"""Sweep candidate ckpts (across the full lbkb1h5z + dbdxldk4 chain) and
compute test-set MSE loss for each. Outputs JSON with {ckpt: test_loss}.

Test loss = the same MSE that training_step computes (matched per-objective:
v-pred or rectified-flow target). Computed under no_grad with deterministic
per-batch noise + timesteps so different ckpts are compared apples-to-apples.

Usage:
    python eval_sa_test_loss.py \\
        --ckpts <hf_path1> <hf_path2> ...  \\
        --out best_ckpts_sa.json \\
        [--limit 500]   # cap test pairs for fast iteration

Steps to evaluate are selected by passing --ckpts; the sbat submits a
representative grid across both runs.
"""
import argparse, json, os, sys, time
from pathlib import Path

import numpy as np
import torch
import torch.nn.functional as F
from huggingface_hub import hf_hub_download

SA_ROOT = Path("/nfs/turbo/coe-ahowens-nobackup/dingqy/friendly-stable-audio-tools")
sys.path.insert(0, str(SA_ROOT))

from stable_audio_tools.models import create_model_from_config              # noqa
from stable_audio_tools.models.utils import load_ckpt_state_dict            # noqa
from stable_audio_tools.training import create_training_wrapper_from_config  # noqa
from stable_audio_tools.inference.sampling import get_alphas_sigmas         # noqa
from stable_audio_tools.data.dataset import HidingSoundManifestDataset      # noqa
from torch.utils.data import DataLoader

HF_REPO = "AE-W/ckpt"
CACHE   = os.environ.get("HF_CACHE", "/nfs/turbo/coe-ahowens-nobackup/dingqy/.cache/huggingface")


def collate_metadata(batch):
    """Default DataLoader collate would dict-merge metadata; instead keep it as
    a list of dicts because the SA conditioner expects metadata: list[dict]."""
    audios = torch.stack([item[0] for item in batch], dim=0)
    metas  = [item[1] for item in batch]
    return audios, metas


@torch.no_grad()
def test_loss_for_ckpt(wrapper, test_loader, device, num_batches=None,
                       seed_base=42):
    """Mean per-sample MSE between model output and the v / rfm target on the
    test loader. Mirrors `DiffusionCondTrainingWrapper.training_step` minus
    log/backprop; deterministic per-batch noise + timesteps so multiple
    ckpts are compared on the same noising pattern."""
    wrapper.eval()
    obj = wrapper.diffusion_objective

    total = 0.0
    n     = 0
    t0    = time.time()

    for i, batch in enumerate(test_loader):
        if num_batches and i >= num_batches:
            break
        reals, metadata = batch
        reals = reals.to(device, non_blocking=True)
        if reals.ndim == 4 and reals.shape[0] == 1:
            reals = reals[0]

        # Deterministic noise + timestep — same across ckpts. seed = base + i
        # so every batch has its own noise, but consistent run-to-run.
        gen = torch.Generator(device=device).manual_seed(seed_base + i)

        with torch.cuda.amp.autocast():
            wrapper.diffusion.conditioner.set_device(device)
            conditioning = wrapper.diffusion.conditioner(metadata)

            if wrapper.diffusion.pretransform:
                with torch.cuda.amp.autocast():
                    diffusion_input = wrapper.diffusion.pretransform.encode(reals)
            else:
                diffusion_input = reals

            t = torch.rand(reals.shape[0], generator=gen, device=device)
            if obj == "v":
                alphas, sigmas = get_alphas_sigmas(t)
            elif obj == "rectified_flow":
                alphas, sigmas = 1 - t, t
            else:
                raise ValueError(f"unknown diffusion_objective: {obj}")

            alphas = alphas[:, None, None]
            sigmas = sigmas[:, None, None]
            noise = torch.randn(diffusion_input.shape, generator=gen, device=device)
            noised = diffusion_input * alphas + noise * sigmas

            if obj == "v":
                targets = noise * alphas - diffusion_input * sigmas
            elif obj == "rectified_flow":
                targets = noise - diffusion_input

            output = wrapper.diffusion(noised, t, cond=conditioning, cfg_dropout_prob=0.0)
            loss = F.mse_loss(output.float(), targets.float(), reduction='mean')

        bs = reals.shape[0]
        total += float(loss.item()) * bs
        n     += bs
        if i % 50 == 0:
            print(f"    batch {i:>4} cum_n={n}  rolling_loss={total/n:.5f}  ({time.time()-t0:.0f}s)", flush=True)

