import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data.dataloader import DataLoader
from torchvision import transforms
from torchvision import utils as vutils

from models import Generator
from utils import copy_G_params, load_params



def get_early_features(net, noise):
    with torch.no_grad():
        feat_4 = net._init(noise)
        feat_8 = net._upsample_8(feat_4)
        feat_16 = net._upsample_16(feat_8)
        feat_32 = net._upsample_32(feat_16)
        feat_64 = net._upsample_64(feat_32)
    return feat_8, feat_16, feat_32, feat_64

def get_late_features(net, feat_64, feat_8, feat_16, feat_32):
    with torch.no_grad():
        feat_128 = net._upsample_128(feat_64)
        feat_128 = net._sle_128(feat_8, feat_128)

        feat_256 = net._upsample_256(feat_128)
        feat_256 = net._sle_256(feat_16, feat_256)

        feat_512 = net._upsample_512(feat_256)
        feat_512 = net._sle_512(feat_32, feat_512)

        feat_1024 = net._upsample_1024(feat_512)

    return net._out_1024(feat_1024)

def style_mix(model_name_or_path, bs, device):
    _in_channels = 256
    im_size = 1024

    netG = Generator(in_channels=_in_channels, out_channels=3)
    netG = netG.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
    _ = netG.to(device)
    _ = netG.eval()

    avg_param_G = copy_G_params(netG)
    load_params(netG, avg_param_G)

    noise_a = torch.randn(bs, 256, 1, 1, device=device).to(device)
    noise_b = torch.randn(bs, 256, 1, 1, device=device).to(device)

    feat_8_a, feat_16_a, feat_32_a, feat_64_a = get_early_features(netG, noise_a)
    feat_8_b, feat_16_b, feat_32_b, feat_64_b = get_early_features(netG, noise_b)

    images_b = get_late_features(netG, feat_64_b, feat_8_b, feat_16_b, feat_32_b)
    images_a = get_late_features(netG, feat_64_a, feat_8_a, feat_16_a, feat_32_a)

    imgs = [ torch.ones(1, 3, im_size, im_size) ]

    imgs.append(images_b.cpu())
    for i in range(bs):
        imgs.append(images_a[i].unsqueeze(0).cpu())
        gimgs = get_late_features(netG, feat_64_a[i].unsqueeze(0).repeat(bs, 1, 1, 1), feat_8_b, feat_16_b, feat_32_b)
        imgs.append(gimgs.cpu())

    imgs = torch.cat(imgs)
    # vutils.save_image(imgs.add(1).mul(0.5), 'style_mix/style_mix_2.jpg', nrow=bs+1)

    return imgs