#!/usr/bin/env python3
"""Generate a point-to-point walking reference with the GEAR kinematic planner.

SONIC is a tracker, not a planner: it follows a reference motion and cannot
invent a gait. GEAR splits those roles, and the planner half lives in
``motionbricks`` (root + pose + VQ-VAE backbones plus the idle/walk clip
library). This drives that planner toward a world xy target and writes the
resulting MuJoCo ``qpos`` trajectory, which SONIC then tracks in ``g1`` mode.

The planner is a self-contained kinematic rollout -- it conditions on the
frames it generated itself, never on a physics robot -- so generating offline
is equivalent to running it in the loop, and it keeps the heavy motionbricks
dependency stack out of the Isaac Lab process.

Runs in ``simulation/.venv-planner``, not the Isaac Lab venv:

  ./jobs/gen_walk_reference.sh --target 1.5 -0.35
"""

from __future__ import annotations

import argparse
import os
import sys
from pathlib import Path
from types import SimpleNamespace

_PROJECT = Path(__file__).resolve().parents[2]
_MB = _PROJECT / "third-party" / "GR00T-WholeBodyControl" / "motionbricks"
if str(_MB) not in sys.path:
    sys.path.insert(0, str(_MB))

import numpy as np
import torch as t


def _parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="GEAR planner walk reference for SONIC.")
    p.add_argument(
        "--target",
        type=float,
        nargs=2,
        default=(1.5, -0.35),
        metavar=("X", "Y"),
        help="world xy the pelvis should walk to; default is the WalkGrab object.",
    )
    p.add_argument(
        "--standoff",
        type=float,
        default=0.85,
        help="stop this far short of --target along the approach ray, metres. The "
        "robot has to end up beside the object, not on top of it, and the planner "
        "coasts a variable 0.3-0.8 m past whatever goal it is given because it "
        "regenerates in multi-frame blocks. Check the reported final distance.",
    )
    p.add_argument(
        "--stop-radius",
        type=float,
        default=0.35,
        help="switch the planner to idle once the root is this close to the "
        "stand-off point, metres.",
    )
    p.add_argument("--max-seconds", type=float, default=12.0)
    p.add_argument(
        "--settle-seconds",
        type=float,
        default=1.0,
        help="extra idle time recorded after arrival so the reference ends standing.",
    )
    p.add_argument("--mode", default="walk", choices=("walk", "slow_walk"))
    p.add_argument(
        "--device",
        default=None,
        help="default: cuda when a GPU is visible, else cpu (slow but works on a login node).",
    )
    p.add_argument("--generate-dt", type=float, default=2.0)
    p.add_argument("--out", type=Path, default=None)
    return p.parse_args()


def _build_agent(args: argparse.Namespace):
    """Instantiate the planner directly.

    ``motionbricks``' own ``navigation_demo`` is not reusable here: it builds a
    WASD controller that imports ``pynput``, which needs an X server and dies on
    a headless node. Everything below the controller is what we actually want.

    The checkpoints' ``hparams.yaml`` store skeleton and VQ-VAE paths relative
    to the motionbricks root, so the process has to sit there while they load.
    """
    os.chdir(_MB)
    from motionbricks.exp_setup.experiment import test
    from motionbricks.motion_backbone.demo.full_agent import full_navigation_agent
    from motionbricks.motion_backbone.inference.motion_inference import motion_inference

    out_dir = _MB / "out"
    clips_ckpt = out_dir / "G1-clip.ckpt"
    if clips_ckpt.stat().st_size < 10_000:
        raise SystemExit(
            f"{clips_ckpt} is still a Git LFS pointer.\n"
            "run simulation/scripts/fetch_gear_lfs.sh first"
        )

    conf_args = SimpleNamespace(
        result_dir=str(out_dir),
        data_root=str(_MB / "datasets"),
        explicit_dataset_folder=None,
        EXP="default",
        return_model_configs=True,
        # The clip library is cached in G1-clip.ckpt, so no motion dataset is
        # needed; asking for a dataloader here would require the training data.
        return_dataloader=False,
    )
    if args.device == "cpu":
        # Two CUDA assumptions are baked into the training-time code path:
        # test() calls torch.cuda.set_device because the config said "gpu", and
        # the pose model torch.load()s its VQ-VAE checkpoint without a
        # map_location, so the CUDA-tagged storages fail to deserialize.
        # Neither matters for inference; both are neutralised just for the call.
        real_set_device, real_load = t.cuda.set_device, t.load

        def _cpu_load(*a, **kw):
            kw.setdefault("map_location", "cpu")
            return real_load(*a, **kw)

        t.cuda.set_device = lambda *a, **k: None
        t.load = _cpu_load
        try:
            models, confs = test(conf_args)
        finally:
            t.cuda.set_device, t.load = real_set_device, real_load
    else:
        models, confs = test(conf_args)

    for name in ("pose", "root"):
        state = t.load(confs[name].ckpt_path, map_location=args.device)["state_dict"]
        models[name].load_state_dict(state)

    inferencer = motion_inference(models, models["pose"].args, device=args.device)
    agent = full_navigation_agent(
        inferencer,
        None,
        device=args.device,
        speed_scale=[1.0, 1.0],
        skeleton_xml=str(_MB / "assets" / "skeletons" / "g1" / "g1.xml"),
        clips="G1",
        ckpt_path=str(clips_ckpt),
        reprocess_clips=False,
        val_dataloader=None,
    ).to(args.device)
    return agent, inferencer


