#!/usr/bin/env python3
"""How accurately does SONIC track a commanded right-wrist VR target?

The planned manipulation policy acts by moving that target, so its steady-state
tracking error is the floor on grasp precision -- a policy cannot place the hand
better than the controller beneath it. This sweeps a grid of targets in the
pelvis frame, holds each until the arm settles, and reports commanded vs
achieved. The reachable subset also bounds where objects may be spawned.

Legs are given a constant standing reference (teleop mode), so any base motion
is SONIC reacting to the arm, not commanded locomotion.

  cd simulation
  ./run_isaaclab.sh --python scripts/probe_wrist_tracking.py --headless
"""

from __future__ import annotations

import argparse
import sys
from pathlib import Path

from isaaclab.app import AppLauncher

if hasattr(sys.stdout, "reconfigure"):
    sys.stdout.reconfigure(line_buffering=True)

_PROJECT = Path(__file__).resolve().parents[2]
_DEFAULT_CKPT = _PROJECT / "checkpoints" / "sonic" / "sonic_v1_1"

parser = argparse.ArgumentParser(description="SONIC wrist-target tracking accuracy.")
parser.add_argument("--checkpoint", type=Path, default=_DEFAULT_CKPT)
parser.add_argument("--hands", choices=("dex3", "inspire", "wuji"), default="wuji")
parser.add_argument("--hold", type=int, default=100, help="control steps per target (50 Hz)")
parser.add_argument("--settle", type=int, default=25, help="steps averaged at the end of each hold")
parser.add_argument(
    "--x", type=float, nargs="+", default=[0.25, 0.40, 0.55], help="forward, pelvis frame"
)
parser.add_argument("--y", type=float, nargs="+", default=[-0.30, -0.15, 0.0], help="lateral")
parser.add_argument("--z", type=float, nargs="+", default=[-0.05, 0.10, 0.25], help="vertical")
parser.add_argument("--output", type=Path, default=None, help="npz of the raw results")
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()

app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app

import numpy as np  # noqa: E402
import torch  # noqa: E402

import isaaclab.sim as sim_utils  # noqa: E402
from isaaclab.assets import Articulation  # noqa: E402
from isaaclab.sim import SimulationContext  # noqa: E402

from v2d_sim.sonic import (  # noqa: E402
    ACTION_CLIP,
    DEFAULT_ANGLES,
    G1_ACTION_SCALE,
    G1_SONIC_JOINT_NAMES,
    NUM_JOINTS,
    History,
    SonicOnnxAgent,
    pack_decoder,
    pack_encoder,
    vr3_local_from_bodies,
)
from v2d_sim.sonic.robot_cfg import g1_sonic_articulation_cfg  # noqa: E402

_CTRL_DT = 0.02
_SIM_DT = 0.005
_DECIMATION = 4
_PELVIS_Z = 0.78


def _np(x) -> np.ndarray:
    if hasattr(x, "detach"):
        return x.detach().cpu().numpy()
    return np.asarray(x)


def _row(x) -> np.ndarray:
    a = np.asarray(_np(x), dtype=np.float64)
    return a[0] if a.ndim >= 2 else a


def _xyzw_to_wxyz(q: np.ndarray) -> np.ndarray:
    q = np.asarray(q, dtype=np.float64)
    return np.stack([q[..., 3], q[..., 0], q[..., 1], q[..., 2]], axis=-1)


def main() -> None:
    ckpt = args_cli.checkpoint.resolve()
    agent = SonicOnnxAgent(ckpt)

    sim = SimulationContext(sim_utils.SimulationCfg(dt=_SIM_DT, device=args_cli.device))
    sim_utils.GroundPlaneCfg().func("/World/Ground", sim_utils.GroundPlaneCfg())
    sim_utils.DomeLightCfg(intensity=2000.0).func(
        "/World/Light", sim_utils.DomeLightCfg(intensity=2000.0)
    )
    robot = Articulation(
        g1_sonic_articulation_cfg("/World/Robot", init_pos=(0.0, 0.0, _PELVIS_Z), hands=args_cli.hands)
    )
    sim.reset()

    device = sim.device
    names = list(robot.joint_names)
    sonic_ids = [names.index(n) for n in G1_SONIC_JOINT_NAMES]
    default = np.asarray(DEFAULT_ANGLES, dtype=np.float64)
    scale = np.asarray(G1_ACTION_SCALE, dtype=np.float64)

    q0 = np.zeros(len(names), dtype=np.float64)
    q0[sonic_ids] = default
    robot.write_root_pose_to_sim_index(
        root_pose=torch.tensor(
            [[0.0, 0.0, _PELVIS_Z, 0.0, 0.0, 0.0, 1.0]], dtype=torch.float32, device=device
        )
    )
    robot.write_root_velocity_to_sim_index(root_velocity=torch.zeros(1, 6, device=device))
    robot.write_joint_state_to_sim_index(
        position=torch.tensor(q0, dtype=torch.float32, device=device).unsqueeze(0),
        velocity=torch.zeros(1, len(names), device=device),
        full_data=True,
    )
    robot.write_data_to_sim()
    sim.step()
    robot.update(_SIM_DT)

    def proprio():
        q = _row(robot.data.joint_pos)[sonic_ids]
        dq = _row(robot.data.joint_vel)[sonic_ids]
        return (
            q,
            dq,
            _row(robot.data.root_ang_vel_b),
            _row(robot.data.projected_gravity_b),
            _row(robot.data.root_pos_w),
            _xyzw_to_wxyz(_row(robot.data.root_quat_w)),
        )

    def measure(pelvis_pos, pelvis_quat):
        bp = np.asarray(_np(robot.data.body_pos_w), dtype=np.float64)
        bq = _xyzw_to_wxyz(np.asarray(_np(robot.data.body_quat_w), dtype=np.float64))
        if bp.ndim == 3:
            bp, bq = bp[0], bq[0]
        return vr3_local_from_bodies(robot.body_names, bp, bq, pelvis_pos, pelvis_quat)

