#!/usr/bin/env python3
"""Data Validation engine (stage 4) — runs in the retarget venv (numpy<2 + h5py + scipy).

Operates on the transformed output (store/<run>/transform/retargeted/*/retargeted.hdf5) which the
retarget engine already annotates with rich QA (`qa`, `m4`, `ik_errors_*`). This script:

  mode=qa          → per-clip Trajectory Quality & Hygiene report (feasibility + motion + integrity)
  mode=correct     → clamp joint limits + smooth vel/acc/jerk → corrected hdf5 (no retime)
  mode=similarity  → transformed-ego vs teleop action-distribution distance + score

Writes JSON to --out (the API reads it back). Invoked exactly like transform_batch.py.
"""
from __future__ import annotations

import argparse
import glob
import json
import os
import xml.etree.ElementTree as ET

import numpy as np

# ---- tunable thresholds (placeholder defaults; surfaced in the report so the UI can show them) ----
TH = {
    "ik_ok_m": 0.02,          # per-frame IK error under this (m) = frame "solved"
    "ik_success_min": 0.90,   # >= this fraction of frames solved → feasibility OK
    "limit_viol_warn": 0.001, # fraction of (frame×joint) samples out of joint range
    "limit_viol_fail": 0.02,
    "selfcol_min_dist_m": 0.02,   # min link clearance (m) below which = collision risk
    "continuity_max_drad": 0.5,   # max per-frame per-joint jump (rad) before "teleport"
    "chatter_warn": 2.0,          # oscillation rate (from retarget qa) warn level
}

FINGER_NAMES = ("left_finger", "right_finger", "finger")


def joint_limits(xml_path: str) -> dict:
    """Parse <joint name range> from yam_real.xml → {base_name: (lo, hi)} (radians)."""
    out = {}
    try:
        root = ET.parse(xml_path).getroot()
        for j in root.iter("joint"):
            name, rng = j.get("name"), j.get("range")
            if name and rng:
                lo, hi = (float(x) for x in rng.split())
                out[name] = (lo, hi)
    except Exception:
        pass
    return out


def limits_vector(joint_names: list, lims: dict) -> np.ndarray:
    """Build (14, 2) lo/hi aligned to the clip's joint_names (strip R_/L_ prefix → base)."""
    rows = []
    for jn in joint_names:
        base = jn.split("_", 1)[1] if (jn.startswith("R_") or jn.startswith("L_")) else jn
        if base in lims:
            rows.append(lims[base])
        elif any(f in base for f in FINGER_NAMES):
            # fingers: use whichever finger range exists
            fr = next((lims[k] for k in lims if "finger" in k), (-1e9, 1e9))
            rows.append(fr)
        else:
            rows.append((-1e9, 1e9))
    return np.array(rows, dtype=float)


def _attr_json(h, key):
    v = h.attrs.get(key)
    if v is None:
        return {}
    try:
        return json.loads(v if isinstance(v, str) else v.decode())
    except Exception:
        return {}


def qa_one(path: str, lims: dict) -> dict:
    import h5py
    with h5py.File(path, "r") as h:
        jp = np.asarray(h["joint_positions"][:], dtype=float)          # (T, 14)
        ts = np.asarray(h["timestamps"][:], dtype=float) if "timestamps" in h else None
        fps = float(h.attrs.get("fps", 30.0))
        jn = json.loads(h.attrs.get("joint_names", "[]"))
        ik_L = np.asarray(h["ik_errors_L"][:], dtype=float) if "ik_errors_L" in h else None
        ik_R = np.asarray(h["ik_errors_R"][:], dtype=float) if "ik_errors_R" in h else None
        qa = _attr_json(h, "qa")
        m4 = _attr_json(h, "m4")
        clip = h.attrs.get("clip", os.path.basename(os.path.dirname(path)))
        task = h.attrs.get("task", "")

    T = jp.shape[0]
    dt = 1.0 / fps if fps else 1.0 / 30

    # --- integrity ---
    finite = bool(np.isfinite(jp).all())
    dframe = np.abs(np.diff(jp, axis=0)) if T > 1 else np.zeros((0, jp.shape[1]))
    max_jump = float(dframe.max()) if dframe.size else 0.0
    frame_drops = 0
    if ts is not None and ts.size > 2:
        gaps = np.diff(ts)
        frame_drops = int(np.sum(gaps > 1.8 * np.median(gaps)))

