"""Closed-loop test of a trained policy in simulation.

Each test episode is set up exactly like a dataset episode (random layout, piece set,
lighting, colours, clutter, cameras and move) from seeds the dataset never used, and
is kept only if the scripted expert can do it. The policy then drives the arm at 30 Hz
from the overhead and wrist images (through the same webcam look and red/blue squares
as the data, then the same H.264 round trip the dataset videos went through, see
camera_effects.training_look) and the joint state, for up to --seconds. Success is judged as for the
expert: the piece within 6 mm of the destination square (or in the tray), upright, and
no other piece moved more than 2 mm.

With --scenes, the episodes are instead rebuilt from a dataset's phase2_episodes.jsonl
(its seed, piece set and board are recorded), so the policy is tested on scenes it was
trained on. The rebuilt scene is compared with the record (board, tray, overhead
camera, move) and the expert is run on it once as a check.

Besides success, every episode records how close the gripper came to the marked piece
(`closest_mm`, in the board plane), whether that piece was lifted, and which square the
gripper was over at its lowest point.

Writes eval_report.md, eval_results.json and eval_video.mp4 (first episodes, overhead
and wrist side by side) to --out.

Run:  MUJOCO_GL=egl .venv/bin/python sim/eval_policy.py --policy Machanize/chess_phase_smolvla --episodes 100
      MUJOCO_GL=egl .venv/bin/python sim/eval_policy.py --policy Machanize/chess_phase_smolvla \
          --scenes data/varied_2000_notes.jsonl --episodes 30 --out sim/reports/diagnose/training_scenes
"""
from __future__ import annotations

import argparse
import json
import multiprocessing as mp
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from queue import Empty

HERE = Path(__file__).resolve().parent
LIFT_M = 0.008          # the expert lifts pawns about 13 mm, knights about 36 mm
sys.path.insert(0, str(HERE))


def load_policy(path: str, n_action_steps: int | None = None, expert_fp32: bool = False):
    """The policy, its pre/post processors and the torch device. `n_action_steps` overrides how
    many steps of each predicted chunk are executed before predicting again.
    `expert_fp32`: run SmolVLA's action expert in float32. Loading otherwise rounds it to bfloat16
    (the checkpoint stores it in float32); its weights are reloaded from the file at full precision."""
    import torch
    from lerobot.configs.policies import PreTrainedConfig
    from lerobot.policies.factory import get_policy_class, make_pre_post_processors

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    pcfg = PreTrainedConfig.from_pretrained(path)
    pcfg.pretrained_path = path
    pcfg.device = device.type
    if n_action_steps:
        pcfg.n_action_steps = n_action_steps
    policy = get_policy_class(pcfg.type).from_pretrained(path, config=pcfg)
    if expert_fp32:
        from safetensors.torch import load_file

        policy.model.vlm_with_expert.lm_expert.float()
        weights = load_file(str(Path(path) / "model.safetensors"))
        expert = {k: v for k, v in weights.items() if ".lm_expert." in k}
        missing, unexpected = policy.load_state_dict(expert, strict=False)
        assert expert and not unexpected, "action expert weights not found in model.safetensors"
    policy.to(device).eval()
    pre, post = make_pre_post_processors(policy_cfg=pcfg, pretrained_path=path,
                                         preprocessor_overrides={"device_processor": {"device": device.type}})
    return policy, pre, post, device


def piece_set_from_record(rec: dict):
    """The piece set and board of a recorded episode (dimensions are kept to 0.1 mm)."""
    from chess_world import KINDS
    from make_scene import PIECES, Layout
    from piece_sets import PieceSet, foot_mm

    dims = rec["piece_set_dims"]
    layout = Layout(**{k: (tuple(x / 1000 for x in v) if isinstance(v, list) else v / 1000)
                       for k, v in dims["layout"].items()})
    width = {k: dims[k]["foot_mm"] / foot_mm(k) for k in KINDS}
    height = {k: dims[k]["height_mm"] / (PIECES[k]["height_m"] * 1000) for k in KINDS}
    return PieceSet(rec["piece_set"], width, height, layout)


def rebuild_check(info: dict, task, rec: dict) -> list[str]:
    """Differences between a rebuilt episode and its record (empty if it matches)."""
    import numpy as np

