"""segmentation_models.pytorch (SMP) backbones.

SMP gives us a large architecture x encoder zoo behind one constructor and is a
plain nn.Module (DDP- and bf16-friendly). Grayscale (in_channels=1) with ImageNet
encoder weights is handled by SMP's built-in channel adaptation.
"""
from __future__ import annotations

import segmentation_models_pytorch as smp

# our arch name -> SMP architecture key
_SMP_ARCH = {
    "unet": "Unet",
    "unetpp": "UnetPlusPlus",
    "unetplusplus": "UnetPlusPlus",
    "manet": "MAnet",
    "linknet": "Linknet",
    "fpn": "FPN",
    "pspnet": "PSPNet",
    "deeplabv3": "DeepLabV3",
    "deeplabv3plus": "DeepLabV3Plus",
    "pan": "PAN",
}


def is_smp_arch(arch: str) -> bool:
    return arch.lower() in _SMP_ARCH


def build_smp(arch: str, in_channels: int, num_classes: int,
              encoder: str = "resnet34", encoder_weights: str = "imagenet", **_):
    weights = None if encoder_weights in ("", "none", None) else encoder_weights
    return smp.create_model(
        arch=_SMP_ARCH[arch.lower()],
        encoder_name=encoder,
        encoder_weights=weights,
        in_channels=in_channels,
        classes=num_classes,
    )