    # --- feasibility ---
    lo_hi = limits_vector(jn, lims) if jn else np.tile([-1e9, 1e9], (jp.shape[1], 1))
    below = jp < lo_hi[:, 0]
    above = jp > lo_hi[:, 1]
    viol = below | above
    limit_viol_frac = float(viol.mean()) if viol.size else 0.0
    per_joint_viol = viol.mean(axis=0).tolist() if viol.size else []

    def ik_frac(ik):
        return float(np.mean(ik <= TH["ik_ok_m"])) if ik is not None and ik.size else None
    ik_success = [f for f in (ik_frac(ik_R), ik_frac(ik_L)) if f is not None]
    ik_success_min = min(ik_success) if ik_success else None

    # self-collision from the retarget qa (min link clearance across arms)
    selfcol_dists = []
    for arm in ("R", "L"):
        d = ((qa.get("arms", {}).get(arm, {}) or {}).get("metrics", {}) or {}).get("selfcol_min_dist")
        if d is not None:
            selfcol_dists.append(float(d))
    selfcol_min = min(selfcol_dists) if selfcol_dists else None

    # --- motion (finite-diff on joints; peaks; plus retarget smoothness/chatter/saturation) ---
    vel = np.diff(jp, axis=0) / dt if T > 1 else np.zeros((0, jp.shape[1]))
    acc = np.diff(vel, axis=0) / dt if vel.shape[0] > 1 else np.zeros((0, jp.shape[1]))
    jerk = np.diff(acc, axis=0) / dt if acc.shape[0] > 1 else np.zeros((0, jp.shape[1]))
    peak_vel = float(np.abs(vel).max()) if vel.size else 0.0
    peak_acc = float(np.abs(acc).max()) if acc.size else 0.0
    peak_jerk = float(np.abs(jerk).max()) if jerk.size else 0.0

    def arm_metric(key):
        vals = []
        for arm in ("R", "L"):
            v = ((qa.get("arms", {}).get(arm, {}) or {}).get("metrics", {}) or {}).get(key)
            if v is not None:
                vals.append(float(v))
        return vals
    ldlj = arm_metric("ldlj")            # log dimensionless jerk (smoothness; more negative = jerkier)
    chatter = arm_metric("chatter_rate")
    saturation = arm_metric("saturation_frac")

    # --- verdict: blend our checks with the retarget's own qa_verdict ---
    reasons = []
    verdict = "PASS"

    def demote(level, why):
        nonlocal verdict
        order = {"PASS": 0, "WARN": 1, "FAIL": 2}
        if order[level] > order[verdict]:
            verdict = level
        reasons.append(why)

    if not finite:
        demote("FAIL", "non-finite joint values")
    if max_jump > TH["continuity_max_drad"]:
        demote("WARN", f"discontinuity {max_jump:.2f} rad/frame")
    if limit_viol_frac > TH["limit_viol_fail"]:
        demote("FAIL", f"joint-limit violation {limit_viol_frac*100:.1f}%")
    elif limit_viol_frac > TH["limit_viol_warn"]:
        demote("WARN", f"minor limit violation {limit_viol_frac*100:.2f}%")
    if ik_success_min is not None and ik_success_min < TH["ik_success_min"]:
        demote("FAIL" if ik_success_min < 0.8 else "WARN", f"IK solved {ik_success_min*100:.0f}%")
    if selfcol_min is not None and selfcol_min < TH["selfcol_min_dist_m"]:
        demote("WARN", f"self-collision clearance {selfcol_min*100:.1f}cm")
    if chatter and max(chatter) > TH["chatter_warn"]:
        demote("WARN", f"chatter {max(chatter):.1f}")
    rv = qa.get("verdict")
    if rv == "FAIL":
        demote("WARN", "retarget QA flagged FAIL")   # respect but don't hard-fail on retarget-only

    return {
        "clip": clip, "task": task, "n_frames": T, "fps": fps,
        "verdict": verdict, "reasons": reasons,
        "feasibility": {
            "limit_viol_frac": round(limit_viol_frac, 4),
            "per_joint_viol": [round(x, 4) for x in per_joint_viol],
            "ik_success_min": None if ik_success_min is None else round(ik_success_min, 3),
            "selfcol_min_dist_m": None if selfcol_min is None else round(selfcol_min, 4),
        },
        "motion": {
            "peak_vel": round(peak_vel, 3), "peak_acc": round(peak_acc, 2), "peak_jerk": round(peak_jerk, 1),
            "ldlj": [round(x, 2) for x in ldlj], "chatter": [round(x, 2) for x in chatter],
            "saturation": [round(x, 3) for x in saturation],
        },
        "integrity": {"finite": finite, "max_jump_rad": round(max_jump, 3), "frame_drops": frame_drops},
        "retarget_verdict": rv,
    }