    # Constant standing reference: one frame, so every future index clamps to it.
    ref_q = default[None, :].copy()
    ref_dq = np.zeros((1, NUM_JOINTS), dtype=np.float64)
    ref_quat = np.array([[1.0, 0.0, 0.0, 0.0]], dtype=np.float64)

    hist = History()
    last_action = np.zeros(NUM_JOINTS, dtype=np.float32)
    q, dq, ang, grav, pos, quat = proprio()
    hist.fill(ang_vel=ang, q_rel=q - default, dq=dq, action=last_action, gravity=grav)

    stand_pos, stand_quat = measure(pos, quat)
    print(f"[probe] hands={args_cli.hands}  joints={len(names)}  sonic={len(sonic_ids)}", flush=True)
    print(
        f"[probe] standing wrist R = {np.array2string(stand_pos[1], precision=3)}"
        f"  (pelvis frame)  root_z={pos[2]:.3f}",
        flush=True,
    )

    targets = np.array([[x, y, z] for x in args_cli.x for y in args_cli.y for z in args_cli.z])
    print(f"[probe] {len(targets)} targets, {args_cli.hold} steps each\n", flush=True)
    print(f"{'commanded (x,y,z)':>26s} {'achieved':>26s} {'err':>7s} {'root_z':>7s} {'drift':>7s}")

    achieved = np.zeros_like(targets)
    errors = np.zeros(len(targets))
    root_zs = np.zeros(len(targets))
    drifts = np.zeros(len(targets))

    vr_pos = np.tile(stand_pos, (1, 1, 1))
    vr_quat = np.tile(stand_quat, (1, 1, 1))

    for i, target in enumerate(targets):
        vr_pos[0, 1] = target
        tail: list[np.ndarray] = []
        for step in range(args_cli.hold):
            if not simulation_app.is_running():
                break
            q, dq, ang, grav, pos, quat = proprio()
            enc = pack_encoder(
                mode="teleop",
                t=0,
                joint_pos=ref_q,
                joint_vel=ref_dq,
                ref_quat_wxyz=ref_quat,
                robot_quat_wxyz=quat,
                vr_local_pos=vr_pos,
                vr_local_quat=vr_quat,
            )
            action = np.clip(agent.decode(pack_decoder(agent.encode(enc), hist)), -ACTION_CLIP, ACTION_CLIP)
            last_action = action.astype(np.float32)
            robot.set_joint_position_target_index(
                target=torch.tensor(
                    default + scale * last_action, dtype=torch.float32, device=device
                ).unsqueeze(0),
                joint_ids=sonic_ids,
            )
            robot.write_data_to_sim()
            for _ in range(_DECIMATION):
                sim.step()
                robot.update(_SIM_DT)
            q, dq, ang, grav, pos, quat = proprio()
            hist.push(ang_vel=ang, q_rel=q - default, dq=dq, action=last_action, gravity=grav)
            if step >= args_cli.hold - args_cli.settle:
                tail.append(measure(pos, quat)[0][1])

        got = np.mean(tail, axis=0) if tail else np.full(3, np.nan)
        achieved[i] = got
        errors[i] = float(np.linalg.norm(got - target))
        root_zs[i] = pos[2]
        # Spread over the averaging window: large means it never settled.
        drifts[i] = float(np.linalg.norm(np.ptp(np.asarray(tail), axis=0))) if tail else np.nan
        print(
            f"{np.array2string(target, precision=3, floatmode='fixed'):>26s} "
            f"{np.array2string(got, precision=3, floatmode='fixed'):>26s} "
            f"{errors[i]:7.4f} {root_zs[i]:7.3f} {drifts[i]:7.4f}",
            flush=True,
        )

    ok = root_zs > 0.6
    print(f"\n[probe] {int(ok.sum())}/{len(targets)} targets kept the robot standing (root_z > 0.6)")
    if ok.any():
        e = errors[ok]
        print(
            f"[probe] tracking error over those: median {np.median(e):.4f} m  "
            f"mean {e.mean():.4f} m  p90 {np.percentile(e, 90):.4f} m  max {e.max():.4f} m"
        )
        best = int(np.argmin(np.where(ok, errors, np.inf)))
        worst = int(np.argmax(np.where(ok, errors, -np.inf)))
        print(f"[probe] best  {np.array2string(targets[best], precision=2)} -> {errors[best]:.4f} m")
        print(f"[probe] worst {np.array2string(targets[worst], precision=2)} -> {errors[worst]:.4f} m")

    if args_cli.output:
        args_cli.output.parent.mkdir(parents=True, exist_ok=True)
        np.savez(
            args_cli.output,
            targets=targets,
            achieved=achieved,
            errors=errors,
            root_z=root_zs,
            drift=drifts,
            stand_wrist=stand_pos[1],
        )
        print(f"[probe] wrote {args_cli.output}")


if __name__ == "__main__":
    main()
    simulation_app.close()