import torch
import torchvision
import torch.nn as nn

# Print losses occasionally and print to tensorboard
def plot_to_tensorboard(
    writer, loss_critic, loss_gen, real, fake, tensorboard_step
):
    writer.add_scalar("Loss Critic", loss_critic, global_step=tensorboard_step)

    with torch.no_grad():
        # take out (up to) 32 examples
        img_grid_real = torchvision.utils.make_grid(real[:8], normalize=True)
        img_grid_fake = torchvision.utils.make_grid(fake[:8], normalize=True)
        writer.add_image("Real", img_grid_real, global_step=tensorboard_step)
        writer.add_image("Fake", img_grid_fake, global_step=tensorboard_step)


def gradient_penalty(critic, real, fake, alpha, train_step, device="cpu"):
    BATCH_SIZE, C, H, W = real.shape
    beta = torch.rand((BATCH_SIZE, 1, 1, 1)).repeat(1, C, H, W).to(device)
    interpolated_images = real * beta + fake * (1 - beta)

    # Calculate critic scores
    mixed_scores = critic(interpolated_images, alpha, train_step)

    # Take the gradient of the scores with respect to the images
    gradient = torch.autograd.grad(
        inputs=interpolated_images,
        outputs=mixed_scores,
        grad_outputs=torch.ones_like(mixed_scores),
        create_graph=True,
        retain_graph=True,
    )[0]
    gradient = gradient.view(gradient.shape[0], -1)
    gradient_norm = gradient.norm(2, dim=1)
    gradient_penalty = torch.mean((gradient_norm - 1) ** 2)
    return gradient_penalty


def save_checkpoint(state, filename="celeba_wgan_gp.pth.tar"):
    print("=> Saving checkpoint")
    torch.save(state, filename)

def load_checkpoint(checkpoint, gen, disc, opt_gen=None, opt_disc=None):
    print("=> Loading checkpoint")
    gen.load_state_dict(checkpoint['gen'])
    disc.load_state_dict(checkpoint['critic'])

    if opt_gen != None and opt_disc != None:
        opt_gen.load_state_dict(checkpoint['opt_gen'])
        opt_disc.load_state_dict(checkpoint['opt_critic'])