import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from collections import namedtuple
import torch.nn as nn
import torchvision.models.vgg as vgg


class VGG19(nn.Module):
    def __init__(self, requires_grad=False):
        super(VGG19, self).__init__()
        vgg_pretrained_features = vgg.vgg19(pretrained=True).features
        self.slice1 = nn.Sequential()
        self.slice2 = nn.Sequential()
        self.slice3 = nn.Sequential()
        self.slice4 = nn.Sequential()
        self.slice5 = nn.Sequential()
        for x in range(4):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(4, 9):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(9, 18):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(18, 27):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(27, 36):
            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 = self.slice1(X)
        h_relu1_2 = h
        h = self.slice2(h)
        h_relu2_2 = h
        h = self.slice3(h)
        h_relu3_4 = h
        h = self.slice4(h)
        h_relu4_4 = h
        h = self.slice5(h)
        h_relu5_4 = h

        vgg_outputs = namedtuple(
            "VggOutputs", ['relu1_2', 'relu2_2',
                           'relu3_4', 'relu4_4', 'relu5_4'])
        out = vgg_outputs(h_relu1_2, h_relu2_2,
                          h_relu3_4, h_relu4_4, h_relu5_4)

        return out