    out = []
    for key in ("board", "tray"):
        if np.abs(np.subtract(info[key]["centre_m"], rec[key]["centre_m"])).max() > 0.001:
            out.append(f"{key} centre {info[key]['centre_m']} vs {rec[key]['centre_m']}")
    a, b = info["overhead"], rec["overhead"]
    if isinstance(a, dict) != isinstance(b, dict) or (isinstance(a, dict) and any(
            abs(a[k] - b[k]) > 0.5 for k in ("tilt_deg", "azimuth_deg", "roll_deg", "fovy_deg"))):
        out.append(f"overhead {a} vs {b}")
    if task.source != rec["source"] or (task.dest.square or "bin") != rec["destination"]:
        out.append(f"move {task.source}->{task.dest.square or 'bin'} vs {rec['source']}->{rec['destination']}")
    return out


def drive(runner, task, policy, pre, post, device, cfg, seconds, rec_rng_seed, video=None, stop_when_done=True,
          recorder=None, on_frame=None, on_substep=None, replan_near=None, near_height=0.07, fast=False):
    """Let the policy drive from the set-up state. Returns (Result, metrics).

    `recorder`: an already begun Recorder to render through (its dataset is not written).
    `on_frame(f, action)`: called after every step; returning True stops the drive there.
    `on_substep(f, k)`: called after every physics substep k of frame f.
    `replan_near`: while the gripper frame is within `near_height` (m) of the board surface,
    predict a new chunk after this many steps of the current one instead of all of them (the
    rest of the chunk is dropped). A real arm knows this height from its joint angles.
    `fast`: render only the frames where the policy predicts a new chunk (it ignores the images of
    the others); the next action of the current chunk is taken from its queue and postprocessed as
    predict_action does. Without camera noise (the baseline profile) the results are identical."""
    import mujoco
    import numpy as np
    import torch
    from lerobot.utils.control_utils import predict_action

    from camera_effects import training_look
    from lerobot_export import Recorder, video_settings

    crf, pix_fmt = video_settings(cfg)
    fps = cfg["dataset"]["fps"]
    rec = recorder
    if rec is None:
        rec = Recorder(cfg, None)
        rec.begin(runner, task, np.random.default_rng(rec_rng_seed))
    d, w = runner.d, runner.w
    start = {p: w.base_pos(d, p) for p in w.pieces}
    watched = [p for p in task.squares.values() if p != task.target]
    lo, hi = runner.grip_range
    home = np.array(cfg["expert"]["home"])
    site = runner.m.site("gripperframe").id
    src = w.square_center(task.source)
    closest, lowest, lifted, placed_at = np.inf, (np.inf, None), False, None
    path = []
    policy.reset()
    frames, predictions = 0, 0
    n_steps = policy.config.n_action_steps
    for f in range(int(seconds * fps)):
        queue = policy._queues["action"] if hasattr(policy, "_queues") else None
        if replan_near and queue is not None and len(queue) > 0:
            near = d.site_xpos[site][2] - w.board_top < near_height
            if near and n_steps - len(queue) >= replan_near:
                queue.clear()                         # predict afresh from the current images
        if queue is not None and len(queue) == 0:
            predictions += 1
        if fast and video is None and queue is not None and len(queue) > 0:
            with torch.inference_mode():
                a = post(queue.popleft())
        else:
            over, wrist = (training_look(rec.observe(runner, c), crf, pix_fmt) for c in ("overhead", "wrist"))
            if video is not None:
                video.append(np.concatenate([over[::2, ::2], wrist[::2, ::2]], axis=1))
            obs = {"observation.images.overhead": over, "observation.images.wrist": wrist,
                   "observation.state": runner.to_lerobot(d.qpos[runner.qadr])}
            a = predict_action(obs, policy, device, pre, post, use_amp=False, task=cfg["dataset"]["instruction"],
                               robot_type="so101_follower")
        a = a.squeeze().float().cpu().numpy()
        d.ctrl[:5] = np.radians(a[:5])
        d.ctrl[5] = lo + np.clip(a[5], 0, 100) / 100 * (hi - lo)
        for k in range(runner.n_sub):
            mujoco.mj_step(runner.m, d)
            if on_substep is not None:
                on_substep(f, k)
        frames += 1
        g = d.site_xpos[site].copy()
        path.append(g)
        closest = min(closest, float(np.linalg.norm(g[:2] - src[:2])))
        if g[2] < lowest[0]:
            lowest = (float(g[2]), w.square_at(g))
        lifted |= bool(w.base_pos(d, task.target)[2] - start[task.target][2] > LIFT_M)
        if on_frame is not None and on_frame(f, a):
            break
        # Done once the piece has been set down and the arm is back near rest.
        if stop_when_done and f % 15 == 0 and f > 60:
            r = runner._judge(task, start, watched, {}, frames, 0.0)
            near_home = np.abs(d.qpos[runner.expert.kin.qadr] - home).max() < 0.35
            if r.success and placed_at is None:
                placed_at = f
            if placed_at is not None and near_home:
                break
    r = runner._judge(task, start, watched, {}, frames, 0.0)
    metrics = dict(closest_mm=round(1000 * closest, 1), lifted=lifted, predictions=predictions,
                   lowest_mm=round(1000 * (lowest[0] - w.board_top), 1), lowest_square=lowest[1],
                   path=np.array(path))
    return r, metrics


