from __future__ import annotations

import torch
import torch.nn as nn

from meshai_bridge.latent_adapter import TrellisHunyuanLatentAdapter

GEOM_IN_DIM = 4096 + 6
VOXEL_OUT_DIM = 32 * 32 * 32
TEXTURE_LATENT_DIM = 512


class GeometryVoxelHead(nn.Module):
    def __init__(self, in_dim: int = GEOM_IN_DIM, out_dim: int = VOXEL_OUT_DIM) -> None:
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(in_dim, 2048),
            nn.GELU(),
            nn.Linear(2048, 4096),
            nn.GELU(),
            nn.Linear(4096, out_dim),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


class TextureViewEncoder(nn.Module):
    """Multi-view CNN -> conditioning vector for Hunyuan path."""

    def __init__(self, out_dim: int = TEXTURE_LATENT_DIM, render_size: int = 128) -> None:
        super().__init__()
        self.render_size = render_size
        self.backbone = nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=2, padding=1),
            nn.GELU(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.GELU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.GELU(),
            nn.AdaptiveAvgPool2d(1),
        )
        flat = 128
        self.proj = nn.Linear(flat, out_dim)

    def forward(self, views: torch.Tensor) -> torch.Tensor:
        # views: [B, V, 3, H, W] — mean pool over views
        b, v, c, h, w = views.shape
        x = views.reshape(b * v, c, h, w)
        feats = self.backbone(x).reshape(b, v, -1).mean(dim=1)
        return self.proj(feats)


class MeshAIHybridTrainBundle(nn.Module):
    """Geometry voxel + texture views + TRELLIS→Hunyuan latent bridge."""

    def __init__(self) -> None:
        super().__init__()
        self.geometry = GeometryVoxelHead()
        self.texture = TextureViewEncoder()
        self.bridge = TrellisHunyuanLatentAdapter(
            trellis_dim=GEOM_IN_DIM,
            hunyuan_dim=TEXTURE_LATENT_DIM,
            hidden_dim=512,
            depth=3,
        )

    def forward(self, geom_in: torch.Tensor, views: torch.Tensor) -> dict[str, torch.Tensor]:
        voxel_pred = self.geometry(geom_in)
        tex_latent = self.texture(views)
        bridge_out = self.bridge(geom_in)
        return {
            "voxel_pred": voxel_pred,
            "tex_latent": tex_latent,
            "bridge_out": bridge_out,
        }