# codes are import from https://github.com/xternalz/WideResNet-pytorch/blob/master/wideresnet.py
# original author: xternalz

import math
import torch
import torch.nn as nn
import torch.nn.functional as F

from utils import count_parameters
from model.dsbn import *

class Expression(nn.Module):
    def __init__(self, func):
        super(Expression, self).__init__()
        self.func = func
    
    def forward(self, input):
        return self.func(input)

class LogisticRegression(torch.nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LogisticRegression, self).__init__()
        self.linear = torch.nn.Linear(input_dim, output_dim)

    def forward(self, x):
        outputs = self.linear(x)
        return outputs

class MLP_bns(torch.nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim=256):
        super(MLP_bns, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dim = hidden_dim
        self.num_domains = 2

        self.fc1 = nn.Linear(self.input_dim, self.hidden_dim, bias=True)
        self.relu = nn.ReLU(inplace=True)
        self.bn = DomainSpecificBatchNorm1d(self.hidden_dim, self.num_domains)
        self.fc2 = nn.Linear(self.hidden_dim, self.output_dim, bias=True)

    def forward(self, x, domain_label):
        x, _ = self.bn(self.fc1(x), domain_label)
        x = self.relu(x)
        out = self.fc2(x)
        return out

class MLP(torch.nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim=256):
        super(MLP, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dim = hidden_dim

        self.fc1 = nn.Linear(self.input_dim, self.hidden_dim, bias=True)
        self.relu = nn.ReLU(inplace=True)
        self.bn = nn.BatchNorm1d(self.hidden_dim)
        self.fc2 = nn.Linear(self.hidden_dim, self.output_dim, bias=True)

    def forward(self, x):
        x = self.bn(self.fc1(x))
        x = self.relu(x)
        out = self.fc2(x)
        return out

class Model(nn.Module):
    def __init__(self, i_c=1, n_c=10):
        super(Model, self).__init__()

        self.conv1 = nn.Conv2d(i_c, 32, 5, stride=1, padding=2, bias=True)
        self.pool1 = nn.MaxPool2d((2, 2), stride=(2, 2), padding=0)

        self.conv2 = nn.Conv2d(32, 64, 5, stride=1, padding=2, bias=True)
        self.pool2 = nn.MaxPool2d((2, 2), stride=(2, 2), padding=0)

        self.flatten = Expression(lambda tensor: tensor.view(tensor.shape[0], -1))
        self.fc1 = nn.Linear(7 * 7 * 64, 1024, bias=True)
        self.fc2 = nn.Linear(1024, n_c)

    def forward(self, x_i, _eval=False):

        if _eval:
            # switch to eval mode
            self.eval()
        else:
            self.train()
            
        x_o = self.conv1(x_i)
        x_o = torch.relu(x_o)
        x_o = self.pool1(x_o)

        x_o = self.conv2(x_o)
        x_o = torch.relu(x_o)
        x_o = self.pool2(x_o)

        x_o = self.flatten(x_o)

        x_o = torch.relu(self.fc1(x_o))

        self.train()

        return self.fc2(x_o)





class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                               padding=0, bias=False) or None
    def forward(self, x):
        if not self.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)

class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
        return nn.Sequential(*layers)
    def forward(self, x):
        return self.layer(x)

class WideResNet(nn.Module):
    def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
        super(WideResNet, self).__init__()
        nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
        assert((depth - 4) % 6 == 0)
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x, _eval=False):
        if _eval:
            # switch to eval mode
            self.eval()
        else:
            self.train()

        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        out = self.block3(out)
        out = self.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(-1, self.nChannels)

        self.train()

        return self.fc(out)


if __name__ == '__main__':
    i = torch.FloatTensor(4, 3, 32, 32)

    n = WideResNet(depth=34, num_classes=10, widen_factor=10, dropRate=0.0)

    # print(n(i).size())

    print(count_parameters(n))

