#!/usr/bin/env python3
"""
Render summary figures + stats from episode_results/ (no GPU needed).

ProgressLM demo-robustness (mirror of the Robometer prefix-robustness figures,
adapted because ProgressLM produces ONE score per checkpoint — not a dense
curve). Same metrics/threshold, terminology renamed Prefix -> Demo per v4.

Reads:  <results-root>/episode_results/<chunk>_<episode>/<mode>.json
Writes: <results-root>/summary/
          fig1_absolute_scores.png   4 checkpoints x episodes x 5 demo modes
          fig2_metrics.png           Demo Range / Demo Std / Reference Error
          fig3_by_length.png         range vs video length
          summary.md                 mean / median / p90, %>threshold, n/a rate
          top10/rankNN_<episode>.png 5-mode x 4-checkpoint overlays, worst episodes

Run with any python that has numpy + matplotlib (e.g. conda qwenvl):
  python render_figures.py [--results-root PATH] [--top-n 10]
"""
from __future__ import annotations

import argparse
import json
from collections import defaultdict
from pathlib import Path

import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

MODES = ["demo5_uniform", "demo3_sparse", "demo9_dense", "demo5_jitterA", "demo5_jitterB"]
BASELINE = "demo5_uniform"
MODE_COLORS = {
    "demo5_uniform": "tab:blue", "demo3_sparse": "tab:orange", "demo9_dense": "tab:green",
    "demo5_jitterA": "tab:red", "demo5_jitterB": "tab:purple",
}
FRACS = ["1/4", "2/4", "3/4", "end"]
THRESHOLD = 20.0


def parse_args():
    p = argparse.ArgumentParser()
    base = Path(__file__).resolve().parent.parent
    p.add_argument("--results-root", default=str(base / "results_full"))
    p.add_argument("--top-n", type=int, default=10)
    return p.parse_args()


def load_episodes(results_root: Path):
    """-> {ep_key: {mode: payload}} for episodes with all 5 modes present."""
    data = {}
    for ep_dir in sorted((results_root / "episode_results").iterdir()):
        if not ep_dir.is_dir():
            continue
        modes = {}
        for m in MODES:
            f = ep_dir / f"{m}.json"
            if f.exists():
                modes[m] = json.loads(f.read_text())
        if len(modes) == len(MODES):
            data[ep_dir.name] = modes
    return data


def main():
    args = parse_args()
    root = Path(args.results_root)
    out = root / "summary"
    (out / "top10").mkdir(parents=True, exist_ok=True)

    data = load_episodes(root)
    eps = sorted(data)
    print(f"episodes with all {len(MODES)} modes: {len(eps)}")
    if not eps:
        return

    # ── extract checkpoint scores + metrics (score is per-checkpoint, not per-pool-idx) ──
    score = defaultdict(dict)            # (ep, frac) -> {mode: score or None}
    rng_, std_ = {}, {}                  # (ep, frac) -> float (over present modes)
    ref_err = defaultdict(list)          # mode -> [score - baseline score]
    na_count = defaultdict(int)          # mode -> #n/a cells
    na_examples = defaultdict(list)      # mode -> [(ep, frac, raw_response)]
    total_cells = 0
    for ep in eps:
        for ci, frac in enumerate(FRACS):
            total_cells += 1
            present = {}
            for m in MODES:
                sc = data[ep][m]["scores_100"][ci]
                score[(ep, frac)][m] = sc
                if sc is None:
                    na_count[m] += 1
                    if len(na_examples[m]) < 3:
                        na_examples[m].append(
                            (ep, frac, data[ep][m].get("raw_responses", ["<none>"] * 4)[ci]))
                else:
                    present[m] = sc
            vals = np.array(list(present.values()), dtype=float)
            if len(vals) >= 2:
                rng_[(ep, frac)] = float(vals.max() - vals.min())
                std_[(ep, frac)] = float(vals.std(ddof=0))
            base_sc = score[(ep, frac)][BASELINE]
            if base_sc is not None:
                for m in MODES:
                    if m == BASELINE:
                        continue
                    if score[(ep, frac)][m] is not None:
                        ref_err[m].append(score[(ep, frac)][m] - base_sc)

    # per-episode aggregates (over checkpoints that have a valid range)
    ep_rngs = {e: [rng_[(e, f)] for f in FRACS if (e, f) in rng_] for e in eps}
    eps_valid = [e for e in eps if ep_rngs[e]]
    ep_mean_rng = {e: float(np.mean(ep_rngs[e])) for e in eps_valid}
    ep_max_rng = {e: float(max(ep_rngs[e])) for e in eps_valid}
    order = sorted(eps_valid, key=lambda e: -ep_mean_rng[e])
    x = np.arange(len(order))
    all_rng = list(rng_.values())
    pct_all = 100.0 * np.mean(np.array(all_rng) > THRESHOLD) if all_rng else 0.0