def main() -> None:
    args = _parse_args()
    if args.device is None:
        args.device = "cuda" if t.cuda.is_available() else "cpu"
    # Resolved before _build_agent chdirs into the motionbricks root.
    args.out = (args.out or (_PROJECT / "simulation" / "assets" / "walk_reference.npz")).resolve()
    from motionbricks.motion_backbone.demo.clips import clip_holder_G1

    agent, inferencer = _build_agent(args)
    fps = int(inferencer.motion_rep.fps)
    min_tok = inferencer._args["min_tokens"]
    max_tok = inferencer._args["max_tokens"]

    clip_names = list(clip_holder_G1.CLIPS.keys())
    walk_id = clip_names.index(args.mode)
    idle_id = clip_names.index("idle")

    def allowed_tokens(mode_name: str) -> t.Tensor:
        spec = clip_holder_G1.CLIPS[mode_name].get("allowed_pred_num_tokens")
        if spec is not None:
            return t.tensor(spec).view([1, -1])
        return t.ones(max_tok - min_tok + 1, dtype=t.int).view([1, -1])

    target = np.asarray(args.target, dtype=np.float64)
    # The planner is steered at the stand-off point; everything is still
    # reported against the object so the numbers mean what they say.
    approach = target / (np.linalg.norm(target) + 1e-9)
    goal = target - args.standoff * approach
    # Matches motionbricks' own demo: replan every 8 frames, times generate_dt.
    controller_dt = (8.0 / fps) * float(args.generate_dt)
    max_steps = int(args.max_seconds * fps)
    settle_steps = int(args.settle_seconds * fps)

    print(f"[planner] device={args.device} fps={fps} tokens=[{min_tok},{max_tok}]", flush=True)
    print(f"[planner] modes={clip_names}", flush=True)
    print(
        f"[planner] object={tuple(np.round(target, 3))} standoff={args.standoff} "
        f"goal={tuple(np.round(goal, 3))} stop_radius={args.stop_radius}",
        flush=True,
    )

    agent.reset()
    frames: list[np.ndarray] = []
    arrived_at: int | None = None
    last_movement = np.array([approach[0], approach[1], 0.0])

    for step in range(max_steps):
        qpos = np.asarray(agent.get_next_frame(), dtype=np.float64)
        frames.append(qpos.copy())

        delta = goal - qpos[:2]
        dist = float(np.linalg.norm(delta))
        obj_dist = float(np.linalg.norm(target - qpos[:2]))
        if dist < args.stop_radius and arrived_at is None:
            arrived_at = step
            print(
                f"[planner] idling at step {step} ({step / fps:.2f}s), "
                f"{obj_dist:.3f} m from the object",
                flush=True,
            )
        if arrived_at is not None and step - arrived_at >= settle_steps:
            break

        walking = arrived_at is None
        if walking:
            movement = np.array([delta[0], delta[1], 0.0]) / (dist + 1e-8)
            last_movement = movement
        else:
            # The planner coasts past the stand-off point, which flips the sign
            # of `delta`. Steering by it here would spin the robot 180 degrees
            # on the spot, so the approach heading is held instead.
            movement = last_movement
        # Always look at the object, never at the stand-off point, so the robot
        # finishes square to what it is about to reach for.
        to_object = target - qpos[:2]
        facing = np.array([to_object[0], to_object[1], 0.0])
        facing /= np.linalg.norm(facing) + 1e-8
        mode_id = walk_id if walking else idle_id
        mode_name = args.mode if walking else "idle"

        control = {
            "movement_direction": t.from_numpy(movement).float().view([1, -1]),
            "facing_direction": t.from_numpy(facing).float().view([1, -1]),
            "mode": t.tensor([mode_id]).view([1, -1]),
            "allowed_pred_num_tokens": allowed_tokens(mode_name),
            "context_mujoco_qpos": agent.get_context_mujoco_qpos(),
        }
        with t.no_grad():
            agent.generate_new_frames(control, controller_dt)

        if step % (fps // 2) == 0:
            print(
                f"  t={step / fps:5.2f}s  root=({qpos[0]:+.3f},{qpos[1]:+.3f})  "
                f"to_object={obj_dist:.3f}  mode={mode_name}",
                flush=True,
            )

    qpos = np.stack(frames, axis=0)
    travelled = float(np.linalg.norm(qpos[-1, :2] - qpos[0, :2]))
    final_dist = float(np.linalg.norm(target - qpos[-1, :2]))
    print(
        f"[planner] {len(qpos)} frames ({len(qpos) / fps:.2f}s), travelled {travelled:.3f} m, "
        f"ends {final_dist:.3f} m from the object",
        flush=True,
    )
    if arrived_at is None:
        print("[planner] WARNING never reached the stop radius", flush=True)

    out = args.out
    out.parent.mkdir(parents=True, exist_ok=True)
    np.savez(
        out,
        # (T, 36): root xyz, root quat wxyz, then 29 joints in MuJoCo order.
        qpos=qpos.astype(np.float32),
        fps=np.int32(fps),
        target=target.astype(np.float32),
        stop_radius=np.float32(args.stop_radius),
        arrived_frame=np.int32(-1 if arrived_at is None else arrived_at),
    )
    print(f"[planner] wrote {out}", flush=True)


if __name__ == "__main__":
    main()