#!/usr/bin/env python
"""50-step gate for the orbit-alpha LoRA (run on a GPU job).

Checks, in order:
  1. lora_B audit   : every lora_B tensor in the saved adapter must be non-zero
                      (min |mean| > 1e-8). Guards against the all-zero-lora_B bug.
  2. diffusers load : QwenImage21Pipeline.load_lora_weights must accept the
                      ai-toolkit (ComfyUI-keyed) file without conversion.
  3. visible change : the adapter must change the output on TRAINING pairs
                      (LPIPS between base and adapter output > 0.02) and must
                      not break the eval pairs (alpha IoU stays sane).

Outputs contact sheet + gate_results.json to the dataset repo eval/gate/<ckpt>/.
Prints a final 'GATE PASS' or 'GATE FAIL: <reason>' line.

Fix note: metrics_v() unpacks its third argument as a tuple
(lp, dino_model, dino_proc); pass METRICS (the tuple from make_metrics())
directly — building a dict here made lp bind to the string 'lp'.
"""
import argparse, json, os, sys
import numpy as np
import torch
from PIL import Image
from huggingface_hub import hf_hub_download, HfApi

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from eval_edit import (load_pipe, make_metrics, metrics_v, composite,
                       get_file, pair_seed, SIZE, STEPS)

CKPT_REPO = 'ysharma/orbit-alpha-lora'
DS_REPO = 'ysharma/gso-orbit-rgba'


def changed_lpips(lpf, img_a, img_b):
    ca, cb = composite(img_a), composite(img_b)
    ta = torch.from_numpy(ca).permute(2, 0, 1)[None].float() * 2 - 1
    tb = torch.from_numpy(cb).permute(2, 0, 1)[None].float() * 2 - 1
    with torch.no_grad():
        return float(lpf(ta, tb).item())


def gen_rows(pipe, METRICS, pairs, out_dir, tag):
    rows, imgs = [], {}
    for p in pairs:
        pid = p['pair_id']
        src = Image.open(get_file(p['source_file'])).convert('RGBA')
        gt = Image.open(get_file(p['target_file'])).convert('RGBA')
        gen = torch.Generator('cuda').manual_seed(pair_seed(pid))
        t0 = __import__('time').time()
        out = pipe(prompt=p['instruction'], image=src, width=SIZE, height=SIZE,
                   num_inference_steps=STEPS, generator=gen,
                   use_kv_cache=True).images[0]
        dt = __import__('time').time() - t0
        mv = metrics_v(out, gt, METRICS)
        imgs[pid] = out
        rows.append({'pair_id': pid, 'tag': tag, 's_edit': round(dt, 2), **mv})
        print(json.dumps(rows[-1]), flush=True)
        out.convert('RGBA').save(os.path.join(out_dir, f'{tag}_{pid}.png'))
    return rows, imgs


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument('--ckpt', default='steps50')
    ap.add_argument('--out', default='/out/gate')
    args = ap.parse_args()
    os.makedirs(args.out, exist_ok=True)

    # ---- 1. lora_B audit ------------------------------------------------
    fname = f'checkpoints/{args.ckpt}/orbit_alpha_lora/orbit_alpha_lora.safetensors'
    ckpt = hf_hub_download(CKPT_REPO, fname, repo_type='model')
    print(f'ckpt: {fname} ({os.path.getsize(ckpt)/1e6:.1f} MB)', flush=True)
    from safetensors.torch import load_file
    sd = load_file(ckpt)
    b_keys = [k for k in sd if 'lora_B' in k]
    a_keys = [k for k in sd if 'lora_A' in k]
    norms = [float(sd[k].abs().float().mean()) for k in b_keys]
    zero_b = sum(1 for n in norms if n < 1e-8)
    audit = {'n_lora_B': len(b_keys), 'n_lora_A': len(a_keys),
             'n_all_zero_B': zero_b,
             'min_mean_abs_B': min(norms) if norms else None,
             'max_mean_abs_B': max(norms) if norms else None}
    print('LORA_B AUDIT:', json.dumps(audit), flush=True)