def run_qa(retargeted_dir: str, xml_path: str) -> dict:
    lims = joint_limits(xml_path)
    files = sorted(glob.glob(os.path.join(retargeted_dir, "*", "retargeted.hdf5")))
    clips = []
    for f in files:
        try:
            clips.append(qa_one(f, lims))
        except Exception as e:
            clips.append({"clip": os.path.basename(os.path.dirname(f)), "verdict": "FAIL",
                          "reasons": [f"read error: {e}"], "error": str(e)})
    counts = {v: sum(1 for c in clips if c.get("verdict") == v) for v in ("PASS", "WARN", "FAIL")}
    certified = [c["clip"] for c in clips if c.get("verdict") in ("PASS", "WARN")]
    return {
        "mode": "qa",
        "n_clips": len(clips),
        "counts": counts,
        "certified": certified,
        "clips": clips,
        "thresholds": TH,
    }


def _peak_jerk(jp: np.ndarray, dt: float) -> float:
    if jp.shape[0] < 4:
        return 0.0
    j = np.diff(jp, n=3, axis=0) / (dt ** 3)
    return float(np.abs(j).max()) if j.size else 0.0


def correct_one(path: str, lims: dict) -> dict:
    """Clamp to joint limits + smooth (Savitzky-Golay) — no retime. Writes corrected.hdf5 alongside."""
    import h5py
    from scipy.signal import savgol_filter
    with h5py.File(path, "r") as h:
        jp = np.asarray(h["joint_positions"][:], dtype=float)
        jn = json.loads(h.attrs.get("joint_names", "[]"))
        fps = float(h.attrs.get("fps", 30.0))
        clip = h.attrs.get("clip", os.path.basename(os.path.dirname(path)))
        keep = {k: np.asarray(h[k][:]) for k in h.keys() if k != "joint_positions"}
        attrs = dict(h.attrs)
    T = jp.shape[0]
    dt = 1.0 / fps if fps else 1.0 / 30
    lo_hi = limits_vector(jn, lims) if jn else np.tile([-1e9, 1e9], (jp.shape[1], 1))

    before = {"limit_viol_frac": round(float(((jp < lo_hi[:, 0]) | (jp > lo_hi[:, 1])).mean()), 4),
              "peak_jerk": round(_peak_jerk(jp, dt), 1)}

    clamped = np.clip(jp, lo_hi[:, 0], lo_hi[:, 1])
    win = min(T if T % 2 else T - 1, 7)          # odd window ≤ T, ≤ 7
    if win >= 5 and T > win:
        smoothed = savgol_filter(clamped, win, 3, axis=0)
        smoothed = np.clip(smoothed, lo_hi[:, 0], lo_hi[:, 1])   # re-clamp after smoothing
    else:
        smoothed = clamped

    after = {"limit_viol_frac": round(float(((smoothed < lo_hi[:, 0]) | (smoothed > lo_hi[:, 1])).mean()), 4),
             "peak_jerk": round(_peak_jerk(smoothed, dt), 1)}

    out_path = os.path.join(os.path.dirname(path), "corrected.hdf5")
    with h5py.File(out_path, "w") as o:
        o.create_dataset("joint_positions", data=smoothed)
        for k, v in keep.items():
            o.create_dataset(k, data=v)
        for k, v in attrs.items():
            o.attrs[k] = v
        o.attrs["corrected"] = True
        o.attrs["smooth_window"] = int(win)
    return {"clip": clip, "before": before, "after": after,
            "jerk_reduction": round(before["peak_jerk"] - after["peak_jerk"], 1),
            "smooth_window": int(win), "output": out_path}


def run_correct(retargeted_dir: str, xml_path: str) -> dict:
    lims = joint_limits(xml_path)
    files = sorted(glob.glob(os.path.join(retargeted_dir, "*", "retargeted.hdf5")))
    clips, tot_j = [], 0.0
    for f in files:
        try:
            c = correct_one(f, lims)
            clips.append(c)
            tot_j += max(0.0, c["jerk_reduction"])
        except Exception as e:
            clips.append({"clip": os.path.basename(os.path.dirname(f)), "error": str(e)})
    ok = [c for c in clips if "error" not in c]
    return {"mode": "correct", "n_clips": len(clips), "corrected": len(ok),
            "mean_jerk_reduction": round(tot_j / max(1, len(ok)), 1), "clips": clips}


def _ego_actions(retargeted_dir: str, max_frames: int = 60000) -> np.ndarray:
    import h5py
    arrs, n = [], 0
    for f in sorted(glob.glob(os.path.join(retargeted_dir, "*", "retargeted.hdf5"))):
        with h5py.File(f, "r") as h:
            a = np.asarray(h["joint_positions"][:], dtype=float)
        arrs.append(a); n += a.shape[0]
        if n >= max_frames:
            break
    return np.concatenate(arrs) if arrs else np.zeros((0, 14))


