"""Marker-dependence test: does the policy go where the red square is?

Each scene is set up once (rebuilt from a dataset's phase2_episodes.jsonl with
--scenes, or drawn at random) and its state saved. The red square is then drawn over
--placements different pieces in turn: the recorded source first, then pieces spread
over the board. Everything else stays identical: the same start state, the same
webcam look and calibration error, and the blue square on the same destination. For
each placement the policy drives for --seconds from the same start, and the gripper's
path is recorded.

Per placement: how close the gripper came to the marked piece, and which of the
placements' pieces it came closest to while down near the board (within 6 cm). The
policy follows the markers if that is the marked one. Per scene: how far apart the gripper's lowest points are across
placements, against how far apart the marked pieces are (near 1 if it follows the
markers, near 0 if it goes to the same place whatever is marked).

Writes marker_report.md, marker_results.json and one image per scene: the gripper
paths drawn over the overhead view, one colour per placement, with a ring on the
piece that was marked.

Run:  MUJOCO_GL=egl .venv/bin/python sim/marker_test.py --policy Machanize/chess_phase_smolvla \
          --scenes data/varied_2000_notes.jsonl --count 6 --out sim/reports/diagnose/marker_test
"""
from __future__ import annotations

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

HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE))

COLOURS = [(255, 60, 40), (40, 200, 255), (255, 210, 0), (170, 90, 255), (60, 220, 90), (255, 120, 200)]


def pixels(cam, pts):
    """Pinhole projection of world points (all in front of the camera)."""
    import numpy as np

    local = (np.asarray(pts) - cam.pos) @ cam.rot
    f = cam.height / 2 / np.tan(np.radians(cam.fovy_deg) / 2)
    return np.stack([cam.width / 2 + f * local[:, 0] / -local[:, 2], cam.height / 2 - f * local[:, 1] / -local[:, 2]], 1)


def spread_squares(first: str, occupied: list[str], w, n: int) -> list[str]:
    """`first`, then the occupied squares farthest from those already chosen."""
    import numpy as np

    chosen = [first]
    pos = {s: w.square_center(s)[:2] for s in occupied}
    while len(chosen) < min(n, len(occupied)):
        best = max((s for s in occupied if s not in chosen),
                   key=lambda s: min(np.linalg.norm(pos[s] - pos[c]) for c in chosen))
        chosen.append(best)
    return chosen


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

    warnings.filterwarnings("ignore")
    import copy

    import mujoco
    import numpy as np
    import torch
    from PIL import Image, ImageDraw

    from episode import EpisodeRunner, load_config
    from eval_policy import drive, load_policy, piece_set_from_record
    from piece_sets import sample_piece_set

    torch.set_num_threads(2)
    cfg = load_config()
    policy, pre, post, device = load_policy(args.policy)
    rng = np.random.default_rng([args.seed, k])
    for i in range(k, len(jobs), n):
        job = jobs[i]
        if "seed" in job and "piece_set_dims" in job:
            runner = EpisodeRunner(cfg, piece_set_from_record(job), render=True)
            seed, label = job["seed"], f"training episode {job['episode_index']}"
        else:
            runner = EpisodeRunner(cfg, sample_piece_set(rng, cfg, f"marker{i}"), render=True)
            seed, label = int(rng.integers(2**62)), "random scene"
        m, d, w = runner.m, runner.d, runner.w
        base = runner.setup(np.random.default_rng(seed))
        saved = (d.qpos.copy(), d.qvel.copy(), d.act.copy(), d.ctrl.copy(), d.time)
        squares = spread_squares(base.source, sorted(base.squares), w, args.placements)
        centres = {s: w.square_center(s) for s in squares}
        background = runner.render("overhead")
        cam = runner.camera("overhead")
        runs = []
        for j, s in enumerate(squares):
            d.qpos[:], d.qvel[:], d.act[:], d.ctrl[:] = saved[:4]
            d.time = saved[4]
            mujoco.mj_forward(m, d)
            task = copy.copy(base)
            task.source, task.target = s, base.squares[s]
            r, met = drive(runner, task, policy, pre, post, device, cfg, args.seconds, seed ^ 0x5EED,
                           stop_when_done=False)
            path = met.pop("path")
            # Where it went down: path points within 6 cm of the board (else the lowest point).
            low_pts = path[path[:, 2] < w.board_top + 0.06]
            if not len(low_pts):
                low_pts = path[[int(np.argmin(path[:, 2]))]]
            near = {c: round(1000 * float(np.linalg.norm(low_pts[:, :2] - centres[c][:2], axis=1).min()), 1) for c in squares}
            low = path[int(np.argmin(path[:, 2]))]
            runs.append(dict(marked=s, piece=w.kind[task.target], closest_mm=met["closest_mm"],
                             closest_to=min(near, key=near.get), follows=min(near, key=near.get) == s,
                             lifted=met["lifted"], lowest_square=met["lowest_square"], lowest_mm=met["lowest_mm"],
                             lowest_xy=[round(float(x), 4) for x in low[:2]], closest_by_square=near))
            # Path over the overhead view.
            if j == 0:
                img = Image.fromarray(background)
            pen = ImageDraw.Draw(img)
            col = COLOURS[j % len(COLOURS)]
            pen.line([tuple(p) for p in pixels(cam, path)], fill=col, width=2)
            x, y = pixels(cam, centres[s][None])[0]
            pen.ellipse([x - 9, y - 9, x + 9, y + 9], outline=col, width=3)
        img.save(Path(args.out) / f"marker_scene_{i:02d}.png")
        lows = np.array([r["lowest_xy"] for r in runs])
        marks = np.array([centres[s][:2] for s in squares])
        pair = lambda a: np.mean([np.linalg.norm(a[p] - a[q]) for p in range(len(a)) for q in range(p + 1, len(a))])
        queue.put(dict(index=i, label=label, seed=seed, board=runner.episode_info["board"],
                       overhead=runner.episode_info["overhead"], placements=runs,
                       follow_rate=round(float(np.mean([r["follows"] for r in runs])), 2),
                       spread_ratio=round(float(pair(lows) / max(pair(marks), 1e-6)), 2)))
        runner.close()
    queue.put(None)