OPEN, CLOSING = 4.0, 3.0      # gripper command, LeRobot 0-100 units: open around a piece 6-11, closed 0


def worker(k, n, args, jobs, queue):
    import warnings

    warnings.filterwarnings("ignore")
    import mujoco
    import numpy as np
    import torch

    from episode import EpisodeRunner, load_config
    from piece_sets import sample_piece_set

    torch.set_num_threads(2)                  # several workers share the CPU; torch would take every core
    cfg = load_config()
    policy, pre, post, device = load_policy(args.policy, args.n_action_steps, args.expert_fp32)
    runner, block_now = None, None
    # Each worker takes a contiguous slice, and every episode (its piece set, scene, move and the
    # policy's sampling noise) comes from (seed, episode index) alone: the same --seed gives the same
    # episodes whatever the number of workers, so models can be compared on identical scenes.
    for i in range(k * args.episodes // n, (k + 1) * args.episodes // n):
        extra = {}
        erng = np.random.default_rng([args.seed, i])
        if jobs:                                  # a recorded training episode
            rec = jobs[i]
            if runner is not None:
                runner.close()
            runner = EpisodeRunner(cfg, piece_set_from_record(rec), render=True)
            ep_seed = rec["seed"]
            task = runner.setup(np.random.default_rng(ep_seed))
            extra = dict(episode_index=rec["episode_index"], rebuild_diffs=rebuild_check(runner.episode_info, task, rec),
                         expert_ok=runner.run(task, ep_seed).success)
        else:
            block = i // args.per_set
            if block != block_now:
                if runner is not None:
                    runner.close()
                runner = EpisodeRunner(cfg, sample_piece_set(np.random.default_rng([args.seed, 10**9 + block]), cfg,
                                                             f"eval_{block}"), render=True)
                block_now = block
            for _ in range(20):                      # a move the expert can do
                ep_seed = int(erng.integers(2**62))
                task = runner.setup(np.random.default_rng(ep_seed))
                if runner.run(task, ep_seed).success:
                    break
        task = runner.setup(np.random.default_rng(ep_seed))
        info = dict(runner.episode_info)
        m_, d, w, ex = runner.m, runner.d, runner.w, runner.expert
        # The expert's grasp of the marked piece (planned, not executed), to measure where the
        # policy's jaws are when it starts closing.
        saved = (d.qpos.copy(), d.qvel.copy(), d.act.copy(), d.ctrl.copy(), d.time)
        try:
            plan = ex.plan_pick(d, task.target, np.random.default_rng(ep_seed))
        except Exception:
            plan = None
        d.qpos[:], d.qvel[:], d.act[:], d.ctrl[:] = saved[:4]
        d.time = saved[4]
        mujoco.mj_forward(m_, d)
        st = dict(open_max=0.0, close=None)

        def on_frame(f, a):
            if plan is None or st["close"] is not None:
                return False
            st["open_max"] = max(st["open_max"], float(a[5]))
            if st["open_max"] > OPEN and a[5] < CLOSING:
                pnt, _ = ex.kin.pose(d.qpos[ex.kin.qadr].copy(), plan.offset)
                err = pnt - plan.grasp_point
                axis = np.array([np.cos(plan.yaw), np.sin(plan.yaw)])
                st["close"] = dict(close_t_s=round(f / 30, 2), close_lateral_mm=round(1000 * float(np.linalg.norm(err[:2])), 1),
                                   close_along_mm=round(1000 * float(err[:2] @ axis), 1),
                                   close_across_mm=round(1000 * float(err[0] * -axis[1] + err[1] * axis[0]), 1),
                                   close_height_mm=round(1000 * float(err[2]), 1))
            return False

