import torch
import torch.nn as nn


class ResNetSE(nn.Module):
    def __init__(self, block, layers, num_filters, nOut, encoder_type='SAP', n_mels=80, n_mel_T=1, log_input=True, **kwargs):
        super(ResNetSE, self).__init__()

        print('Embedding size is %d, encoder %s.' % (nOut, encoder_type))

        self.inplanes = num_filters[0]
        self.encoder_type = encoder_type
        self.n_mels = n_mels
        self.log_input = log_input

        self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.bn1 = nn.BatchNorm2d(num_filters[0])

        self.layer1 = self._make_layer(block, num_filters[0], layers[0])
        self.layer2 = self._make_layer(block, num_filters[1], layers[1], stride=(2, 2))
        self.layer3 = self._make_layer(block, num_filters[2], layers[2], stride=(2, 2))
        self.layer4 = self._make_layer(block, num_filters[3], layers[3], stride=(2, 2))

        self.instancenorm = nn.InstanceNorm1d(n_mels)

        outmap_size = int(self.n_mels * n_mel_T / 8)

        self.attention = nn.Sequential(
                nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
                nn.ReLU(),
                nn.BatchNorm1d(128),
                nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
                nn.Softmax(dim=2),
        )

        if self.encoder_type == "SAP":
            out_dim = num_filters[3] * outmap_size
        elif self.encoder_type == "ASP":
            out_dim = num_filters[3] * outmap_size * 2
        else:
            raise ValueError('Undefined encoder')

        self.fc = nn.Linear(out_dim, nOut)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion,
                              kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def new_parameter(self, *size):
        out = nn.Parameter(torch.FloatTensor(*size))
        nn.init.xavier_normal_(out)
        return out

    def forward(self, x):

        # with torch.no_grad():
        #     x = self.torchfb(x) + 1e-6
        #     if self.log_input: x = x.log()
        #     x = self.instancenorm(x).unsqueeze(1)

        x = self.conv1(x)
        x = self.relu(x)
        x = self.bn1(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = x.reshape(x.size()[0], -1, x.size()[-1])

        w = self.attention(x)

        if self.encoder_type == "SAP":
            x = torch.sum(x * w, dim=2)
        elif self.encoder_type == "ASP":
            mu = torch.sum(x * w, dim=2)
            sg = torch.sqrt((torch.sum((x ** 2) * w, dim=2) - mu ** 2).clamp(min=1e-5))
            x = torch.cat((mu, sg), 1)

        x = x.view(x.size()[0], -1)
        x = self.fc(x)

        return x




class SEBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
        super(SEBasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.se = SELayer(planes, reduction)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.relu(out)
        out = self.bn1(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)
        return out


class SEBottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
        super(SEBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se = SELayer(planes * 4, reduction)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)
        out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class SELayer(nn.Module):
    def __init__(self, channel, reduction=8):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
                nn.Linear(channel, channel // reduction),
                nn.ReLU(inplace=True),
                nn.Linear(channel // reduction, channel),
                nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y