#!/usr/bin/env python
"""Checkpoint selection for the orbit-alpha LoRA.

Fixed 48-edit subset (first 12 eval objects x 4 pairs) at 20 steps, for the
untouched base model AND every saved checkpoint, all through ONE pipeline load
(adapter unload/load between checkpoints). Choice rule per spec: best LPIPS
that does not lose alpha IoU vs the base model.

Usage: python select_ckpt.py --out /out/ckpt_select
Uploads summary + per-edit results to ysharma/gso-orbit-rgba/eval/ckpt_select/.
"""
import argparse, json, os, collections
import numpy as np
import eval_edit as ee

# 20-step selection pass, per spec (ee.run_pairs reads module-level STEPS)
ee.STEPS = 20

MODEL_REPO = 'ysharma/orbit-alpha-lora'
CKPT_DIR = 'checkpoints/steps2000res768/orbit_alpha_lora'
CKPTS = [
    ('ckpt500', f'{CKPT_DIR}/orbit_alpha_lora_000000500.safetensors'),
    ('ckpt1000', f'{CKPT_DIR}/orbit_alpha_lora_000001000.safetensors'),
    ('ckpt1500', f'{CKPT_DIR}/orbit_alpha_lora_000001500.safetensors'),
    ('ckpt2000', f'{CKPT_DIR}/orbit_alpha_lora.safetensors'),
]
DS_REPO = ee.DS_REPO
OUT_REPO_PATH = 'eval/ckpt_select'


def pick_subset(pairs, n_obj=12):
    by_obj = collections.OrderedDict()
    for p in pairs:
        by_obj.setdefault(p['object_id'], []).append(p)
    objs = list(by_obj)[:n_obj]
    sel = [p for o in objs for p in by_obj[o]]
    assert len(objs) == n_obj and all(len(by_obj[o]) == 4 for o in objs), \
        f'expected {n_obj} objects x 4 pairs, got {len(objs)} objs'
    return sel


def swap_adapter(pipe, path):
    try:
        pipe.unload_lora_weights()
    except Exception as e:
        print(f'[swap] unload_lora_weights failed ({e!r}); continuing with load', flush=True)
    pipe.load_lora_weights(path)
    print(f'[swap] loaded {path}', flush=True)


def agg_rows(rows, label):
    a = float(np.mean([r['alpha_iou'] for r in rows]))
    l = float(np.mean([r['lpips'] for r in rows]))
    p = float(np.mean([r['psnr'] for r in rows]))
    d = float(np.mean([r['dino_sim'] for r in rows]))
    s = float(np.mean([r['s_edit'] for r in rows]))
    print(f'{label}: n={len(rows)} aIoU={a:.3f} LPIPS={l:.4f} PSNR={p:.2f} DINO={d:.3f} s/edit={s:.1f}', flush=True)
    return {'name': label, 'n': len(rows), 'alpha_iou': round(a, 4),
            'lpips': round(l, 4), 'psnr': round(p, 2),
            'dino_sim': round(d, 4), 's_edit': round(s, 2)}


def upload(local, remote):
    from huggingface_hub import HfApi
    HfApi().upload_file(path_or_fileobj=local, path_in_repo=remote,
                        repo_id=DS_REPO, repo_type='dataset',
                        commit_message=f'ckpt-select: {remote}')


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument('--out', required=True)
    args = ap.parse_args()
    os.makedirs(args.out, exist_ok=True)

    pairs = pick_subset([json.loads(l) for l in open(ee.get_file('pairs_eval.jsonl'))])
    print(f'subset: {len(pairs)} edits, objects={sorted({p["object_id"] for p in pairs})[:3]}...', flush=True)

    pipe = ee.load_pipe()
    m = ee.make_metrics()

    summary = []
    # base model at 20 steps first (reference point)
    rows = ee.run_pairs(pipe, m, pairs, 'instruction', os.path.join(args.out, 'base20'))
    summary.append(agg_rows(rows, 'base20'))
    base_aiou = summary[0]['alpha_iou']

    for name, path in CKPTS:
        local = ee.hf_hub_download(repo_id=MODEL_REPO, repo_type='model', filename=path)
        swap_adapter(pipe, local)
        rows = ee.run_pairs(pipe, m, pairs, 'instruction', os.path.join(args.out, name))
        r = agg_rows(rows, name)
        r['kept_aiou'] = bool(r['alpha_iou'] >= base_aiou)
        summary.append(r)

    # choice rule: best LPIPS among checkpoints that do not lose alpha IoU
    ok = [s for s in summary[1:] if s['kept_aiou']]
    chosen = min(ok, key=lambda s: s['lpips']) if ok else None
    if chosen is None:
        chosen = min(summary[1:], key=lambda s: s['lpips'])
        print('WARNING: no checkpoint kept base alpha IoU; fell back to best LPIPS overall', flush=True)
    summary.append({'chosen': chosen['name']})

    with open(os.path.join(args.out, 'summary.json'), 'w') as f:
        json.dump(summary, f, indent=2)
    print('SUMMARY ' + json.dumps(summary), flush=True)
    upload(os.path.join(args.out, 'summary.json'), f'{OUT_REPO_PATH}/summary.json')
    for name, _ in [('base20', None)] + CKPTS:
        upload(os.path.join(args.out, name, 'results.jsonl'), f'{OUT_REPO_PATH}/{name}_results.jsonl')
    print('DONE', flush=True)


if __name__ == '__main__':
    main()