import torch
import torchvision
from torchvision import datasets, models, transforms
import torch.nn as nn
import torch.nn.functional as F
import os
import clip
# from pixel2style2pixel.models.psp import pSp
from argparse import Namespace
from utils import normalize
from stylegan.stylegan2_generator import StyleGAN2Generator
import hydra
from omegaconf import DictConfig, OmegaConf
import sys
import os
sys.path.append('./pixel2style2pixel')

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


def get_prompt(cfg):
    model, preprocess = clip.load("RN50", device=device)
    prompt=cfg.prompt
    text=clip.tokenize(prompt).to(device)
    with torch.no_grad():
            prompt = model.encode_text(text)
    return prompt

def get_stylegan_generator(cfg):
    # model, preprocess = clip.load("RN50", device=device)
    resolution=cfg.resolution
    generator=StyleGAN2Generator(resolution=resolution)
    checkpoint = torch.load(cfg.paths.stylegan, map_location=device)
    generator.load_state_dict(checkpoint['generator'])
    generator.to(device)
    generator.eval()
    return generator


# class GanAttack(nn.Module):

# def get_stylegan_inverter(cfg):

#     # ensure_checkpoint_exists(ckpt_path)
#     path=cfg.paths.inverter_cfg
#     ckpt = torch.load(path, map_location='cuda:0')
#     opts = ckpt['opts']
#     opts['checkpoint_path'] = path
#     if 'learn_in_w' not in opts:
#         opts['learn_in_w'] = False
#     if 'output_size' not in opts:
#         opts['output_size'] = 1024
        
#     net = pSp(Namespace(**opts))
#     net.eval()
#     net.cuda()
#     return net
    
class GanAttack(nn.Module):
    def __init__(self, cfg,prompt):
        super().__init__()

        # self.net= get_stylegan_inverter(cfg)
        # self.generator.eval()
        # self.inverter=inverter
        # self.images_resize=images_resize
        self.generator=get_stylegan_generator(cfg)
        self.prompt=prompt
        text_len=self.prompt.shape[1]
        self.mlp=nn.Sequential(
            nn.Linear(text_len+512, 4096),
                                 nn.ReLU(inplace=True),
                                 nn.Linear(4096, 512)
        )
        


    def forward(self, img,basecode,detailcode):
        batch_size=img.shape[0]
        prompt=self.prompt.repeat(batch_size,18,1).to(device)
        # print(prompt.shape)
        x_prompt=torch.cat([basecode,prompt],dim=2)
        x_prompt=self.mlp(x_prompt)
        x=x_prompt+x
        result_images=self.generator.synthesis(detailcode,randomize_noise=False,basecode=x)['image']
        

        return result_images,x
    
class CLIPLoss(torch.nn.Module):
    def __init__(self):
        super(CLIPLoss, self).__init__()
        self.model, self.preprocess = clip.load("RN50", device="cuda")
        self.model.eval()
        self.face_pool = torch.nn.AdaptiveAvgPool2d((224, 224))
        # self.mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device="cuda").view(1,3,1,1)
        # self.std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device="cuda").view(1,3,1,1)

    def forward(self, image, text):
        image=normalize(image)
        image = self.face_pool(image)
        similarity = 1 - self.model(image, text)[0]/ 100
        return similarity
    

class VggLoss(torch.nn.Module):
    def __init__(self):
        super(VggLoss, self).__init__()
        self.model=models.vgg11(pretrained=True)
        self.model.features=nn.Sequential()
        
        # self.mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device="cuda").view(1,3,1,1)
        # self.std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device="cuda").view(1,3,1,1)

    def forward(self, image1, image2):
        # image=normalize(image)
        with torch.no_grad:
            feature1=self.model(image1)
            feature2=self.model(image2)
        feature1=torch.flatten(feature1)
        feature2=torch.flatten(feature2)
        similarity = F.cosine_similarity(feature1,feature2)
        return similarity
    
@hydra.main(version_base=None, config_path="./config", config_name="config")
def test(cfg):
    prompt=torch.randn([1,1024]).to(device)
    model=GanAttack(cfg,prompt).to(device)
    data=torch.randn([2,3,256,256]).to(device)
    result_images,x=model(data)
    print(result_images.shape)
    print(x.shape)
if __name__ == "__main__":
    test()