    # ---- 2. diffusers load (base rows first, THEN load adapter) ---------
    pipe = load_pipe()
    METRICS = make_metrics()
    ev = [json.loads(l) for l in open(get_file('pairs_eval.jsonl'))][:4]
    tr = [json.loads(l) for l in open(get_file('pairs_train.jsonl'))][:2]

    base_rows, base_imgs = gen_rows(pipe, METRICS, tr + ev, args.out, 'base')
    pipe.load_lora_weights(ckpt)
    print('LOAD_LORA_WEIGHTS OK', flush=True)
    lora_rows, lora_imgs = gen_rows(pipe, METRICS, tr + ev, args.out, 'lora')

    # ---- 3. visible change ---------------------------------------------
    ch = []
    train_ids = {p['pair_id'] for p in tr}
    for r_b, r_l in zip(base_rows, lora_rows):
        c = changed_lpips(METRICS[0], base_imgs[r_b['pair_id']],
                          lora_imgs[r_b['pair_id']])
        ch.append({'pair_id': r_b['pair_id'], 'is_train': r_b['pair_id'] in
                   train_ids, 'changed_lpips': round(c, 4)})
        print(json.dumps(ch[-1]), flush=True)
    tr_ch = [c['changed_lpips'] for c in ch if c['is_train']]
    ev_ch = [c['changed_lpips'] for c in ch if not c['is_train']]

    # ---- contact sheet ---------------------------------------------------
    from PIL import ImageDraw
    pairs_all = tr + ev
    cell = 256
    sheet = Image.new('RGB', (cell * 4, cell * len(pairs_all)), (30, 30, 30))
    d = ImageDraw.Draw(sheet)
    for i, p in enumerate(pairs_all):
        pid = p['pair_id']
        tiles = [composite(Image.open(get_file(p['source_file'])).convert('RGBA')),
                 composite(Image.open(get_file(p['target_file'])).convert('RGBA')),
                 composite(base_imgs[pid]), composite(lora_imgs[pid])]
        for j, t in enumerate(tiles):
            sheet.paste(Image.fromarray(t).resize((cell, cell)), (j * cell, i * cell))
    sheet.save(os.path.join(args.out, 'contact_sheet.png'))

    results = {'ckpt': args.ckpt, 'audit': audit,
               'base_rows': base_rows, 'lora_rows': lora_rows, 'changed': ch,
               'mean_changed_train': float(np.mean(tr_ch)) if tr_ch else None,
               'mean_changed_eval': float(np.mean(ev_ch)) if ev_ch else None}
    with open(os.path.join(args.out, 'gate_results.json'), 'w') as f:
        json.dump(results, f, indent=1)

    api = HfApi()
    for f_ in ('contact_sheet.png', 'gate_results.json'):
        try:
            api.upload_file(path_or_fileobj=os.path.join(args.out, f_),
                            path_in_repo=f'eval/gate/{args.ckpt}/{f_}',
                            repo_id=DS_REPO, repo_type='dataset')
            print(f'[upload] {f_} -> eval/gate/{args.ckpt}/ OK', flush=True)
        except Exception as e:
            print(f'[upload] {f_} FAILED: {e}', flush=True)

    # ---- verdict ---------------------------------------------------------
    ok_audit = len(b_keys) > 0 and zero_b == 0
    ok_change = bool(tr_ch) and min(tr_ch) > 0.02
    ok_iou = np.mean([r['alpha_iou'] for r in lora_rows if r['pair_id'] in
                      train_ids]) > 0.5
    if ok_audit and ok_change and ok_iou:
        print(f'GATE PASS (audit ok, changed_train mean '
              f'{np.mean(tr_ch):.3f}, changed_eval mean '
              f'{np.mean(ev_ch):.3f})', flush=True)
    else:
        print(f'GATE FAIL: audit_ok={ok_audit} change_ok={ok_change} '
              f'iou_ok={ok_iou}', flush=True)


if __name__ == '__main__':
    main()