    return total / max(n, 1), n


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--ckpts", nargs="+", required=True,
                    help="HF paths under AE-W/ckpt, e.g. sa_open_bg2fg_rebalance/lbkb1h5z/epoch=0-step=10000.ckpt")
    ap.add_argument("--out", default="/nfs/turbo/coe-ahowens-nobackup/dingqy/sa_test_loss_sweep.json")
    ap.add_argument("--model-config",
                    default=str(SA_ROOT / "stable_audio_tools/configs/model_configs/txt2audio/stable_audio_open_1_0_bg2fg_rebalance.json"))
    ap.add_argument("--dataset-config",
                    default=str(SA_ROOT / "stable_audio_tools/configs/dataset_configs/hidingsound_sa_open_bg2fg_rebalance.json"))
    ap.add_argument("--batch-size", type=int, default=8)
    ap.add_argument("--num-workers", type=int, default=4)
    ap.add_argument("--limit", type=int, default=0,
                    help="cap test pairs (0 = all). Use ~500 for a quick first pass.")
    ap.add_argument("--seed-base", type=int, default=42)
    args = ap.parse_args()

    print(f"loading model config: {args.model_config}")
    mc = json.load(open(args.model_config))
    print(f"loading dataset config: {args.dataset_config}")
    ds_cfg = json.load(open(args.dataset_config))

    print("instantiating model + training wrapper (one time)...", flush=True)
    base_model = create_model_from_config(mc)
    wrapper = create_training_wrapper_from_config(mc, base_model)
    wrapper = wrapper.cuda()

    # Build TEST dataset
    print(f"building test dataset from manifest: {ds_cfg['manifest_path']}")
    ds = HidingSoundManifestDataset(
        manifest_path=ds_cfg["manifest_path"],
        data_root=ds_cfg.get("data_root"),
        split="test",
        sample_size=mc["sample_size"],
        sample_rate=mc["sample_rate"],
        random_crop=False,             # deterministic
        rebalance_enabled=False,
        prompt_stats_path=None,
        smoothing=0.0,
    )
    if args.limit:
        ds.pairs = ds.pairs[: args.limit]
    print(f"  {len(ds)} test pairs")

    test_loader = DataLoader(
        ds, batch_size=args.batch_size, shuffle=False,
        num_workers=args.num_workers, collate_fn=collate_metadata,
        pin_memory=True,
    )

    results = {}
    if Path(args.out).exists():
        results = json.load(open(args.out))

    for ckpt_rel in args.ckpts:
        if ckpt_rel in results:
            print(f"\n[skip] {ckpt_rel}  (cached test_loss={results[ckpt_rel]['test_loss']:.5f})", flush=True)
            continue
        print(f"\n=== {ckpt_rel} ===", flush=True)
        if os.path.isabs(ckpt_rel) and os.path.exists(ckpt_rel):
            # Local file (e.g. the pretrained sa_open_1_0_bg_expanded.ckpt
            # baseline) — skip the HF download path.
            local = ckpt_rel
        else:
            local = hf_hub_download(repo_id=HF_REPO, filename=ckpt_rel,
                                    repo_type="dataset", cache_dir=CACHE)
        sd = load_ckpt_state_dict(local)
        # Two ckpt formats coexist:
        # - Lightning ckpt (saved during training): keys = diffusion.* / diffusion_ema.*
        # - Stability raw inner-model save (pretrained baseline): keys = model.model.*
        # Lightning's load_state_dict handles the first; the second needs to
        # go via copy_state_dict on wrapper.diffusion (which itself is a
        # ConditionedDiffusionModelWrapper with native model.model.* keys).
        is_raw_inner_save = any(k.startswith("model.model.") for k in sd.keys()) \
                            and not any(k.startswith("diffusion.") for k in sd.keys())
        if is_raw_inner_save:
            from stable_audio_tools.utils.torch_common import copy_state_dict
            copy_state_dict(wrapper.diffusion, sd)
            print(f"  copy_state_dict into wrapper.diffusion (raw inner-model save)")
            ema_loaded = False
        else:
            missing, unexpected = wrapper.load_state_dict(sd, strict=False)
            print(f"  load_state_dict: missing={len(missing)} unexpected={len(unexpected)}")
            ema_loaded = any(k.startswith("diffusion_ema") for k in sd.keys())
        if ema_loaded and getattr(wrapper, "diffusion_ema", None) is not None:
            wrapper.diffusion.model = wrapper.diffusion_ema.ema_model
            print("  using EMA weights")
        else:
            print(f"  using non-EMA (raw) weights  (ema_loaded={ema_loaded})")
        wrapper = wrapper.cuda().eval()

        loss, n = test_loss_for_ckpt(wrapper, test_loader, device="cuda",
                                     seed_base=args.seed_base)
        print(f"  test_loss = {loss:.5f}  (n={n})", flush=True)
        results[ckpt_rel] = {"test_loss": loss, "n": n}
        with open(args.out, "w") as f:
            json.dump(results, f, indent=2)
        print(f"  saved → {args.out}", flush=True)

    # Final summary
    print("\n=== summary (sorted by test_loss) ===")
    sorted_results = sorted(results.items(), key=lambda x: x[1]["test_loss"])
    for ckpt_rel, info in sorted_results:
        print(f"  {info['test_loss']:.5f}  (n={info['n']})  {ckpt_rel}")

    if sorted_results:
        best = sorted_results[0]
        print(f"\n>>> best ckpt: {best[0]}  test_loss={best[1]['test_loss']:.5f}")


if __name__ == "__main__":
    main()