import torch
import torch.nn as nn
import torchvision.models as models

class StyleLoss(nn.Module):
    r"""
    Perceptual loss, VGG-based
    https://arxiv.org/abs/1603.08155
    https://github.com/dxyang/StyleTransfer/blob/master/utils.py
    """

    def __init__(self):
        super(StyleLoss, self).__init__()
        self.add_module('vgg', VGG19())
        self.criterion = torch.nn.L1Loss()

    def compute_gram(self, x):
        b, ch, h, w = x.size()
        f = x.view(b, ch, w * h)
        f_T = f.transpose(1, 2)
        G = f.bmm(f_T) / (h * w * ch)

        return G

    def __call__(self, x, y):
        # Compute features
        x_vgg, y_vgg = self.vgg(x), self.vgg(y)

        # Compute loss
        style_loss = 0.0
        style_loss += self.criterion(self.compute_gram(x_vgg['relu2_2']), self.compute_gram(y_vgg['relu2_2']))
        style_loss += self.criterion(self.compute_gram(x_vgg['relu3_4']), self.compute_gram(y_vgg['relu3_4']))
        style_loss += self.criterion(self.compute_gram(x_vgg['relu4_4']), self.compute_gram(y_vgg['relu4_4']))
        style_loss += self.criterion(self.compute_gram(x_vgg['relu5_2']), self.compute_gram(y_vgg['relu5_2']))

        return style_loss

class VGG19(torch.nn.Module):
    def __init__(self):
        super(VGG19, self).__init__()
        features = models.vgg19(pretrained=True).features
        features.cuda()
        self.relu1_1 = torch.nn.Sequential()
        self.relu1_2 = torch.nn.Sequential()

        self.relu2_1 = torch.nn.Sequential()
        self.relu2_2 = torch.nn.Sequential()

        self.relu3_1 = torch.nn.Sequential()
        self.relu3_2 = torch.nn.Sequential()
        self.relu3_3 = torch.nn.Sequential()
        self.relu3_4 = torch.nn.Sequential()

        self.relu4_1 = torch.nn.Sequential()
        self.relu4_2 = torch.nn.Sequential()
        self.relu4_3 = torch.nn.Sequential()
        self.relu4_4 = torch.nn.Sequential()

        self.relu5_1 = torch.nn.Sequential()
        self.relu5_2 = torch.nn.Sequential()
        self.relu5_3 = torch.nn.Sequential()
        self.relu5_4 = torch.nn.Sequential()

        for x in range(2):
            self.relu1_1.add_module(str(x), features[x])

        for x in range(2, 4):
            self.relu1_2.add_module(str(x), features[x])

        for x in range(4, 7):
            self.relu2_1.add_module(str(x), features[x])

        for x in range(7, 9):
            self.relu2_2.add_module(str(x), features[x])

        for x in range(9, 12):
            self.relu3_1.add_module(str(x), features[x])

        for x in range(12, 14):
            self.relu3_2.add_module(str(x), features[x])

        for x in range(14, 16):
            self.relu3_3.add_module(str(x), features[x])

        for x in range(16, 18):
            self.relu3_4.add_module(str(x), features[x])

        for x in range(18, 21):
            self.relu4_1.add_module(str(x), features[x])

        for x in range(21, 23):
            self.relu4_2.add_module(str(x), features[x])

        for x in range(23, 25):
            self.relu4_3.add_module(str(x), features[x])

        for x in range(25, 27):
            self.relu4_4.add_module(str(x), features[x])

        for x in range(27, 30):
            self.relu5_1.add_module(str(x), features[x])

        for x in range(30, 32):
            self.relu5_2.add_module(str(x), features[x])

        for x in range(32, 34):
            self.relu5_3.add_module(str(x), features[x])

        for x in range(34, 36):
            self.relu5_4.add_module(str(x), features[x])

        # don't need the gradients, just want the features
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, x):
        relu1_1 = self.relu1_1(x)
        relu1_2 = self.relu1_2(relu1_1)

        relu2_1 = self.relu2_1(relu1_2)
        relu2_2 = self.relu2_2(relu2_1)

        relu3_1 = self.relu3_1(relu2_2)
        relu3_2 = self.relu3_2(relu3_1)
        relu3_3 = self.relu3_3(relu3_2)
        relu3_4 = self.relu3_4(relu3_3)

        relu4_1 = self.relu4_1(relu3_4)
        relu4_2 = self.relu4_2(relu4_1)
        relu4_3 = self.relu4_3(relu4_2)
        relu4_4 = self.relu4_4(relu4_3)

        relu5_1 = self.relu5_1(relu4_4)
        relu5_2 = self.relu5_2(relu5_1)
        relu5_3 = self.relu5_3(relu5_2)
        relu5_4 = self.relu5_4(relu5_3)

        out = {
            'relu1_1': relu1_1,
            'relu1_2': relu1_2,

            'relu2_1': relu2_1,
            'relu2_2': relu2_2,

            'relu3_1': relu3_1,
            'relu3_2': relu3_2,
            'relu3_3': relu3_3,
            'relu3_4': relu3_4,

            'relu4_1': relu4_1,
            'relu4_2': relu4_2,
            'relu4_3': relu4_3,
            'relu4_4': relu4_4,

            'relu5_1': relu5_1,
            'relu5_2': relu5_2,
            'relu5_3': relu5_3,
            'relu5_4': relu5_4,
        }
        return out