from torch import nn
import torch.nn.functional as F
import torch


class MLP(nn.Module):
    def __init__(self, dim_in, dim_hidden, dim_out):
        super(MLP, self).__init__()
        self.layer_input = nn.Linear(dim_in, dim_hidden)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout()
        self.layer_hidden = nn.Linear(dim_hidden, dim_out)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
        x = self.layer_input(x)
        x = self.dropout(x)
        x = self.relu(x)
        x = self.layer_hidden(x)
        # return self.softmax(x)
        return x


class CNNMnist(nn.Module):
    def __init__(self):
        super(CNNMnist, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        # return F.log_softmax(x, dim=1)
        return x

class CNNFemnist(nn.Module):
    def __init__(self, args):
        super(CNNFemnist, self).__init__()
        self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=3)
        self.conv2 = nn.Conv2d(10, args.out_channels, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(int(16820/20*args.out_channels), 50)
        self.fc2 = nn.Linear(50, args.num_classes)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
        x1 = F.relu(self.fc1(x))
        x = F.dropout(x1, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1), x1


class CNNFashion_Mnist(nn.Module):
    def __init__(self):
        super(CNNFashion_Mnist, self).__init__()
        self.pool = nn.MaxPool2d(2, 2)
        self.bn1 = nn.BatchNorm2d(16)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
        self.fc1 = nn.Linear(7*7*32, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        # x = x.view(x.size(0), -1)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        return x

class CNNCifar(nn.Module):
    def __init__(self):
        super(CNNCifar, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        # x = x.view(x.size(0), -1)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class CNNPath(nn.Module):
    def __init__(self):
        super(CNNPath, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 9)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        # x = x.view(x.size(0), -1)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class CNNTissue(nn.Module):
    def __init__(self):
        super(CNNTissue, self).__init__()
        self.pool = nn.MaxPool2d(2, 2)
        self.bn1 = nn.BatchNorm2d(16)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
        self.fc1 = nn.Linear(7*7*32, 8)

    def forward(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        # x = x.view(x.size(0), -1)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        return x

    # def __init__(self):
    #     super(CNNTissue, self).__init__()
    #     self.conv1 = nn.Sequential(
    #         nn.Conv2d(1, 16, kernel_size=5, padding=2),
    #         nn.BatchNorm2d(16),
    #         nn.ReLU(),
    #         nn.MaxPool2d(2))
    #     self.conv2 = nn.Sequential(
    #         nn.Conv2d(16, 32, kernel_size=5, padding=2),
    #         nn.BatchNorm2d(32),
    #         nn.ReLU(),
    #         nn.MaxPool2d(2))
    #     self.fc = nn.Linear(7*7*32, 8)

    # def forward(self, x):
    #     out = self.conv1(x)
    #     out = self.conv2(out)
    #     out = out.view(out.size(0), -1)
    #     out = self.fc(out)
    #     return out

class Lenet(nn.Module):
    def __init__(self, args):
        super(Lenet, self).__init__()
        self.n_cls = 10
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=5)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=5)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 5 * 5, 384)
        self.fc2 = nn.Linear(384, 192)
        self.fc3 = nn.Linear(192, self.n_cls)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 5 * 5)
        x1 = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x1))
        x = self.fc3(x)

        return F.log_softmax(x, dim=1), x1

# define a simple CNN model

class oct_net(nn.Module):
    def __init__(self, in_channels = 1, num_classes = 4):
        super(oct_net, self).__init__()

        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels, 16, kernel_size=3),
            nn.BatchNorm2d(16),
            nn.ReLU())

        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 16, kernel_size=3),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))

        self.layer3 = nn.Sequential(
            nn.Conv2d(16, 64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU())
        
        self.layer4 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU())

        self.layer5 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))

        self.fc = nn.Sequential(
            nn.Linear(64 * 4 * 4, 128),
            nn.ReLU(),
            nn.Linear(128, 128),
            nn.ReLU(),
            nn.Linear(128, num_classes))

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x