"""Model registry: maps a config `model` key to a constructed nn.Module.

Every model's forward signature is (primary_input, tab=None) so the trainer can
call them uniformly. `modality` (from config) decides which input the loader feeds
as `primary_input`: slice tensor (B,T,3,H,W), volume (B,1,D,H,W), or tabular (B,F).
"""
from __future__ import annotations

from .cnn2d import ResNet50Center, DenseNet2p5D, DenseNetLateFusion
from .cnn3d import ResNet3D
from .transformers import ViT2D, SwinT3D
from .hybrids import HCCTCompact, VSwinFormerLite
from .trifuse import TriFuseAD
from .baselines_tab import TabularMLP


def build_model(cfg, n_tab_features: int = 7, pretrained: bool = True):
    m = cfg.model
    n = 3
    if m == "tabular_mlp":
        return TabularMLP(n_features=n_tab_features, n_classes=n, dropout=cfg.dropout)
    if m == "resnet50":
        return ResNet50Center(n_classes=n, pretrained=pretrained)
    if m == "densenet2p5d":
        return DenseNet2p5D(n_classes=n, pretrained=pretrained, n_slices=cfg.n_slices)
    if m == "resnet3d":
        return ResNet3D(n_classes=n)
    if m == "vit2d":
        return ViT2D(n_classes=n, pretrained=pretrained)
    if m == "swin3d":
        return SwinT3D(n_classes=n, img_size=cfg.vol_size)
    if m == "hcct":
        return HCCTCompact(n_classes=n)
    if m == "vswin_lite":
        return VSwinFormerLite(n_classes=n, img_size=cfg.vol_size)
    if m == "densenet_latefusion":
        return DenseNetLateFusion(n_classes=n, n_tab_features=n_tab_features,
                                  pretrained=pretrained, n_slices=cfg.n_slices, dropout=cfg.dropout)
    if m.startswith("trifuse"):
        # ablation flags encoded in the model key: trifuse, trifuse_axial, trifuse_meanpool,
        # trifuse_nometa, trifuse_concat
        return TriFuseAD(
            n_classes=n, n_tab_features=n_tab_features,
            planes=tuple(cfg.planes), n_slices=cfg.n_slices, dropout=cfg.dropout,
            use_transformer=("meanpool" not in m),
            use_metadata=("nometa" not in m),
            fusion=("concat" if "concat" in m else "gated"),
            pretrained=pretrained,
        )
    raise ValueError(f"unknown model key: {m}")