"""Standalone inference script for visualization (does not modify original code)."""
import argparse
import os
import sys
from types import SimpleNamespace
from typing import Any

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

os.environ.setdefault("MONAI_SKIP_SUBMODULES", "1")
from monai.inferers import sliding_window_inference

ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
SRC_DIR = os.path.join(ROOT_DIR, "src")
if SRC_DIR not in sys.path:
    sys.path.insert(0, SRC_DIR)

from gliomasam3_moe.models.gliomasam3_moe import GliomaSAM3_MoE
from gliomasam3_moe.data.brats_dataset import SegMambaNPZDataset
from gliomasam3_moe.utils.brats_regions import regions_to_label
from gliomasam3_moe.utils.postprocess import remove_small_components


def _to_namespace(obj: Any):
    if isinstance(obj, dict):
        return SimpleNamespace(**{k: _to_namespace(v) for k, v in obj.items()})
    return obj


def load_config(path: str) -> SimpleNamespace:
    with open(path, "r") as f:
        cfg = yaml.safe_load(f)
    return _to_namespace(cfg)


def save_nifti(path: str, arr: np.ndarray, affine: np.ndarray):
    img = nib.Nifti1Image(arr, affine)
    nib.save(img, path)


def save_segmamba_3c(path: str, arr_3c: np.ndarray, affine: np.ndarray | None = None):
    if affine is None:
        affine = np.eye(4)
    if arr_3c.ndim != 4 or arr_3c.shape[0] != 3:
        raise ValueError(f"expected (3,D,H,W), got {arr_3c.shape}")
    arr = arr_3c.transpose(1, 2, 3, 0)
    save_nifti(path, arr.astype(np.uint8), affine)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", type=str, default=os.path.join(ROOT_DIR, "configs/train.yaml"))
    parser.add_argument("--input", type=str, required=True)
    parser.add_argument("--output", type=str, required=True)
    parser.add_argument("--checkpoint", type=str, required=True)
    parser.add_argument("--cases", type=str, default="", help="Comma-separated case IDs (optional)")
    args = parser.parse_args()

    cfg = load_config(args.config)
    device = torch.device(cfg.device if torch.cuda.is_available() else "cpu")

    model = GliomaSAM3_MoE(**cfg.model.__dict__).to(device)
    ckpt = torch.load(args.checkpoint, map_location="cpu")
    # Filter out freqs_cis which is dynamically computed and may have shape mismatch
    state_dict = {k: v for k, v in ckpt["model"].items() if "freqs_cis" not in k}
    missing, unexpected = model.load_state_dict(state_dict, strict=False)
    if missing:
        non_freqs = [k for k in missing if "freqs_cis" not in k]
        if non_freqs:
            print(f"Missing keys (non-freqs_cis): {non_freqs}")
    model.eval()

    input_path = args.input
    if not os.path.isdir(input_path):
        raise ValueError("Input must be a directory containing *.npz files.")

    if args.cases:
        case_ids = [c.strip() for c in args.cases.split(",")]
        npz_paths = [os.path.join(input_path, f"{c}.npz") for c in case_ids]
        npz_paths = [p for p in npz_paths if os.path.isfile(p)]
    else:
        npz_paths = None

    dataset = SegMambaNPZDataset(
        data_dir=input_path,
        npz_paths=npz_paths,
        test=True,
        ensure_npy=True,
        map_et_to_4=True,
    )
    loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)

    os.makedirs(args.output, exist_ok=True)
    with torch.no_grad():
        for batch in loader:
            image = batch["image"].to(device)
            case_id = batch["case_id"][0] if isinstance(batch["case_id"], (list, tuple)) else batch["case_id"]
            print(f"Processing {case_id}...")

            logits = sliding_window_inference(
                inputs=image,
                roi_size=tuple(cfg.infer.roi_size),
                sw_batch_size=cfg.infer.sw_batch_size,
                predictor=lambda x: model(x)[0],
                overlap=cfg.infer.overlap,
            )
            _, aux = model(image)
            probs = torch.sigmoid(logits)
            pi_et = aux["pi_et"].view(probs.shape[0], 1, 1, 1, 1)
            probs[:, 2:3] = probs[:, 2:3] * pi_et
            regions_bin = (probs > cfg.infer.threshold).float()

            et_pp = remove_small_components(regions_bin[:, 2], cfg.infer.et_cc_min_size)
            regions_bin[:, 2] = et_pp

            label_map = regions_to_label(regions_bin)

            affine = np.eye(4)
            prob_np = probs[0].detach().cpu().numpy().transpose(1, 2, 3, 0)
            bin_np = regions_bin[0].detach().cpu().numpy().transpose(1, 2, 3, 0)
            lbl_np = label_map[0, 0].detach().cpu().numpy().astype(np.int16)

            save_nifti(os.path.join(args.output, f"{case_id}_regions_prob.nii.gz"), prob_np, affine)
            save_nifti(os.path.join(args.output, f"{case_id}_regions_bin.nii.gz"), bin_np, affine)
            save_nifti(os.path.join(args.output, f"{case_id}_label.nii.gz"), lbl_np, affine)

            seg_arr = regions_bin[0].detach().cpu().numpy().astype(np.uint8)
            save_segmamba_3c(os.path.join(args.output, f"{case_id}.nii.gz"), seg_arr, affine)

            print(f"  Saved: {case_id}")


if __name__ == "__main__":
    main()