        video = [] if i < args.video_episodes else None
        torch.manual_seed(int(erng.integers(2**31)))    # the policy's sampling noise, per episode
        t0 = time.time()
        r, m = drive(runner, task, policy, pre, post, device, cfg, args.seconds, ep_seed ^ 0x5EED, video,
                     on_frame=on_frame, replan_near=args.replan_near, near_height=args.near_height_mm / 1000,
                     fast=args.fast)
        m.pop("path")
        queue.put(dict(index=i, success=bool(r.success), reason=r.reason, frames=r.frames, seconds=round(time.time() - t0, 1),
                       piece=w.kind[task.target], source=task.source, destination=task.dest.square or "bin",
                       move_kind=task.kind, centre_error_mm=r.centre_error_mm, robot_plays=info["board"]["robot_plays"],
                       arm_colour=info["arm_colour"], key_light=info["lights"]["key"], floor=info["floor"],
                       overhead=info["overhead"], square_mm=round(w.square_size * 1000, 1),
                       disturbed=bool(r.disturbed), max_disturbance_mm=r.max_disturbance_mm, closed=st["close"] is not None,
                       **(st["close"] or {}), **m, **extra, video=video if video else None))
    queue.put(None)


def pick_scenes(path: str, count: int) -> list[dict]:
    """`count` recorded episodes spread evenly over the file."""
    recs = [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()]
    for i, r in enumerate(recs):
        r.setdefault("episode_index", i)
    step = len(recs) / count
    return [recs[int(i * step)] for i in range(count)]


def main():
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--policy", required=True, help="Hugging Face repo id or local path of the trained policy")
    ap.add_argument("--episodes", type=int, default=100)
    ap.add_argument("--workers", type=int, default=6)
    ap.add_argument("--seconds", type=float, default=20.0, help="time limit per episode")
    ap.add_argument("--per-set", type=int, default=5, help="episodes per random piece set and board")
    ap.add_argument("--seed", type=int, default=1_000_003, help="never used by generate_dataset (seeds 0 and 1)")
    ap.add_argument("--scenes", help="phase2_episodes.jsonl of a dataset: rebuild its episodes instead")
    ap.add_argument("--video-episodes", type=int, default=6)
    ap.add_argument("--n-action-steps", type=int, help="replan after this many steps of each chunk (default: as trained)")
    ap.add_argument("--replan-near", type=int, help="near the board, replan after this many steps of each chunk")
    ap.add_argument("--near-height-mm", type=float, default=70.0, help="gripper-frame height above the board that counts as near")
    ap.add_argument("--fast", action="store_true", help="render only the frames the policy predicts from (see drive)")
    ap.add_argument("--expert-fp32", action="store_true", help="run the action expert in float32 (see load_policy)")
    ap.add_argument("--out", default=str(HERE / "reports" / "policy_eval"))
    args = ap.parse_args()
    out = Path(args.out)
    out.mkdir(parents=True, exist_ok=True)
    jobs = pick_scenes(args.scenes, args.episodes) if args.scenes else None
    ctx = mp.get_context("spawn")
    queue = ctx.Queue()
    procs = [ctx.Process(target=worker, args=(k, args.workers, args, jobs, queue)) for k in range(args.workers)]
    for p in procs:
        p.start()
    t0, results, finished = time.time(), [], 0
    # One line per finished episode, written as it arrives, so a stopped run can still be paired.
    partial = out / "eval_episodes.jsonl"
    partial.unlink(missing_ok=True)
    while finished < len(procs):
        try:
            r = queue.get(timeout=60)
        except Empty:
            if not any(p.is_alive() for p in procs):      # a worker crashed without reporting
                print(f"{sum(p.exitcode != 0 for p in procs)} worker(s) crashed; reporting what finished", flush=True)
                break
            continue
        if r is None:
            finished += 1
            continue
        results.append(r)
        with partial.open("a") as f:
            f.write(json.dumps({k: v for k, v in r.items() if k != "video"},
                               default=lambda o: o.item() if hasattr(o, "item") else str(o)) + "\n")
        ok = sum(x["success"] for x in results)
        print(f"{len(results)}/{args.episodes} [ep {r['index']}]: {'ok  ' if r['success'] else 'fail'} {r['piece']} {r['source']}->{r['destination']} "
              f"closest {r['closest_mm']} mm, lifted {r['lifted']} ({r['frames']} frames) | "
              f"{ok}/{len(results)} = {100 * ok / len(results):.1f}%", flush=True)
    for p in procs:
        p.join()
    results.sort(key=lambda r: r["index"])
    clips = [r.pop("video") for r in results]
    import numpy as np
    clips = [c for c in clips if c]
    if clips:
        import av

