/runs/*noloophole*) lands. FIXME.
  - prog_wide arm uses contaminated pairs; job 28 (prog_wide_clean) will replace it. TODO.
  - full-env (paper-scale) panel: no method runs exist, only paper anchors. Out of scope here.
"""
from __future__ import annotations
import json
from pathlib import Path

import polars as pl
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

RED, GREEN, GREY = "#c0392b", "#1e8449", "#9aa0a6"
RUNS = Path("out/runs")
OUT = Path("out/plots")
CSV = OUT / "floor_ceiling.csv"
PAPER_CEILING = 0.223          # Ariahw et al. no-loophole solve -- provisional fast-env ceiling

# arm display order, identified by a substring of the run's out_tag (seed-43 fast runs)
ARMS = [
    ("routeV per-token", "_dir6_routeV_pertoken_s43",        "ok"),
    ("routeV authored",  "_dir8_routeV_authored_perroll_s43", "ok"),
    ("routeV prog_wide", "_dir6_routeV_s43",                 "TODO: contaminated pairs -> job 28 prog_wide_clean"),
    ("routeV random-V",  "_dir6_routeV_random_s43",          "ok (directionality control)"),
    ("vanilla GRPO",     "_dir8_vanilla_s43",                "ok (defines hack-worst anchor)"),
]


def _find_run(tag: str) -> Path:
    cands = sorted(d for d in RUNS.iterdir()
                   if d.name.endswith(tag) and (d / "deploy_test.json").exists())
    if not cands:
        raise FileNotFoundError(f"no run dir ending '{tag}' with a deploy_test.json")
    return cands[-1]                 # latest timestamp wins


def _l5(rows: list[dict], k: str) -> float:
    v = [r[k] for r in rows[-5:]]
    return sum(v) / len(v)


# ── stage 1: build the inspectable csv ──────────────────────────────────────
def build_csv() -> pl.DataFrame:
    rows = []
    for label, tag, status in ARMS:
        run = _find_run(tag)
        dep = json.loads((run / "deploy_test.json").read_text())
        ev = [json.loads(l) for l in (run / "eval_curve.jsonl").read_text().splitlines()]
        rows.append(dict(
            label=label, kind="method",
            hack_deploy=round(dep["deploy_hack"], 4), solve_deploy=round(dep["deploy_solve"], 4),
            hack_on=round(_l5(ev, "train_hack"), 4),  hack_off=round(_l5(ev, "deploy_hack"), 4),
            solve_on=round(_l5(ev, "train_solve"), 4), solve_off=round(_l5(ev, "deploy_solve"), 4),
            source=f"{run.name}/[deploy_test.json + eval_curve.jsonl]", status=status))

    base = json.loads((_find_run("_dir8_baseline_s43") / "deploy_test.json").read_text())
    rows.append(dict(label="base (floor)", kind="anchor_floor",
                     hack_deploy=round(base["deploy_hack"], 4), solve_deploy=round(base["deploy_solve"], 4),
                     hack_on=None, hack_off=None, solve_on=None, solve_off=None,
                     source="*_dir8_baseline_s43/deploy_test.json", status="ok (base model; steps=0)"))

    ceil_path = next(RUNS.glob("*noloophole*/deploy_test.json"), None)
    if ceil_path:
        ceil_solve, status = round(json.loads(ceil_path.read_text())["deploy_solve"], 4), "ok"
        source = f"{ceil_path.parent.name}/deploy_test.json"
    else:
        ceil_solve, status = PAPER_CEILING, "FIXME: PROVISIONAL paper 0.223 -- awaiting job 24 (no-loophole ceiling)"
        source = "Ariahw et al. 2025 (paper), NOT our run"
    rows.append(dict(label="ceiling", kind="anchor_ceiling",
                     hack_deploy=0.0, solve_deploy=ceil_solve,
                     hack_on=None, hack_off=None, solve_on=None, solve_off=None,
                     source=source, status=status))

    df = pl.DataFrame(rows)
    OUT.mkdir(parents=True, exist_ok=True)
    df.write_csv(CSV)
    return df


# ── stage 2: plot from the csv ──────────────────────────────────────────────
def _anchors(df: pl.DataFrame) -> dict:
    g = lambda kind, col: df.filter(pl.col("kind") == kind)[col][0]
    ceil_status = g("anchor_ceiling", "status")
    return dict(base_solve=g("anchor_floor", "solve_deploy"),
                vanilla_hack=df.filter(pl.col("label") == "vanilla GRPO")["hack_deploy"][0],
                ceiling=g("anchor_ceiling", "solve_deploy"),
                provisional=ceil_status.startswith("FIXME"))