def main():
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--policy", required=True)
    ap.add_argument("--scenes", help="phase2_episodes.jsonl: rebuild these training episodes")
    ap.add_argument("--count", type=int, default=6, help="scenes")
    ap.add_argument("--placements", type=int, default=5, help="red-square positions per scene")
    ap.add_argument("--seconds", type=float, default=8.0, help="per placement: long enough to reach and grasp")
    ap.add_argument("--workers", type=int, default=6)
    ap.add_argument("--seed", type=int, default=2_000_003)
    ap.add_argument("--out", default=str(HERE / "reports" / "marker_test"))
    args = ap.parse_args()
    out = Path(args.out)
    out.mkdir(parents=True, exist_ok=True)
    if args.scenes:
        from eval_policy import pick_scenes

        jobs = pick_scenes(args.scenes, args.count)
    else:
        jobs = [{} for _ in range(args.count)]
    ctx = mp.get_context("spawn")
    queue = ctx.Queue()
    procs = [ctx.Process(target=worker, args=(k, args.workers, args, jobs, queue)) for k in range(min(args.workers, len(jobs)))]
    for p in procs:
        p.start()
    t0, results, finished = time.time(), [], 0
    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)
        print(f"scene {r['index']} ({r['label']}): follows {r['follow_rate']:.0%}, spread ratio {r['spread_ratio']}; "
              + ", ".join(f"{p['marked']}->{p['closest_to']} ({p['closest_mm']} mm)" for p in r["placements"]), flush=True)
    for p in procs:
        p.join()
    results.sort(key=lambda r: r["index"])
    import numpy as np

    runs = [p for r in results for p in r["placements"]]
    summary = dict(policy=args.policy, profile=os.environ.get("PHASE2_PROFILE"), scenes=len(results),
                   placements=len(runs), follow_percent=round(100 * float(np.mean([p["follows"] for p in runs])), 1),
                   chance_percent=round(100 / args.placements, 1),
                   median_closest_mm=round(float(np.median([p["closest_mm"] for p in runs])), 1),
                   median_spread_ratio=round(float(np.median([r["spread_ratio"] for r in results])), 2),
                   seconds_per_placement=args.seconds, minutes=round((time.time() - t0) / 60, 1))
    (out / "marker_results.json").write_text(json.dumps(dict(summary=summary, scenes=results), indent=1))
    lines = ["# Marker-dependence test", "",
             f"Policy `{args.policy}`. {len(results)} scenes"
             + (f" rebuilt from the training data (`{Path(args.scenes).name}`)" if args.scenes else "")
             + (f", settings profile `{summary['profile']}`" if summary["profile"] else "")
             + f". In each scene the red square was moved over {args.placements} different pieces in turn, "
             f"with everything else identical, and the policy drove for {args.seconds:.0f} s from the same start.", "",
             f"- **The gripper went closest to the marked piece in {summary['follow_percent']}% of placements** "
             f"(chance: {summary['chance_percent']}%).",
             f"- Median closest approach to the marked piece: {summary['median_closest_mm']} mm.",
             f"- Spread ratio (how far apart the gripper's lowest points are, over how far apart the marked "
             f"pieces are): median {summary['median_spread_ratio']}. Near 1 means it follows the markers; "
             f"near 0 means it goes to the same place whatever is marked.", "",
             "Images `marker_scene_NN.png`: gripper paths over the overhead view, one colour per placement, "
             "ring on the marked piece.", ""]
    for r in results:
        lines += [f"## Scene {r['index']}: {r['label']}", "",
                  f"Follows {r['follow_rate']:.0%}, spread ratio {r['spread_ratio']}.", "",
                  "| marked | piece | closest to marked (mm) | went closest to | lifted | lowest over |",
                  "|---|---|---|---|---|---|"]
        lines += [f"| {p['marked']} | {p['piece']} | {p['closest_mm']} | {p['closest_to']} | "
                  f"{'yes' if p['lifted'] else 'no'} | {p['lowest_square'] or '-'} |" for p in r["placements"]] + [""]
    (out / "marker_report.md").write_text("\n".join(lines) + "\n")
    print(json.dumps(summary, indent=1))


if __name__ == "__main__":
    main()