import torch.nn as nn
import torch
import const
import densenet


STATE_DIM = 8 * 32
class OneNet(nn.Module):
    def __init__(self, input_dim, packet_num):
        super(OneNet, self).__init__()
        IN_DIM = input_dim * packet_num # byte
        FEATURE_DIM = 32
        
        # transform the given packet into a tensor which is in a good feature space
        self.feature_layer = nn.Sequential(
            nn.Linear(IN_DIM, 32),
            nn.ReLU(),
            nn.Linear(32, FEATURE_DIM),
            nn.ReLU()
        )

        # generates the current state 's'
        self.f = nn.Sequential(
            nn.Linear(STATE_DIM + FEATURE_DIM, STATE_DIM),
            nn.ReLU(),
            nn.Linear(STATE_DIM, STATE_DIM),
            nn.ReLU()
        )

        # check whether the given packet is malicious
        self.g = nn.Sequential(
            nn.Linear(STATE_DIM + FEATURE_DIM, 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, 2),
        )

    def forward(self, x, s):
        x   = self.feature_layer(x)
        x   = torch.cat((x, s), 1)
        s2  = self.f(x)
        x2  = self.g(x)

        return x2, s2


class CnnNet(nn.Module):
    def __init__(self):
        super(CnnNet, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(1, 2, 3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.conv2 = nn.Sequential(
          nn.Conv2d(2, 4, 3, padding=1),
          nn.ReLU(True),
          nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.conv3 = nn.Sequential(
          nn.Conv2d(4, 8, 3, padding=1),
          nn.ReLU(True),
          nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.fc4 = nn.Linear(8, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = torch.flatten(x, 1)
        x = self.fc4(x)
        return x


class DenseNet(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        cnn_model = densenet.DenseNet(num_classes=2)
        self.features = nn.Sequential(
            cnn_model
        )

    def forward(self, x):
        x = self.features(x)
        return x
import torch.nn as nn
import torch
import const
import densenet


STATE_DIM = 8 * 32
class OneNet(nn.Module):
    def __init__(self, input_dim, packet_num):
        super(OneNet, self).__init__()
        IN_DIM = input_dim * packet_num # byte
        FEATURE_DIM = 32
        
        # transform the given packet into a tensor which is in a good feature space
        self.feature_layer = nn.Sequential(
            nn.Linear(IN_DIM, 32),
            nn.ReLU(),
            nn.Linear(32, FEATURE_DIM),
            nn.ReLU()
        )

        # generates the current state 's'
        self.f = nn.Sequential(
            nn.Linear(STATE_DIM + FEATURE_DIM, STATE_DIM),
            nn.ReLU(),
            nn.Linear(STATE_DIM, STATE_DIM),
            nn.ReLU()
        )

        # check whether the given packet is malicious
        self.g = nn.Sequential(
            nn.Linear(STATE_DIM + FEATURE_DIM, 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, 2),
        )

    def forward(self, x, s):
        x   = self.feature_layer(x)
        x   = torch.cat((x, s), 1)
        s2  = self.f(x)
        x2  = self.g(x)

        return x2, s2


class CnnNet(nn.Module):
    def __init__(self):
        super(CnnNet, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(1, 2, 3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.conv2 = nn.Sequential(
          nn.Conv2d(2, 4, 3, padding=1),
          nn.ReLU(True),
          nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.conv3 = nn.Sequential(
          nn.Conv2d(4, 8, 3, padding=1),
          nn.ReLU(True),
          nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.fc4 = nn.Linear(8, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = torch.flatten(x, 1)
        x = self.fc4(x)
        return x


class DenseNet(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        cnn_model = densenet.DenseNet(num_classes=2)
        self.features = nn.Sequential(
            cnn_model
        )

    def forward(self, x):
        x = self.features(x)
        return x
# byte # transform the given packet into a tensor which is in a good feature space # generates the current state 's' # check whether the given packet is malicious