import torch
import torch.nn as nn
from ldm.modules.attention import BasicTransformerBlock
from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
import torch.nn.functional as F
from ..attention import SelfAttention, FeedForward
from .convnext import convnext_tiny




class PositionNet(nn.Module):
    def __init__(self, resize_input=448, out_dim=768):
        super().__init__()
        self.resize_input = resize_input
        self.down_factor = 32 # determined by the convnext backbone 
        self.out_dim = out_dim
        assert self.resize_input % self.down_factor == 0
        
        self.convnext_tiny_backbone = convnext_tiny(pretrained=True)
        
        self.num_tokens = (self.resize_input // self.down_factor) ** 2
        
        convnext_feature_dim = 768
        self.pos_embedding = nn.Parameter(torch.empty(1, self.num_tokens, convnext_feature_dim).normal_(std=0.02))  # from BERT
      
        self.linears = nn.Sequential(
            nn.Linear( convnext_feature_dim, 512),
            nn.SiLU(),
            nn.Linear( 512, 512),
            nn.SiLU(),
            nn.Linear(512, out_dim),
        )

        self.null_feature = torch.nn.Parameter(torch.zeros([convnext_feature_dim]))


    def forward(self, canny_edge, mask):
        B = canny_edge.shape[0] 

        # token from edge map 
        canny_edge = torch.nn.functional.interpolate(canny_edge, self.resize_input)
        canny_edge_feature = self.convnext_tiny_backbone(canny_edge)
        objs = canny_edge_feature.reshape(B, -1, self.num_tokens)
        objs = objs.permute(0, 2, 1) # N*Num_tokens*dim

        # expand null token
        null_objs = self.null_feature.view(1,1,-1)
        null_objs = null_objs.repeat(B,self.num_tokens,1)
        
        # mask replacing 
        mask = mask.view(-1,1,1)
        objs = objs*mask + null_objs*(1-mask)
        
        # add pos 
        objs = objs + self.pos_embedding

        # fuse them 
        objs = self.linears(objs)

        assert objs.shape == torch.Size([B,self.num_tokens,self.out_dim])        
        return objs