"""Building blocks: MLP and the pre-norm Transolver block.

Faithful to Transolver ``model/Transolver_Irregular_Mesh.py`` (MIT). The only structural
change is that the attention module is injected (so Stage 2 can swap Physics-Attention for
LinearNO without touching anything else).
"""
import torch.nn as nn

ACTIVATION = {
    "gelu": nn.GELU,
    "tanh": nn.Tanh,
    "sigmoid": nn.Sigmoid,
    "relu": nn.ReLU,
    "leaky_relu": lambda: nn.LeakyReLU(0.1),
    "softplus": nn.Softplus,
    "ELU": nn.ELU,
    "silu": nn.SiLU,
}


class MLP(nn.Module):
    """Transolver MLP: Linear->act (->[Linear->act]^n_layers) ->Linear, optional residual."""

    def __init__(self, n_input, n_hidden, n_output, n_layers=1, act="gelu", res=True):
        super().__init__()
        if act not in ACTIVATION:
            raise NotImplementedError(act)
        act_cls = ACTIVATION[act]
        self.n_layers = n_layers
        self.res = res
        self.linear_pre = nn.Sequential(nn.Linear(n_input, n_hidden), act_cls())
        self.linear_post = nn.Linear(n_hidden, n_output)
        self.linears = nn.ModuleList(
            [nn.Sequential(nn.Linear(n_hidden, n_hidden), act_cls()) for _ in range(n_layers)]
        )

    def forward(self, x):
        x = self.linear_pre(x)
        for layer in self.linears:
            x = layer(x) + x if self.res else layer(x)
        return self.linear_post(x)


class TransolverBlock(nn.Module):
    """Pre-norm transformer block; the last block also carries the decoder head.

        fx = fx + Attn(LayerNorm(fx))
        fx = fx + MLP(LayerNorm(fx))
        (last block) return Linear(LayerNorm(fx))  -> out_dim
    """

    def __init__(
        self,
        attention: nn.Module,
        hidden_dim: int,
        dropout: float = 0.0,
        act: str = "gelu",
        mlp_ratio: int = 1,
        last_layer: bool = False,
        out_dim: int = 1,
    ):
        super().__init__()
        self.last_layer = last_layer
        self.ln_1 = nn.LayerNorm(hidden_dim)
        self.Attn = attention
        self.ln_2 = nn.LayerNorm(hidden_dim)
        self.mlp = MLP(hidden_dim, hidden_dim * mlp_ratio, hidden_dim, n_layers=0, res=False, act=act)
        if last_layer:
            self.ln_3 = nn.LayerNorm(hidden_dim)
            self.mlp2 = nn.Linear(hidden_dim, out_dim)

    def forward(self, fx):
        fx = self.Attn(self.ln_1(fx)) + fx
        fx = self.mlp(self.ln_2(fx)) + fx
        if self.last_layer:
            return self.mlp2(self.ln_3(fx))
        return fx