import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from utils import *


class Lenet(nn.Module):

    def __init__(self):
        super(Lenet, self).__init__()
        self.conv1 = nn.Conv2d(1,6,5,stride=1,padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1   = nn.Linear(400, 120)
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 10)


    def forward(self, x):

        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)

        clean = F.softmax(out, 1)


        return clean


class Linear(nn.Module):
    def __init__(self, in_features, out_features):

        super(Linear, self).__init__()
        self.w = nn.Parameter(torch.randn(in_features, out_features))
         
    def forward(self, x):
        x = x.mm(self.w)
        return x 
    
class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4
    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))


    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x, revision=True):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)

        out = self.linear(out)

        clean = F.softmax(out, 1)

        return clean
        
def ResNet18(num_classes):
    return ResNet(BasicBlock, [2,2,2,2], num_classes)
    
def ResNet34(num_classes):
    return ResNet(BasicBlock, [3,4,6,3], num_classes)

def ResNet50(num_classes):
    return ResNet(Bottleneck, [3,4,6,3], num_classes)

def ResNet101(num_classes):
    return ResNet(Bottleneck, [3,4,23,3], num_classes)

def ResNet152(num_classes):
    return ResNet(Bottleneck, [3,8,36,3], num_classes)


class sig_t(nn.Module):
    def __init__(self, device, num_classes, init=2):
        super(sig_t, self).__init__()

        self.register_parameter(name='w', param=nn.parameter.Parameter(-init*torch.ones(num_classes, num_classes)))

        self.w.to(device)

        co = torch.ones(num_classes, num_classes)
        ind = np.diag_indices(co.shape[0])
        co[ind[0], ind[1]] = torch.zeros(co.shape[0])
        self.co = co.to(device)
        self.identity = torch.eye(num_classes).to(device)


    def forward(self):
        sig = torch.sigmoid(self.w)
        T = self.identity.detach() + sig*self.co.detach()
        T = F.normalize(T, p=1, dim=1)

        return T
