from PIL import Image
import torch
import torch.optim as optim
from torchvision import transforms
import torch.nn as nn
from Models_Class.NST_class import (
    ContentLoss,
    Normalization,
    StyleLoss,
)
                                    
import copy

style_weight = 1e8
content_weight = 1e1
def image_loader(image_path, loader, device):
    image = Image.open(image_path).convert('RGB')
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)

def save_image(tensor, path="output.png"):
    image = tensor.cpu().clone()
    image = image.squeeze(0)
    image = transforms.ToPILImage()(image)
    image.save(path)

def gram_matrix(input):
    a, b, c, d = input.size()
    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    return G.div(a * b * c * d)


def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                               style_img, content_img, content_layers, style_layers, device):
    cnn = copy.deepcopy(cnn)
    normalization = Normalization(normalization_mean, normalization_std).to(device)
    content_losses = []
    style_losses = []
    model = nn.Sequential(normalization)

    i = 0
    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = f'conv_{i}'
        elif isinstance(layer, nn.ReLU):
            name = f'relu_{i}'
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = f'pool_{i}'
        elif isinstance(layer, nn.BatchNorm2d):
            name = f'bn_{i}'
        else:
            raise RuntimeError(f'Unrecognized layer: {layer.__class__.__name__}')

        model.add_module(name, layer)

        if name in content_layers:
            target = model(content_img).detach()
            content_loss = ContentLoss(target)
            model.add_module(f"content_loss_{i}", content_loss)
            content_losses.append(content_loss)

        if name in style_layers:
            target_feature = model(style_img).detach()
            style_loss = StyleLoss(target_feature)
            model.add_module(f"style_loss_{i}", style_loss)
            style_losses.append(style_loss)

    for i in range(len(model) - 1, -1, -1):
        if isinstance(model[i], (ContentLoss, StyleLoss)):
            break
    model = model[:i+1]
    return model, style_losses, content_losses



def run_style_transfer(cnn, normalization_mean, normalization_std,
                       content_img, style_img, input_img,content_layers, style_layers, device, num_steps=300):
    print("Building the style transfer model..")
    model, style_losses, content_losses = get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                               style_img, content_img,content_layers, style_layers, device )
    optimizer = optim.LBFGS([input_img.requires_grad_()])

    print("Optimizing..")
    run = [0]
    while run[0] <= num_steps:
        def closure():
            input_img.data.clamp_(0, 1)
            optimizer.zero_grad()
            model(input_img)
            style_score = sum(sl.loss for sl in style_losses)
            content_score = sum(cl.loss for cl in content_losses)
            loss = style_weight * style_score + content_weight * content_score
            loss.backward()

            if run[0] % 50 == 0:
                print(f"Step {run[0]}:")
                print(f"  Style Loss: {style_score.item():.4f}")
                print(f"  Content Loss: {content_score.item():.4f}")
                print(f"  Total Loss: {loss.item():.4f}\n")

            run[0] += 1
            return loss

        optimizer.step(closure)

    input_img.data.clamp_(0, 1)
    return input_img