def _teleop_actions(teleop_dir: str, max_frames: int = 60000) -> np.ndarray:
    import pyarrow.parquet as pq
    arrs, n = [], 0
    for f in sorted(glob.glob(os.path.join(teleop_dir, "**", "*.parquet"), recursive=True)):
        try:
            col = pq.read_table(f, columns=["action"]).column("action").to_pylist()
        except Exception:
            continue
        a = np.array(col, dtype=float)
        if a.ndim != 2:
            continue
        arrs.append(a); n += a.shape[0]
        if n >= max_frames:
            break
    return np.concatenate(arrs) if arrs else np.zeros((0, 14))


def run_similarity(retargeted_dir: str, teleop_dir: str) -> dict:
    """Task-agnostic execution similarity: do transformed-ego action distributions look like
    expert teleop? Per-joint Wasserstein (action + velocity) + range coverage + histograms."""
    from scipy.stats import wasserstein_distance
    ego = _ego_actions(retargeted_dir)
    tel = _teleop_actions(teleop_dir)
    if ego.size == 0 or tel.size == 0:
        return {"mode": "similarity", "error": "no ego or teleop actions",
                "ego_frames": int(ego.shape[0]), "teleop_frames": int(tel.shape[0])}

    D = min(ego.shape[1], tel.shape[1])
    per_joint = []
    for j in range(D):
        e, t = ego[:, j], tel[:, j]
        s = t.std() or 1.0
        wd = wasserstein_distance(e / s, t / s)
        ev, tv = np.diff(e), np.diff(t)
        sv = tv.std() or 1.0
        wdv = wasserstein_distance(ev / sv, tv / sv) if ev.size and tv.size else 0.0
        lo, hi = np.percentile(t, [1, 99])
        cov = float(np.mean((e >= lo) & (e <= hi)))
        # shared-range histograms for the UI overlay
        rlo, rhi = float(min(e.min(), t.min())), float(max(e.max(), t.max()))
        be, _ = np.histogram(e, bins=20, range=(rlo, rhi), density=True)
        bt, edges = np.histogram(t, bins=20, range=(rlo, rhi), density=True)
        per_joint.append({
            "joint": j, "w_action": round(float(wd), 3), "w_vel": round(float(wdv), 3),
            "sim": round(float(np.exp(-wd)), 3), "coverage": round(cov, 3),
            "hist": {"edges": [round(x, 3) for x in edges.tolist()],
                     "ego": [round(x, 3) for x in be.tolist()],
                     "teleop": [round(x, 3) for x in bt.tolist()]},
        })
    score = float(np.mean([p["sim"] for p in per_joint]))
    coverage = float(np.mean([p["coverage"] for p in per_joint]))
    verdict = "PASS" if (score >= 0.6 and coverage >= 0.7) else ("WARN" if score >= 0.4 else "FAIL")
    return {"mode": "similarity", "score": round(score, 3), "coverage": round(coverage, 3),
            "verdict": verdict, "ego_frames": int(ego.shape[0]), "teleop_frames": int(tel.shape[0]),
            "n_joints": D, "per_joint": per_joint}


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--mode", default="qa", choices=["qa", "correct", "similarity"])
    ap.add_argument("--retargeted", required=True, help="store/<run>/transform/retargeted")
    ap.add_argument("--xml", default=os.path.join(os.path.dirname(__file__), "yam_real.xml"))
    ap.add_argument("--teleop", default="", help="teleop actions .npy (for similarity)")
    ap.add_argument("--thresholds", default="", help="JSON overrides merged into TH")
    ap.add_argument("--out", required=True)
    a = ap.parse_args()

    if a.thresholds:
        try:
            TH.update({k: float(v) for k, v in json.loads(a.thresholds).items() if k in TH})
        except Exception:
            pass

    if a.mode == "qa":
        rep = run_qa(a.retargeted, a.xml)
    elif a.mode == "correct":
        rep = run_correct(a.retargeted, a.xml)
    elif a.mode == "similarity":
        rep = run_similarity(a.retargeted, a.teleop)
    else:
        rep = {"mode": a.mode, "error": "unknown mode"}

    with open(a.out, "w") as f:
        json.dump(rep, f, indent=2)
    print(json.dumps({"mode": a.mode, "n_clips": rep.get("n_clips"), "counts": rep.get("counts"),
                      "out": a.out}))


if __name__ == "__main__":
    main()