        with av.open(str(out / "eval_video.mp4"), "w") as container:
            stream = container.add_stream("libx264", rate=30)
            stream.height, stream.width = clips[0][0].shape[:2]
            stream.pix_fmt = "yuv420p"
            stream.options = {"crf": "23"}
            for clip in clips:
                for frame in clip:
                    for packet in stream.encode(av.VideoFrame.from_ndarray(np.ascontiguousarray(frame), format="rgb24")):
                        container.mux(packet)
            for packet in stream.encode():
                container.mux(packet)
    rate = lambda rs: round(100 * sum(r["success"] for r in rs) / max(len(rs), 1), 1)
    groups = {}
    for key in ("piece", "move_kind", "robot_plays", "arm_colour", "key_light", "floor"):
        g = defaultdict(list)
        for r in results:
            g[r[key]].append(r)
        groups[key] = {k: dict(success_percent=rate(v), episodes=len(v)) for k, v in sorted(g.items())}
    dest = defaultdict(list)
    for r in results:
        dest["tray" if r["destination"] == "bin" else "square"].append(r)
    groups["destination"] = {k: dict(success_percent=rate(v), episodes=len(v)) for k, v in dest.items()}
    if not jobs:
        moves = defaultdict(list)
        for r in results:
            moves[f"{r['source']}-{r['destination']}"].append(r)
        groups["move"] = {k: dict(success_percent=rate(v), episodes=len(v)) for k, v in sorted(moves.items())}
    closest = np.array([r["closest_mm"] for r in results])
    reach = dict(median_closest_mm=round(float(np.median(closest)), 1),
                 within_half_square_percent=round(100 * float(np.mean(closest < 12.5)), 1),
                 lifted_percent=round(100 * float(np.mean([r["lifted"] for r in results])), 1),
                 lowest_over_source_percent=round(100 * float(np.mean([r["lowest_square"] == r["source"] for r in results])), 1))
    closed = [r for r in results if r.get("close_lateral_mm") is not None]
    med = lambda rs, key: round(float(np.median([r[key] for r in rs])), 1) if rs else None
    precision = dict(closed_percent=round(100 * len(closed) / max(len(results), 1), 1),
                     median_close_lateral_mm=med(closed, "close_lateral_mm"),
                     median_close_lateral_mm_success=med([r for r in closed if r["success"]], "close_lateral_mm"),
                     median_close_lateral_mm_failure=med([r for r in closed if not r["success"]], "close_lateral_mm"),
                     close_within_2mm_percent=round(100 * sum(r["close_lateral_mm"] <= 2 for r in closed) / max(len(closed), 1), 1),
                     median_close_height_mm=med(closed, "close_height_mm"),
                     disturbed_percent=round(100 * sum(r["disturbed"] for r in results) / max(len(results), 1), 1))
    pieces = {}
    for kind in sorted({r["piece"] for r in results}):
        rs = [r for r in results if r["piece"] == kind]
        cs = [r for r in rs if r.get("close_lateral_mm") is not None]
        pieces[kind] = dict(episodes=len(rs), success_percent=rate(rs),
                            lifted_percent=round(100 * sum(r["lifted"] for r in rs) / len(rs), 1),
                            median_close_lateral_mm=med(cs, "close_lateral_mm"),
                            disturbed_percent=round(100 * sum(r["disturbed"] for r in rs) / len(rs), 1))
    summary = dict(policy=args.policy, profile=os.environ.get("PHASE2_PROFILE"), n_action_steps=args.n_action_steps,
                   replan_near=args.replan_near, near_height_mm=args.near_height_mm,
                   offsamples=os.environ.get("SIM_OFFSAMPLES"),
                   seed=args.seed, precision=precision, pieces=pieces,
                   episodes=len(results),
                   success_percent=rate(results), seconds_limit=args.seconds, minutes=round((time.time() - t0) / 60, 1),
                   reach=reach, by=groups)
    if jobs:
        summary["training_scenes"] = dict(
            source=args.scenes, rebuilt_exactly=sum(not r["rebuild_diffs"] for r in results),
            expert_ok=sum(r["expert_ok"] for r in results))
    (out / "eval_results.json").write_text(json.dumps(dict(summary=summary, episodes=results), indent=1,
                                                       default=lambda o: o.item() if hasattr(o, "item") else str(o)))
    where = ("scenes rebuilt from the training data (`" + Path(args.scenes).name + "`)" if jobs else
