"""Quick checkpoint load and forward-pass sanity check for two-stage models.

Example:
    python experiments/test_model.py --checkpoint-dir output/two_stage_encoding
"""

import argparse
from pathlib import Path

import torch

ROOT = Path(__file__).resolve().parent.parent
import sys

sys.path.append(str(ROOT))

from src.flowfm.checkpointing import load_state_dict, resolve_stage2_checkpoint
from src.flowfm.config import load_config, resolve_subjects
from src.flowfm.data_pipeline import make_data_loaders
from src.flowfm.model_factory import (
    build_stage1_model,
    build_stage2_models,
    infer_feature_dims,
    infer_target_dim,
)


def main() -> None:
    parser = argparse.ArgumentParser(description="Sanity-check two-stage checkpoint loading")
    parser.add_argument(
        "--checkpoint-dir",
        type=str,
        default=str(ROOT / "output" / "two_stage_encoding"),
        help="Directory containing config.yaml and stage checkpoints",
    )
    parser.add_argument("--cfg-path", type=str, default=None)
    parser.add_argument("--stage2-ckpt", type=str, default=None)
    parser.add_argument("--device", type=str, default=None)
    parser.add_argument("--n-timesteps", type=int, default=25)
    args = parser.parse_args()

    checkpoint_dir = Path(args.checkpoint_dir)
    cfg_path = args.cfg_path or str(checkpoint_dir / "config.yaml")

    cfg = load_config(cfg_path)
    if args.device is not None:
        cfg.device = args.device

    device = torch.device(cfg.device)
    subjects = resolve_subjects(cfg)

    data_loaders = make_data_loaders(cfg)
    sample_batch = next(iter(data_loaders["train"]))

    feat_dims = infer_feature_dims(sample_batch)
    target_dim = infer_target_dim(sample_batch)

    stage1_model = build_stage1_model(
        cfg=cfg,
        feat_dims=feat_dims,
        subjects=subjects,
        device=device,
    )
    stage1_path = checkpoint_dir / "stage1_best.pt"
    stage1_model.load_state_dict(load_state_dict(stage1_path, device))
    stage1_model.eval()

    stage2_models = build_stage2_models(
        cfg=cfg,
        target_dim=target_dim,
        subjects=subjects,
        device=device,
    )
    stage2_path = resolve_stage2_checkpoint(checkpoint_dir, args.stage2_ckpt)
    stage2_models.load_state_dict(load_state_dict(stage2_path, device))
    stage2_models.eval()

    print(f"Loaded Stage 1 checkpoint: {stage1_path}")
    print(f"Loaded Stage 2 checkpoint: {stage2_path}")

    feats = [f.to(device) for f in sample_batch["features"]]

    with torch.no_grad():
        mu_anchor = stage1_model(feats)
        preds = []
        for i, sub in enumerate(subjects):
            cfm = stage2_models[str(sub)]
            mu = mu_anchor[:, i].transpose(1, 2)
            pred = cfm(mu, n_timesteps=int(args.n_timesteps)).transpose(1, 2)
            preds.append(pred.unsqueeze(1))

        pred_all = torch.cat(preds, dim=1)

    print(f"Stage 1 output shape: {tuple(mu_anchor.shape)}")
    print(f"Stage 2 output shape: {tuple(pred_all.shape)}")


if __name__ == "__main__":
    main()