from collections import namedtuple

import torch
from torchvision import models


class Vgg16(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super(Vgg16, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.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, 16):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(16, 23):
            self.slice4.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_3 = h
        h = self.slice4(h)
        h_relu4_3 = h
        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3'])
        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)
        return out


class Vgg19(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super(Vgg19, self).__init__()
        # vgg_pretrained_features = models.vgg19(pretrained=True).features
        self.vgg_pretrained_features = 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, indices=None):
        if indices is None:
            indices = [2, 7, 12, 21, 30]
        out = []
        # indices = sorted(indices)
        for i in range(indices[-1]):
            X = self.vgg_pretrained_features[i](X)
            if (i + 1) in indices:
                out.append(X)

        return out

        # 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


if __name__ == '__main__':
    vgg = Vgg19()
    import ipdb

    ipdb.set_trace()