#!/usr/bin/env python
"""Auto-eval for C-qwen-morph (morphology via shared-Qwen text encode, LM unfrozen)
on held-out LeKiwi. Compares correct vs wrong vs no robot-description text, and
against prior baselines (C-diverse numeric-MLP 295mm; C-mega text-in-prompt 522mm).
"""

from __future__ import annotations

import numpy as np
import torch
import yaml
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
from transformers import AutoTokenizer

from tinyvla.data.canonical import CanonicalChunkStore, quantile_normalize
from tinyvla.data.eval_utils import StateAdapter
from tinyvla.modeling_tinyvla import TinyVLAPolicy

CKPT = "outputs/tv2_C_qwen_morph/final"
DS = "lekiwi_cleanup"
ROOT = f"/home/alexw/tinyvla_data/lekiwi/{DS}"


@torch.no_grad()
def main():
    pol = TinyVLAPolicy.from_pretrained(CKPT).cuda().eval()
    cfg = pol.config
    tok = AutoTokenizer.from_pretrained(cfg.lm_model_name)
    prompts = yaml.safe_load(open("configs/morphology/robot_prompts.yaml"))

    m = LeRobotDatasetMetadata(DS, root=ROOT)
    ds = LeRobotDataset(DS, root=ROOT, delta_timestamps={"action": [t / m.fps for t in range(50)]},
                        video_backend="torchcodec")
    _sa = StateAdapter(ds.meta, cfg.max_state_dim)
    store = CanonicalChunkStore(DS, src_fps=m.fps, chunk=50)
    st = store.compute_stats()
    q01, q99 = np.asarray(st["q01"]), np.asarray(st["q99"])
    span = np.maximum(q99 - q01, 0.01 * np.median(np.abs(np.concatenate([q01, q99])) + 1e-6))
    mid = 0.5 * (q01 + q99)

    imk = sorted(k for k in ds.meta.features if k.startswith("observation.images"))
    prim = next((k for k in imk if "front" in k or "base" in k), imk[0])

    def integ(d):
        return np.cumsum(d[:, :3], 0)

    n_eps = ds.num_episodes
    test = range(max(0, n_eps - 15), n_eps)

    def tok_desc(text):
        if text is None:
            ids = torch.zeros(1, cfg.morph_text_max_len, dtype=torch.long)
            mask = torch.zeros(1, cfg.morph_text_max_len, dtype=torch.bool)
            mask[0, 0] = True
            return ids, mask
        t = tok([text], padding="max_length", truncation=True,
                max_length=cfg.morph_text_max_len, return_tensors="pt")
        return t["input_ids"], t["attention_mask"].bool()

    def run(desc_text):
        ids, mask = tok_desc(desc_text)
        errs, zf = [], []
        for ep in test:
            s = int(m.episodes["dataset_from_index"][ep])
            e = int(m.episodes["dataset_to_index"][ep])
            for idx in range(s, e - 1, 30):
                item = ds[idx]
                task = item.get("task") or ""
                t = tok([task], padding=True, truncation=True, max_length=48, return_tensors="pt")
                img = torch.nn.functional.interpolate(item[prim][None], size=(256, 256), mode="bilinear")[0]
                stt = _sa(item["observation.state"])
                b = {"observation.images.cam0": img[None].cuda(),
                     "observation.images.cam1": torch.zeros_like(img)[None].cuda(),
                     "observation.state": stt[None].cuda(),
                     "observation.language.tokens": t["input_ids"].cuda(),
                     "observation.language.attention_mask": t["attention_mask"].bool().cuda(),
                     "morph_text_ids": ids.cuda(), "morph_text_mask": mask.cuda(),
                     "embodiment_id": torch.tensor([0]).cuda()}
                with torch.autocast("cuda", torch.bfloat16):
                    pr = pol.predict_action_chunk(b)[0].cpu().float().numpy()
                gu = quantile_normalize(store.chunk_for(ep, idx - s), q01, q99)[:, :7] * span / 2 + mid
                pu = pr[:, :7] * span / 2 + mid
                errs.append(np.linalg.norm(integ(pu)[-1] - integ(gu)[-1]) * 1000)
                zf.append(np.linalg.norm(integ(gu)[-1]) * 1000)
        return np.mean(errs), np.mean(zf)

    print(f"=== C-qwen-morph (LM unfrozen, morph via shared Qwen) on held-out LeKiwi ===")
    zero_floor = None
    conditions = [
        ("desc=lekiwi (correct)", prompts["lekiwi"]),
        ("desc=so101 (wrong, non-mobile arm)", prompts["so101"]),
        ("desc=none", None),
    ]
    for label, text in conditions:
        err, zf = run(text)
        if zero_floor is None:
            zero_floor = zf
        print(f"{label:38} endpoint {err:.1f}mm")
    print(f"\nzero-floor: {zero_floor:.1f}mm")
    print("prior baselines: C-diverse numeric-MLP 295.0mm | C-mega text-in-prompt(frozen LM) 298.5mm best")


if __name__ == "__main__":
    main()