import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import io
from PIL import Image
import matplotlib.pyplot as plt

import torchvision.transforms as transforms
from torchvision.models import vgg19, VGG19_Weights

from torchvision import models
import matplotlib.pyplot as plt
import torchvision.utils as vutils


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

def image_loader(image):
    #image = Image.open(image_name)
    # fake batch dimension required to fit network's input dimensions

    imsize = 512 if torch.cuda.is_available() else 128  # use small size if no GPU

    loader = transforms.Compose([
        transforms.Resize(imsize),  # scale imported image
        transforms.ToTensor()])  # transform it into a torch tensor
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)

def save_image(tensor, path):
    image = tensor.clone().detach()
    image = image.squeeze(0)
    image = transforms.ToPILImage()(image)
    image.save(path)

class ContentLoss(nn.Module):
    def __init__(self, target,):
        super(ContentLoss, self).__init__()
        # we 'detach' the target content from the tree used
        # to dynamically compute the gradient: this is a stated value,
        # not a variable. Otherwise the forward method of the criterion
        # will throw an error.
        self.target = target.detach()
    def forward(self, input):
        self.loss = F.mse_loss(input, self.target)
        return input
    
def gram_matrix(input):
    a, b, c, d = input.size()  # a=batch size(=1)
    # b=number of feature maps
    # (c,d)=dimensions of a f. map (N=c*d)
    features = input.view(a * b, c * d)  # resize F_XL into \hat F_XL
    G = torch.mm(features, features.t())  # compute the gram product
    # we 'normalize' the values of the gram matrix
    # by dividing by the number of element in each feature maps.
    return G.div(a * b * c * d)

class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super(StyleLoss, self).__init__()
        self.target = gram_matrix(target_feature).detach()
    def forward(self, input):
        G = gram_matrix(input)
        self.loss = F.mse_loss(G, self.target)
        return input
    
#cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.eval()

#cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406])
#cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225])

# create a module to normalize input image so we can easily put it in a
# ``nn.Sequential``
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super(Normalization, self).__init__()
        # .view the mean and std to make them [C x 1 x 1] so that they can
        # directly work with image Tensor of shape [B x C x H x W].
        # B is batch size. C is number of channels. H is height and W is width.
        self.mean = torch.tensor(mean).view(-1, 1, 1)
        self.std = torch.tensor(std).view(-1, 1, 1)
    def forward(self, img):
        # normalize ``img``
        return (img - self.mean) / self.std
    
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']

def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                               style_img, content_img,
                               content_layers=content_layers_default,
                               style_layers=style_layers_default):
    # normalization module
    normalization = Normalization(normalization_mean, normalization_std)

    # just in order to have an iterable access to or list of content/style
    # losses
    content_losses = []
    style_losses = []

    # assuming that ``cnn`` is a ``nn.Sequential``, so we make a new ``nn.Sequential``
    # to put in modules that are supposed to be activated sequentially
    model = nn.Sequential(normalization)

    i = 0  # increment every time we see a conv
    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = 'conv_{}'.format(i)
        elif isinstance(layer, nn.ReLU):
            name = 'relu_{}'.format(i)
            # The in-place version doesn't play very nicely with the ``ContentLoss``
            # and ``StyleLoss`` we insert below. So we replace with out-of-place
            # ones here.
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = 'pool_{}'.format(i)
        elif isinstance(layer, nn.BatchNorm2d):
            name = 'bn_{}'.format(i)
        else:
            raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))

        model.add_module(name, layer)

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

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

    # now we trim off the layers after the last content and style losses
    for i in range(len(model) - 1, -1, -1):
        if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
            break

    model = model[:(i + 1)]

    return model, style_losses, content_losses

def get_input_optimizer(input_img):
    # this line to show that input is a parameter that requires a gradient
    optimizer = optim.LBFGS([input_img])
    return optimizer

class StyleTransferModel:
    def __init__(self, content_img, style_img, num_steps=300, style_weight=1000000, content_weight=1):
        self.content_img = content_img
        self.style_img = style_img.resize(content_img.size)
        #self.style_img = self.style_img.resize(self.content_img.size)
        self.style_img = image_loader(self.style_img)
        self.content_img = image_loader(self.content_img)
        self.input_img = self.content_img.clone()
        self.num_steps = num_steps
        self.style_weight = style_weight
        self.content_weight = content_weight

        self.cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.to(device).eval()
        self.cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
        self.cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
        
    def run_style_transfer(self):
        print('Building the style transfer model..')
        model, style_losses, content_losses = get_style_model_and_losses(
            self.cnn, self.cnn_normalization_mean, self.cnn_normalization_std, 
            self.style_img, self.content_img)

        self.input_img.requires_grad_(True)
        model.eval()
        model.requires_grad_(False)
        optimizer = get_input_optimizer(self.input_img)

        print('Optimizing..')
        run = [0]
        while run[0] <= self.num_steps:
            def closure():
                with torch.no_grad():
                    self.input_img.clamp_(0, 1)

                optimizer.zero_grad()
                model(self.input_img)

                style_score = 0
                content_score = 0

                for sl in style_losses:
                    style_score += sl.loss
                for cl in content_losses:
                    content_score += cl.loss

                style_score *= self.style_weight
                content_score *= self.content_weight

                loss = style_score + content_score
                loss.backward()

                run[0] += 1
                if run[0] % 50 == 0:
                    print(f"run {run[0]}:")
                    print(f'Style Loss : {style_score.item():4f} Content Loss: {content_score.item():4f}')
                    print()

                return style_score + content_score

            optimizer.step(closure)

        with torch.no_grad():
            self.input_img.clamp_(0, 1)

        return self.input_img