             f"the `{summary['profile']}` settings profile's scene from seeds not used for training"
             if summary["profile"] else
             "random layouts, lighting, colours, clutter and camera views from seeds not used for training; "
             "each move checked doable by the expert")
    lines = [f"# Policy evaluation in simulation", "",
             f"Policy `{args.policy}`, {len(results)} closed-loop episodes on {where}."
             + (f" Replanning every {args.n_action_steps} steps." if args.n_action_steps else "")
             + (f" Near the board (gripper within {args.near_height_mm:.0f} mm), replanning every {args.replan_near} steps."
                if args.replan_near else "")
             + (f" Anti-aliasing samples: {os.environ.get('SIM_OFFSAMPLES')}." if os.environ.get("SIM_OFFSAMPLES") else ""), "",
             f"**Success: {summary['success_percent']}%** (piece within 6 mm of the target square or in the tray, "
             f"upright, nothing else moved more than 2 mm; {args.seconds:.0f} s limit).", "",
             "## Reach", "",
             f"- Median closest approach of the gripper to the marked piece (board plane): {reach['median_closest_mm']} mm "
             f"(a square is about {np.median([r['square_mm'] for r in results]):.0f} mm).",
             f"- Gripper within half a square of the marked piece: {reach['within_half_square_percent']}% of episodes.",
             f"- Marked piece lifted more than 8 mm: {reach['lifted_percent']}%.",
             f"- Gripper's lowest point was over the marked square: {reach['lowest_over_source_percent']}%.", "",
             "## Grasp precision", "",
             "Where the jaws are when the policy starts closing (command below 3 of 100), against the grasp the "
             "scripted expert would make on the same piece.", "",
             f"- Closed on the piece's square in {precision['closed_percent']}% of episodes.",
             f"- Median sideways error at closing: {precision['median_close_lateral_mm']} mm "
             f"(successes {precision['median_close_lateral_mm_success']}, failures {precision['median_close_lateral_mm_failure']}); "
             f"within 2 mm in {precision['close_within_2mm_percent']}%. Median height error {precision['median_close_height_mm']} mm.",
             f"- Another piece moved more than 2 mm in {precision['disturbed_percent']}% of episodes.", "",
             "| piece | episodes | success % | lifted % | median closing error mm | disturbed % |", "|---|---|---|---|---|---|"]
    lines += [f"| {k} | {v['episodes']} | {v['success_percent']} | {v['lifted_percent']} | {v['median_close_lateral_mm']} | "
              f"{v['disturbed_percent']} |" for k, v in pieces.items()] + [""]
    lines += [f"Episodes from seed {args.seed}: the same seed gives the same episodes for any number of workers.", ""]
    if jobs:
        ts = summary["training_scenes"]
        lines += [f"Rebuild check: {ts['rebuilt_exactly']} of {len(results)} scenes matched their record "
                  f"(board, tray, overhead camera, move); the expert completed {ts['expert_ok']} of {len(results)} "
                  f"rebuilt scenes.", ""]
    for key, title in (("move", "By move"), ("piece", "By piece"), ("destination", "By destination"),
                       ("move_kind", "By move kind"), ("robot_plays", "By side the robot plays"),
                       ("arm_colour", "By arm colour"), ("key_light", "By main light"), ("floor", "By floor")):
        if key not in groups:
            continue
        lines += [f"## {title}", "", "| | success % | episodes |", "|---|---|---|"]
        lines += [f"| {k} | {v['success_percent']} | {v['episodes']} |" for k, v in groups[key].items()] + [""]
    lines += ["## Episodes", "", "| # | move | success | closest mm | lifted | lowest over | reason |",
              "|---|---|---|---|---|---|---|"]
    lines += [f"| {r.get('episode_index', r['index'])} | {r['piece']} {r['source']}-{r['destination']} | "
              f"{'yes' if r['success'] else 'no'} | {r['closest_mm']} | {'yes' if r['lifted'] else 'no'} | "
              f"{r['lowest_square'] or '-'} | {r['reason']} |" for r in results]
    (out / "eval_report.md").write_text("\n".join(lines) + "\n")
    print(json.dumps({k: v for k, v in summary.items() if k != "by"}, indent=1))


if __name__ == "__main__":
    main()