# %%
import os
import torch
from PIL import Image
from einops import rearrange, repeat
import numpy as np

import torch
import torch.nn.functional as F

align_weights = torch.load("align_weights.pth")
from torch import nn
from backbone import CLIPAttnNode, DiNOv2AttnNode, MAEAttnNode
class ThreeAttnNodes(nn.Module):
    def __init__(self, align_weights=align_weights):
        super().__init__()
        self.backbone1 = CLIPAttnNode()
        self.backbone2 = DiNOv2AttnNode()
        self.backbone3 = MAEAttnNode()
        for backbone in [self.backbone1, self.backbone2, self.backbone3]:
            backbone.requires_grad_(False)
            backbone.eval()
        self.align_weights = align_weights
            
    @torch.no_grad()
    def forward(self, x):
        # resize x to 672x672
        x = F.interpolate(x, size=(672, 672), mode="bilinear")
        feat1 = self.backbone1(x)
        feat3 = self.backbone3(x)
        # resize x to 588x588
        x = F.interpolate(x, size=(588, 588), mode="bilinear")
        feat2 = self.backbone2(x)
        feats = torch.cat([feat1, feat2, feat3], dim=1)
        out = torch.einsum("b l p i, l o i -> b l p o", feats, self.align_weights)
        out = rearrange(out[:, :, 1:], "b l (h w) o -> b l h w o", h=42, w=42)
        return out