import torch
import torch.nn as nn
from torchvision import transforms
from timm import create_model
from genconvit.config import load_config
from .model_embedder import HybridEmbed
import os
config = load_config()
torch.hub.set_dir('./cache')
os.environ["HUGGINGFACE_HUB_CACHE"] = "./cache"
class Encoder(nn.Module):

    def __init__(self, latent_dims=4):
        super(Encoder, self).__init__()

        self.features = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(num_features=16),
            nn.LeakyReLU(),
            
            nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(num_features=32),
            nn.LeakyReLU(),
            
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(num_features=64),
            nn.LeakyReLU(),

            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(num_features=128),
            nn.LeakyReLU()
        )

        self.latent_dims = latent_dims
        self.fc1 = nn.Linear(128*14*14, 256)
        self.fc2 = nn.Linear(256, 128)
        self.mu = nn.Linear(128*14*14, self.latent_dims)
        self.var = nn.Linear(128*14*14, self.latent_dims)

        self.kl = 0
        self.kl_weight = 0.5#0.00025
        self.relu = nn.LeakyReLU()

    def reparameterize(self, x):
        # https://github.com/AntixK/PyTorch-VAE/blob/a6896b944c918dd7030e7d795a8c13e5c6345ec7/models/vanilla_vae.py
        std = torch.exp(0.5*self.mu(x))
        eps = torch.randn_like(std)
        z = eps * std + self.mu(x)

        return z, std

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, start_dim=1)

        mu =  self.mu(x)
        var = self.var(x)
        z,_ = self.reparameterize(x)
        self.kl = self.kl_weight*torch.mean(-0.5*torch.sum(1+var - mu**2 - var.exp(), dim=1), dim=0) 
        
        return z

class Decoder(nn.Module):
  
    def __init__(self, latent_dims=4):
        super(Decoder, self).__init__()

        self.features = nn.Sequential(
            nn.ConvTranspose2d(256, 64, kernel_size=2, stride=2),
            nn.LeakyReLU(),

            nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2),
            nn.LeakyReLU(),

            nn.ConvTranspose2d(32, 16, kernel_size=2, stride=2),
            nn.LeakyReLU(),

            nn.ConvTranspose2d(16, 3, kernel_size=2, stride=2),
            nn.LeakyReLU()
        )

        self.latent_dims = latent_dims
        
        self.unflatten = nn.Unflatten(dim=1, unflattened_size=(256, 7, 7))

    def forward(self, x): 
        x = self.unflatten(x)
        x = self.features(x)
        return x
        
class GenConViTVAE(nn.Module):
    def __init__(self, config, pretrained=True):
        super(GenConViTVAE, self).__init__()
        self.latent_dims = config['model']['latent_dims']
        self.encoder = Encoder(self.latent_dims)
        self.decoder = Decoder(self.latent_dims)
        self.embedder = create_model(config['model']['embedder'], pretrained=True)
        self.convnext_backbone = create_model(config['model']['backbone'], pretrained=True, num_classes=1000, drop_path_rate=0, head_init_scale=1.0)
        self.convnext_backbone.patch_embed = HybridEmbed(self.embedder, img_size=config['img_size'], embed_dim=768)
        self.num_feature = self.convnext_backbone.head.fc.out_features * 2
 
        self.fc = nn.Linear(self.num_feature, self.num_feature//4)
        self.fc3 = nn.Linear(self.num_feature//2, self.num_feature//4)
        self.fc2 = nn.Linear(self.num_feature//4, config['num_classes'])
        self.relu = nn.ReLU()
        self.resize = transforms.Resize((224,224), antialias=True)

    def forward(self, x):
        z = self.encoder(x)
        x_hat = self.decoder(z)

        x1 = self.convnext_backbone(x)
        x2 = self.convnext_backbone(x_hat)
        x = torch.cat((x1,x2), dim=1)
        x = self.fc2(self.relu(self.fc(self.relu(x))))
        
        return x, self.resize(x_hat)