import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
import torchvision.transforms.functional as TF

# 🚀 Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 🔧 Preprocessing
transform = transforms.Compose([
    transforms.Resize((512, 512)),
    transforms.ToTensor()
])

def load_image(img):
    image = img.convert("RGB")
    return transform(image).unsqueeze(0).to(device)

# 🎯 Loss modules
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super().__init__()
        self.mean = mean.view(-1, 1, 1)
        self.std = std.view(-1, 1, 1)
    def forward(self, img):
        return (img - self.mean) / self.std

class ContentLoss(nn.Module):
    def __init__(self, target):
        super().__init__()
        self.target = target.detach()
        self.loss = 0
    def forward(self, input):
        self.loss = nn.functional.mse_loss(input, self.target)
        return input

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

class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super().__init__()
        self.target = gram_matrix(target_feature).detach()
        self.loss = 0
    def forward(self, input):
        G = gram_matrix(input)
        self.loss = nn.functional.mse_loss(G, self.target)
        return input

# 🧬 Model builder
def get_model_losses(cnn, norm_mean, norm_std, style_img, content_img):
    normalization = Normalization(norm_mean, norm_std).to(device)
    model = nn.Sequential(normalization)

    content_losses = []
    style_losses = []

    i = 0
    for layer in cnn.children():
        name = None
        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}"
        if name:
            model.add_module(name, layer)
            if name == "conv_4":
                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 ["conv_1", "conv_2", "conv_3", "conv_4", "conv_5"]:
                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 j in range(len(model) - 1, -1, -1):
        if isinstance(model[j], ContentLoss) or isinstance(model[j], StyleLoss):
            break

    return model[:j + 1], style_losses, content_losses

# ✨ Stylization pipeline
def run_nst(content_pil, style_pil, steps=300):
    content = load_image(content_pil)
    style = load_image(style_pil)
    input_img = content.clone().requires_grad_(True)

    cnn = models.vgg19(pretrained=True).features.to(device).eval()
    norm_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
    norm_std = torch.tensor([0.229, 0.224, 0.225]).to(device)

    model, style_losses, content_losses = get_model_losses(
        cnn, norm_mean, norm_std, style, content
    )

    optimizer = optim.LBFGS([input_img])
    run = [0]

    while run[0] <= 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 = content_score + 1e6 * style_score
            loss.backward()

            run[0] += 1
            return loss
        optimizer.step(closure)

    output = input_img.clone().detach().cpu().squeeze(0)
    return TF.to_pil_image(output)