"""主干层 block：Dense（GateSelfAttn + SwiGLU FFN）/ MoE（GateSelfAttn + MoE FFN）。"""

from __future__ import annotations

from typing import Optional

import torch
import torch.nn as nn

from ..modules.ffn import SwiGLUFFN
from ..modules.gate_attention import GateSelfAttention
from ..modules.moe import MoEBlock, MoEStats


class DenseBlock(nn.Module):
    """PreNorm GateSelfAttention + PreNorm SwiGLU FFN。"""

    def __init__(self, dim: int, num_heads: int, ffn_mult: int = 4, dropout: float = 0.0) -> None:
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = GateSelfAttention(dim, num_heads=num_heads, dropout=dropout)
        self.norm2 = nn.LayerNorm(dim)
        self.ffn = SwiGLUFFN(dim, mult=ffn_mult, dropout=dropout)

    def forward(
        self,
        x: torch.Tensor,
        rope_cos: Optional[torch.Tensor] = None,
        rope_sin: Optional[torch.Tensor] = None,
        visual_slice: Optional[tuple[int, int]] = None,
    ) -> torch.Tensor:
        x = x + self.attn(self.norm1(x), rope_cos=rope_cos, rope_sin=rope_sin, visual_slice=visual_slice)
        x = x + self.ffn(self.norm2(x))
        return x


class MoEBlockWithAttn(nn.Module):
    """PreNorm GateSelfAttention + PreNorm MoE FFN。"""

    def __init__(
        self,
        dim: int,
        num_heads: int,
        num_routed: int = 7,
        num_shared: int = 1,
        topk: int = 3,
        ffn_mult: int = 4,
        dropout: float = 0.0,
    ) -> None:
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = GateSelfAttention(dim, num_heads=num_heads, dropout=dropout)
        self.norm2 = nn.LayerNorm(dim)
        self.moe = MoEBlock(
            dim,
            num_routed=num_routed,
            num_shared=num_shared,
            topk=topk,
            ffn_mult=ffn_mult,
            dropout=dropout,
        )

    def set_mode(self, mode: str) -> None:
        self.moe.set_mode(mode)

    def set_temperature(self, t: float) -> None:
        self.moe.set_temperature(t)

    def forward(
        self,
        x: torch.Tensor,
        rope_cos: Optional[torch.Tensor] = None,
        rope_sin: Optional[torch.Tensor] = None,
        visual_slice: Optional[tuple[int, int]] = None,
    ) -> tuple[torch.Tensor, MoEStats]:
        x = x + self.attn(self.norm1(x), rope_cos=rope_cos, rope_sin=rope_sin, visual_slice=visual_slice)
        moe_out, stats = self.moe(self.norm2(x))
        x = x + moe_out
        return x, stats