"""Model definitions for the CD-MSC best ensemble (frozen-embedding probes).
Two architectures, both consuming a frozen foundation-model / physics embedding and emitting 9 species logits:
  - RP      : rich Pre-LN residual probe, used for the Perch (1536-d) and BirdMAE (1024-d) members.
  - HarmNet : small MLP, used for the 102-d physics harmonic feature member.
Both also carry an auxiliary 5-way domain head (`dm`) used only during training (domain-aux / for MMD/CORAL on
the embedding); it is unused at inference. The architecture must match the training script exactly so saved
checkpoints load cleanly.
"""
import torch, torch.nn as nn, torch.nn.functional as F


class ResidualBlock(nn.Module):
    """Pre-LayerNorm residual MLP block: x + Dropout(W2 GELU(W1 LN(x)))."""
    def __init__(self, d, p=0.2):
        super().__init__()
        self.ln = nn.LayerNorm(d, eps=1e-6)
        self.f1 = nn.Linear(d, d * 4)
        self.f2 = nn.Linear(d * 4, d)
        self.d1 = nn.Dropout(p)
        self.d2 = nn.Dropout(p)

    def forward(self, x):
        return x + self.d2(self.f2(self.d1(F.gelu(self.f1(self.ln(x))))))


class RP(nn.Module):
    """Rich probe for high-dim FM embeddings (Perch / BirdMAE).
    input LayerNorm -> Linear(d->256) -> 2x ResidualBlock -> LayerNorm -> {species head, domain head}.
    Training-time augmentation (active only in .train()): additive Gaussian embedding noise (sigma=0.05) and
    feature channel-dropout (p=0.1). `e` is the L2-normalizable embedding used by SupCon/MMD/CORAL.
    """
    def __init__(self, d, pd=256, p=0.2, noise=0.05, chdrop=0.1):
        super().__init__()
        self.iln = nn.LayerNorm(d, eps=1e-6)
        self.pr = nn.Linear(d, pd)
        self.b = nn.ModuleList([ResidualBlock(pd, p), ResidualBlock(pd, p)])
        self.po = nn.LayerNorm(pd, eps=1e-6)
        self.sp = nn.Linear(pd, 9)   # species logits
        self.dm = nn.Linear(pd, 5)   # auxiliary domain logits (training only)
        self.noise = noise
        self.chdrop = chdrop

    def forward(self, x):
        if self.training:
            x = x + torch.randn_like(x) * self.noise
            x = x * (torch.rand(x.shape[1], device=x.device) > self.chdrop).float()[None, :]
        h = self.pr(self.iln(x))
        for bl in self.b:
            h = bl(h)
        e = self.po(h)
        return self.sp(e), self.dm(e), e


class HarmNet(nn.Module):
    """Dedicated MLP for the 102-d physics harmonic feature (device-invariant comb / f0).
    LN -> Linear(d->128) -> GELU -> Dropout(0.2) -> Linear(128->64) -> LN -> {species head, domain head}.
    Training-time additive noise sigma=0.03.
    """
    def __init__(self, d):
        super().__init__()
        self.net = nn.Sequential(
            nn.LayerNorm(d, eps=1e-6), nn.Linear(d, 128), nn.GELU(),
            nn.Dropout(0.2), nn.Linear(128, 64), nn.LayerNorm(64, eps=1e-6),
        )
        self.sp = nn.Linear(64, 9)
        self.dm = nn.Linear(64, 5)

    def forward(self, x):
        if self.training:
            x = x + torch.randn_like(x) * 0.03
        e = self.net(x)
        return self.sp(e), self.dm(e), e


def build(arch, d):
    """Factory: arch in {'rp','harm'} -> model for a d-dim input embedding."""
    return HarmNet(d) if arch == "harm" else RP(d)