    # ── fig1: absolute scores ─────────────────────────────────────────────
    fig, axes = plt.subplots(4, 1, figsize=(16, 14), sharex=True)
    for ax, frac in zip(axes, FRACS):
        for i, e in enumerate(order):
            vals = [score[(e, frac)][m] for m in MODES if score[(e, frac)][m] is not None]
            if vals:
                ax.plot([i, i], [min(vals), max(vals)], color="0.85", lw=1, zorder=1)
        for m in MODES:
            ys = [score[(e, frac)][m] for e in order]
            xs = [i for i, y in enumerate(ys) if y is not None]
            yy = [y for y in ys if y is not None]
            ax.scatter(xs, yy, s=8, color=MODE_COLORS[m], label=m, zorder=2)
        ax.set_ylabel("score (0-100)")
        ax.set_title(f"checkpoint {frac}", loc="left", fontsize=11)
        ax.set_ylim(0, 100)
        ax.grid(alpha=0.2)
    axes[0].legend(ncol=5, fontsize=9, loc="upper right")
    axes[-1].set_xlabel("episode (sorted by mean Demo Range, desc)")
    fig.suptitle("Summary of absolute progress scores — 5 demo modes per episode\n"
                 "(gray bar = min-max spread at the same physical target frame)", y=0.995)
    fig.tight_layout()
    fig.savefig(out / "fig1_absolute_scores.png", dpi=150)
    plt.close(fig)

    # ── fig2: metrics ─────────────────────────────────────────────────────
    fig, axes = plt.subplots(2, 2, figsize=(15, 11))

    ax = axes[0][0]
    for frac in FRACS:
        vals = sorted((rng_[(e, frac)] for e in eps if (e, frac) in rng_), reverse=True)
        if not vals:
            continue
        pct = 100.0 * np.mean(np.array(vals) > THRESHOLD)
        ax.plot(vals, label=f"{frac}  ({pct:.0f}% > {THRESHOLD:.0f} pts)")
    ax.axhline(THRESHOLD, color="red", ls="--", lw=1)
    ax.set_xlabel("episode rank (desc)")
    ax.set_ylabel("Demo Range (pts)")
    ax.set_title("(A) Demo Range per checkpoint, sorted")
    ax.legend(fontsize=9)
    ax.grid(alpha=0.2)

    ax = axes[0][1]
    ax.hist(all_rng, bins=30, color="tab:red", alpha=0.75)
    ax.axvline(THRESHOLD, color="black", ls="--", lw=1.5,
               label=f"{THRESHOLD:.0f}-pt threshold")
    ax.set_xlabel("Demo Range (pts)")
    ax.set_ylabel("count (episode x checkpoint)")
    ax.set_title(f"(B) Demo Range distribution — {pct_all:.0f}% above threshold")
    ax.legend(fontsize=9)
    ax.grid(alpha=0.2)

    ax = axes[1][0]
    ax.hist(list(std_.values()), bins=30, color="tab:blue", alpha=0.75)
    ax.set_xlabel("Demo Std (pts)")
    ax.set_ylabel("count (episode x checkpoint)")
    ax.set_title("(C) Demo Std distribution")
    ax.grid(alpha=0.2)

    ax = axes[1][1]
    ax.boxplot([ref_err[m] for m in MODES if m != BASELINE],
               labels=[m.replace("_", "\n") for m in MODES if m != BASELINE],
               showmeans=True)
    ax.axhline(0, color="black", lw=1)
    ax.set_ylabel(f"score - {BASELINE} score (pts)")
    ax.set_title("(D) Reference Error vs baseline (signed)")
    ax.grid(alpha=0.2)

    fig.suptitle("Demo-robustness metrics (ProgressLM-3B-RL, 4 checkpoints)", y=0.995)
    fig.tight_layout()
    fig.savefig(out / "fig2_metrics.png", dpi=150)
    plt.close(fig)

    # ── fig3: range vs video length ───────────────────────────────────────
    fig, ax = plt.subplots(figsize=(10, 6))
    dur = np.array([data[e][BASELINE]["total_raw_frames"]
                    / max(data[e][BASELINE]["native_fps"], 1e-6) for e in eps_valid])
    mrng = np.array([ep_max_rng[e] for e in eps_valid])
    if len(dur):
        qs = np.quantile(dur, [0, 0.25, 0.5, 0.75, 1.0])
        groups, labels = [], []
        for lo, hi in zip(qs[:-1], qs[1:]):
            m = (dur >= lo) & (dur <= hi)
            groups.append(mrng[m])
            labels.append(f"{lo:.0f}-{hi:.0f}s\n(n={int(m.sum())})")
        ax.boxplot(groups, labels=labels, showmeans=True)
    ax.axhline(THRESHOLD, color="red", ls="--", lw=1)
    ax.set_xlabel("video length (quartile bins)")
    ax.set_ylabel("max Demo Range over 4 checkpoints (pts)")
    ax.set_title("Demo Range vs video length")
    ax.grid(alpha=0.2)
    fig.tight_layout()
    fig.savefig(out / "fig3_by_length.png", dpi=150)
    plt.close(fig)

