"""Export one test subject's data for Figure 4 case study."""
from __future__ import annotations

import argparse
from pathlib import Path

import nibabel as nib
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader

from pet_vlm_dataset import PETSUVRDataset, collate_pet_suvr
from train_pet_foundation import PETSUVRFoundationModel, build_encoder


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint", type=Path, required=True)
    parser.add_argument("--split", type=Path, default=Path("data/metadata/splits/test.csv"))
    parser.add_argument("--subject-index", type=int, default=0, help="which test subject to use (0=first)")
    parser.add_argument("--backbone", default=None)
    parser.add_argument("--medicalnet-weights", type=Path, default=Path("pretrained/medicalnet/resnet_50_23dataset.pth"))
    parser.add_argument("--batch-size", type=int, default=1)
    parser.add_argument("--num-workers", type=int, default=0)
    parser.add_argument("--output-size", type=int, nargs=3, default=None)
    parser.add_argument("--embed-dim", type=int, default=None)
    parser.add_argument("--freeze-encoder", type=bool, default=None)
    parser.add_argument("--out", type=Path, required=True)
    args = parser.parse_args()

    ckpt = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
    saved_args = ckpt.get("args", {})
    for name in ("backbone", "embed_dim", "freeze_encoder"):
        if getattr(args, name, None) is None and name in saved_args:
            setattr(args, name, saved_args[name])
    if args.output_size is None:
        args.output_size = tuple(saved_args.get("output_size", (96, 96, 96)))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    manifest = pd.read_csv(args.split)
    row = manifest.iloc[args.subject_index]

    dataset = PETSUVRDataset(args.split, output_size=tuple(args.output_size))
    sample = dataset[args.subject_index]
    n_regions = int(sample["suvr"].numel())

    encoder = build_encoder(args)
    model = PETSUVRFoundationModel(encoder, n_regions, args.embed_dim or 256, bool(args.freeze_encoder)).to(device)
    model.load_state_dict(ckpt["model"], strict=True)
    model.eval()

    image = sample["image"].unsqueeze(0).to(device)
    suvr = sample["suvr"].unsqueeze(0).to(device)

    with torch.no_grad():
        outputs = model(image, suvr)

    true_suvr_arr = suvr.cpu().numpy().flatten()
    pred_suvr_arr = outputs["pred_suvr"].cpu().numpy().flatten()

    # Load PET volume for slice extraction
    pet_path = str(row["pet_path"])
    pet_vol = nib.load(pet_path).get_fdata(dtype=np.float32)
    # Extract middle slices in each orientation
    d, h, w = pet_vol.shape
    axial_slice = pet_vol[d // 2, :, :]
    coronal_slice = pet_vol[:, h // 2, :]
    sagittal_slice = pet_vol[:, :, w // 2]

    # Load region labels from SUVR CSV
    suvr_csv_path = str(row["suvr_csv_path"])
    suvr_df = pd.read_csv(suvr_csv_path)
    region_labels = [str(lbl) for lbl in suvr_df["label_name"].tolist() if str(lbl) != "Background"]

    args.out.parent.mkdir(parents=True, exist_ok=True)
    np.savez(args.out,
             subject_id=str(row["sample_id"]),
             true_suvr=true_suvr_arr,
             pred_suvr=pred_suvr_arr,
             region_labels=np.array(region_labels, dtype=object),
             axial_slice=axial_slice,
             coronal_slice=coronal_slice,
             sagittal_slice=sagittal_slice)
    print(f"wrote case study for subject {row['sample_id']} to {args.out}")
    print(f"  true SUVR range: [{true_suvr_arr.min():.3f}, {true_suvr_arr.max():.3f}]")
    print(f"  pred SUVR range: [{pred_suvr_arr.min():.3f}, {pred_suvr_arr.max():.3f}]")
    print(f"  PET shape: {pet_vol.shape}, slices extracted")


if __name__ == "__main__":
    main()