def _panel_normalized(ax, methods: pl.DataFrame, a, title):
    base, vh, ceil = a["base_solve"], a["vanilla_hack"], a["ceiling"]
    labels = [l for l in methods["label"] if l != "vanilla GRPO"]   # vanilla = the 0% hack anchor
    for yi, lab in enumerate(labels):
        r = methods.filter(pl.col("label") == lab).to_dicts()[0]
        hack_rm = (vh - r["hack_deploy"]) / vh
        solve_rc = (r["solve_deploy"] - base) / (ceil - base)
        ax.barh(yi + 0.18, hack_rm, height=0.32, color=RED, alpha=0.85)
        ax.text(hack_rm + 0.015, yi + 0.18, f"{r['hack_deploy']:.3f}  ({hack_rm*100:.0f}%)",
                va="center", ha="left", fontsize=8, color=RED)
        ax.barh(yi - 0.18, solve_rc, height=0.32, color=GREEN, alpha=0.85)
        ax.text(solve_rc + 0.015 if solve_rc >= 0 else solve_rc - 0.015, yi - 0.18,
                f"{r['solve_deploy']:.3f}  ({solve_rc*100:+.0f}%)",
                va="center", ha="left" if solve_rc >= 0 else "right", fontsize=8, color=GREEN)
    ax.axvline(0, color=GREY, lw=0.8)
    ax.axvline(1.0, color=GREY, lw=0.8, ls=":")
    ax.text(1.0, len(labels) - 0.35, "ceiling / no-hack", fontsize=7, color=GREY, ha="center")
    ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=9)
    ax.set_xlim(-0.35, 1.25); ax.set_xlabel("fraction of floor→ceiling range  (right = better)")
    ax.set_title(title, fontsize=10, loc="left")
    ax.text(0.01, 0.99, "red = hack removed (vs vanilla)   green = solve recovered (base→ceiling)",
            transform=ax.transAxes, fontsize=7.5, color="#444", va="top")
    for s in ("top", "right", "left"):
        ax.spines[s].set_visible(False)
    ax.tick_params(left=False)


def _panel_knob(ax, methods: pl.DataFrame):
    labels = list(methods["label"])
    for yi, lab in enumerate(labels):
        r = methods.filter(pl.col("label") == lab).to_dicts()[0]
        ax.annotate("", xy=(r["hack_off"], yi + 0.16), xytext=(r["hack_on"], yi + 0.16),
                    arrowprops=dict(arrowstyle="->", color=RED, lw=1.6, alpha=0.9))
        ax.plot([r["hack_on"], r["hack_off"]], [yi + 0.16] * 2, "o", color=RED, ms=4, alpha=0.5)
        ax.text(r["hack_on"] + 0.012, yi + 0.16, f"on {r['hack_on']:.2f}", va="center", ha="left", fontsize=7, color=RED)
        ax.text(r["hack_off"] - 0.012, yi + 0.16, f"{r['hack_off']:.2f}", va="center", ha="right", fontsize=7.5, color=RED)
        ax.annotate("", xy=(r["solve_off"], yi - 0.16), xytext=(r["solve_on"], yi - 0.16),
                    arrowprops=dict(arrowstyle="->", color=GREEN, lw=1.6, alpha=0.9))
        ax.plot([r["solve_on"], r["solve_off"]], [yi - 0.16] * 2, "o", color=GREEN, ms=4, alpha=0.5)
        ax.text(max(r["solve_on"], r["solve_off"]) + 0.012, yi - 0.16, f"solve {r['solve_off']:.2f}",
                va="center", ha="left", fontsize=7.5, color=GREEN)
    ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=9)
    ax.set_xlim(-0.02, 0.80)
    ax.set_xlabel("rate  (arrow = knob-ON → knob-OFF on held-out val; left = better for hack)")
    ax.set_title("B. the knob effect (held-out val n=32, L5 -- isolates the quarantine)", fontsize=10, loc="left")
    for s in ("top", "right", "left"):
        ax.spines[s].set_visible(False)
    ax.tick_params(left=False)


def plot(df: pl.DataFrame) -> None:
    a = _anchors(df)
    methods = df.filter(pl.col("kind") == "method")
    prov = "  [ceiling PROVISIONAL=0.223, FIXME job 24]" if a["provisional"] else ""
    fig, axes = plt.subplots(2, 1, figsize=(8.5, 8.0), gridspec_kw=dict(height_ratios=[1, 1.05]))
    _panel_normalized(axes[0], methods, a, f"A. normalized floor→ceiling, deploy (test n=119){prov}")
    _panel_knob(axes[1], methods)
    fig.suptitle("vGROUT: floor-to-ceiling method comparison (seed 43, 60-step fast)",
                 fontsize=11, x=0.02, ha="left")
    fig.tight_layout(rect=(0, 0, 1, 0.97))
    for ext in ("pdf", "png"):
        fig.savefig(OUT / f"floor_ceiling.{ext}", dpi=150, bbox_inches="tight")


def main() -> None:
    df = build_csv()
    flags = df.filter(~pl.col("status").str.starts_with("ok"))
    print(f"wrote {CSV}")
    if len(flags):
        print("\n=== TODO/FIXME in data ===")
        for r in flags.to_dicts():
            print(f"  [{r['label']}] {r['status']}")
    plot(df)
    print(f"\nwrote {OUT}/floor_ceiling.pdf and .png")


if __name__ == "__main__":
    main()