import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torch.nn.utils.spectral_norm as spectral_norm

# ResNet block used in pix2pixHD
# We keep the same architecture as pix2pixHD.
class ResnetBlock(nn.Module):
    def __init__(self, dim, norm_layer, activation=nn.ReLU(False), kernel_size=3):
        super().__init__()

        pw = (kernel_size - 1) // 2
        self.conv_block = nn.Sequential(
            nn.ReflectionPad2d(pw),
            nn.Conv2d(dim, dim, kernel_size=kernel_size),
            norm_layer(dim),
            activation,
            nn.ReflectionPad2d(pw),
            nn.Conv2d(dim, dim, kernel_size=kernel_size),
            norm_layer(dim),
        )

    def forward(self, x):
        y = self.conv_block(x)
        out = x + y
        return out

class Alex(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super().__init__()
        alex_pretrained_features = torchvision.models.alexnet(pretrained=True).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        for x in range(2):
            self.slice1.add_module(str(x), alex_pretrained_features[x])
        for x in range(2, 7):
            self.slice2.add_module(str(x), alex_pretrained_features[x])
        for x in range(7, 12):
            self.slice3.add_module(str(x), alex_pretrained_features[x])
        for x in range(12, 21):
            self.slice4.add_module(str(x), alex_pretrained_features[x])
        for x in range(21, 30):
            self.slice5.add_module(str(x), alex_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h_relu1 = self.slice1(X)
        h_relu2 = self.slice2(h_relu1)
        h_relu3 = self.slice3(h_relu2)
        h_relu4 = self.slice4(h_relu3)
        h_relu5 = self.slice5(h_relu4)
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out

# VGG architecter, used for the perceptual loss using a pretrained VGG network
class VGG19(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super().__init__()
        vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        for x in range(2):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(2, 7):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(7, 12):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(12, 21):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(21, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h_relu1 = self.slice1(X)
        h_relu2 = self.slice2(h_relu1)
        h_relu3 = self.slice3(h_relu2)
        h_relu4 = self.slice4(h_relu3)
        h_relu5 = self.slice5(h_relu4)
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out