    # ── summary.md ────────────────────────────────────────────────────────
    def stats(vals):
        a = np.array(vals)
        if len(a) == 0:
            return "n/a (no data)"
        return (f"mean {a.mean():.2f} | median {np.median(a):.2f} | "
                f"p90 {np.quantile(a, 0.9):.2f} | max {a.max():.2f}")

    cam = data[eps[0]][BASELINE]["camera"]
    lines = ["# ProgressLM-3B-RL demo-robustness — full batch summary", ""]
    lines += [f"Episodes: **{len(eps)}**  |  camera: {cam}  |  "
              f"modes: {', '.join(MODES)}  |  baseline: {BASELINE}  |  "
              f"threshold: {THRESHOLD:.0f} pts", ""]
    lines += ["Each score = ProgressLM scoring one fixed target frame against a self-demo; "
              "the perturbation is the demo organisation. Metrics compare the 5 modes at the "
              "same physical target frame.", ""]
    lines += ["## Demo Range (max - min of the 5 mode scores, same target frame)", "",
              "| checkpoint | stats | % > threshold |", "|---|---|---|"]
    for frac in FRACS:
        vals = [rng_[(e, frac)] for e in eps if (e, frac) in rng_]
        pct = 100.0 * np.mean(np.array(vals) > THRESHOLD) if vals else 0.0
        lines.append(f"| {frac} | {stats(vals)} | **{pct:.1f}%** |")
    lines.append(f"| all | {stats(all_rng)} | **{pct_all:.1f}%** |")
    ep_any = (100.0 * np.mean([ep_max_rng[e] > THRESHOLD for e in eps_valid])
              if eps_valid else 0.0)
    lines += ["", f"Episodes with >= 1 checkpoint above threshold: **{ep_any:.1f}%**", ""]
    lines += ["## Demo Std", "", f"All cells: {stats(list(std_.values()))}", ""]
    lines += ["## Reference Error vs baseline (signed, pts)", "",
              "| mode | mean | median | std | n |", "|---|---|---|---|---|"]
    for m in MODES:
        if m == BASELINE:
            continue
        a = np.array(ref_err[m])
        if len(a):
            lines.append(f"| {m} | {a.mean():+.2f} | {np.median(a):+.2f} | {a.std():.2f} | {len(a)} |")
        else:
            lines.append(f"| {m} | n/a | n/a | n/a | 0 |")
    lines.append("")

    # ── n/a report ────────────────────────────────────────────────────────
    lines += ["## n/a rate (per mode; a cell = one episode x checkpoint)", "",
              f"Total cells per mode: **{total_cells}**", "",
              "| mode | n/a count | n/a rate |", "|---|---|---|"]
    high_na = []
    for m in MODES:
        rate = 100.0 * na_count[m] / max(total_cells, 1)
        flag = "  **>10%**" if rate > 10.0 else ""
        lines.append(f"| {m} | {na_count[m]} | {rate:.1f}%{flag} |")
        if rate > 10.0:
            high_na.append(m)
    lines.append("")
    if high_na:
        lines += ["### High-n/a modes — 3 example raw responses each", ""]
        for m in high_na:
            lines.append(f"**{m}**")
            for ep, frac, raw in na_examples[m]:
                snippet = (raw or "").replace("\n", " ")[:400]
                lines.append(f"- `{ep}` @ {frac}: {snippet}")
            lines.append("")

    # ── top10: 5-mode x 4-checkpoint overlays ─────────────────────────────
    worst = sorted(eps_valid, key=lambda e: -ep_max_rng[e])[:args.top_n]
    lines += [f"## Top {args.top_n} least-robust episodes (by max Demo Range)", "",
              "| rank | episode | max range | mean range | figure |",
              "|---|---|---|---|---|"]
    xt = np.arange(len(FRACS))
    for rank, e in enumerate(worst, 1):
        fname = f"rank{rank:02d}_{e}.png"
        lines.append(f"| {rank} | {e} | {ep_max_rng[e]:.1f} | "
                     f"{ep_mean_rng[e]:.1f} | top10/{fname} |")

        fig, ax = plt.subplots(figsize=(11, 6))
        for m in MODES:
            ys = [score[(e, f)][m] for f in FRACS]
            xs = [i for i, y in enumerate(ys) if y is not None]
            yy = [y for y in ys if y is not None]
            ax.plot(xs, yy, "-o", color=MODE_COLORS[m], lw=1.6, ms=6, label=m)
        ax.set_xticks(xt)
        ax.set_xticklabels(FRACS)
        ax.set_xlabel("checkpoint (target frame position in episode)")
        ax.set_ylabel("progress score (0-100)")
        ax.set_ylim(0, 100)
        ax.grid(alpha=0.2)
        ax.legend(fontsize=9)
        task = data[e][BASELINE]["task"]
        ax.set_title(f"#{rank} {e}  max Demo Range {ep_max_rng[e]:.1f}\n{task[:110]}",
                     fontsize=10)
        fig.tight_layout()
        fig.savefig(out / "top10" / fname, dpi=140)
        plt.close(fig)

    (out / "summary.md").write_text("\n".join(lines))
    print("written:", out)


if __name__ == "__main__":
    main()