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


class Flatten(torch.nn.Module):
    """
    [[Source]](https://github.com/learnables/learn2learn/blob/master/learn2learn/nn/misc.py)

    **Description**

    Utility Module to flatten inputs to `(batch_size, -1)` shape.

    **Example**
    ~~~python
    flatten = Flatten()
    x = torch.randn(5, 3, 32, 32)
    x = flatten(x)
    print(x.shape)  # (5, 3072)
    ~~~
    """

    def forward(self, x):
        return x.view(x.size(0), -1)


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        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


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

        self.block_size = block_size
        #self.gamma = gamma
        #self.bernouli = Bernoulli(gamma)

    def forward(self, x, gamma):
        # shape: (bsize, channels, height, width)

        if self.training:
            batch_size, channels, height, width = x.shape
            
            bernoulli = Bernoulli(gamma)
            mask = bernoulli.sample((batch_size, channels, height - (self.block_size - 1), width - (self.block_size - 1))).cuda()
            block_mask = self._compute_block_mask(mask)
            countM = block_mask.size()[0] * block_mask.size()[1] * block_mask.size()[2] * block_mask.size()[3]
            count_ones = block_mask.sum()

            return block_mask * x * (countM / count_ones)
        else:
            return x

    def _compute_block_mask(self, mask):
        left_padding = int((self.block_size-1) / 2)
        right_padding = int(self.block_size / 2)
        
        batch_size, channels, height, width = mask.shape
        #print ("mask", mask[0][0])
        non_zero_idxs = torch.nonzero(mask)
        nr_blocks = non_zero_idxs.shape[0]

        offsets = torch.stack(
            [
                torch.arange(self.block_size).view(-1, 1).expand(self.block_size, self.block_size).reshape(-1), # - left_padding,
                torch.arange(self.block_size).repeat(self.block_size), #- left_padding
            ]
        ).t().cuda()
        offsets = torch.cat((torch.zeros(self.block_size**2, 2).cuda().long(), offsets.long()), 1)
        
        if nr_blocks > 0:
            non_zero_idxs = non_zero_idxs.repeat(self.block_size ** 2, 1)
            offsets = offsets.repeat(nr_blocks, 1).view(-1, 4)
            offsets = offsets.long()

            block_idxs = non_zero_idxs + offsets
            #block_idxs += left_padding
            padded_mask = F.pad(mask, (left_padding, right_padding, left_padding, right_padding))
            padded_mask[block_idxs[:, 0], block_idxs[:, 1], block_idxs[:, 2], block_idxs[:, 3]] = 1.
        else:
            padded_mask = F.pad(mask, (left_padding, right_padding, left_padding, right_padding))
            
        block_mask = 1 - padded_mask#[:height, :width]
        return block_mask
    

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0, drop_block=False,
                 block_size=1, use_se=False):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.LeakyReLU(0.1)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv3x3(planes, planes)
        self.bn3 = nn.BatchNorm2d(planes)
        self.maxpool = nn.MaxPool2d(stride)
        self.downsample = downsample
        self.stride = stride
        self.drop_rate = drop_rate
        self.num_batches_tracked = 0
        self.drop_block = drop_block
        self.block_size = block_size
        self.DropBlock = DropBlock(block_size=self.block_size)
        self.use_se = use_se
        if self.use_se:
            self.se = SELayer(planes, 4)

    def forward(self, x):
        self.num_batches_tracked += 1

        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)
        if self.use_se:
            out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        out = self.maxpool(out)

        if self.drop_rate > 0:
            if self.drop_block == True:
                feat_size = out.size()[2]
                keep_rate = max(1.0 - self.drop_rate / (20*2000) * (self.num_batches_tracked), 1.0 - self.drop_rate)
                gamma = (1 - keep_rate) / self.block_size**2 * feat_size**2 / (feat_size - self.block_size + 1)**2
                out = self.DropBlock(out, gamma=gamma)
            else:
                out = F.dropout(out, p=self.drop_rate, training=self.training, inplace=True)

        return out


class ResNet(nn.Module):

    def __init__(self, block, n_blocks, keep_prob=1.0, avg_pool=False, drop_rate=0.0,
                 dropblock_size=5, num_classes=-1, use_se=False):
        super(ResNet, self).__init__()

        self.inplanes = 3
        self.use_se = use_se
        self.layer1 = self._make_layer(block, n_blocks[0], 64,
                                       stride=2, drop_rate=drop_rate)
        self.layer2 = self._make_layer(block, n_blocks[1], 160,
                                       stride=2, drop_rate=drop_rate)
        self.layer3 = self._make_layer(block, n_blocks[2], 320,
                                       stride=2, drop_rate=drop_rate, drop_block=True, block_size=dropblock_size)
        self.layer4 = self._make_layer(block, n_blocks[3], 640,
                                       stride=2, drop_rate=drop_rate, drop_block=True, block_size=dropblock_size)
        if avg_pool:
            # self.avgpool = nn.AvgPool2d(5, stride=1)
            self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.keep_prob = keep_prob
        self.keep_avg_pool = avg_pool
        self.dropout = nn.Dropout(p=1 - self.keep_prob, inplace=False)
        self.drop_rate = drop_rate
        
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        self.features = nn.Sequential(
            self.layer1,
            self.layer2,
            self.layer3,
            self.layer4,
            self.avgpool,
            Flatten()
        )
        self.num_classes = num_classes
        if self.num_classes > 0:
            self.classifier = nn.Linear(640, self.num_classes)

    def _make_layer(self, block, n_block, planes, stride=1, drop_rate=0.0, drop_block=False, block_size=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=1, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        if n_block == 1:
            layer = block(self.inplanes, planes, stride, downsample, drop_rate, drop_block, block_size, self.use_se)
        else:
            layer = block(self.inplanes, planes, stride, downsample, drop_rate, self.use_se)
        layers.append(layer)
        self.inplanes = planes * block.expansion

        for i in range(1, n_block):
            if i == n_block - 1:
                layer = block(self.inplanes, planes, drop_rate=drop_rate, drop_block=drop_block,
                              block_size=block_size, use_se=self.use_se)
            else:
                layer = block(self.inplanes, planes, drop_rate=drop_rate, use_se=self.use_se)
            layers.append(layer)

        return nn.Sequential(*layers)

    def forward(self, x, is_feat=False):
        # x = self.layer1(x)
        # f0 = x
        # x = self.layer2(x)
        # f1 = x
        # x = self.layer3(x)
        # f2 = x
        # x = self.layer4(x)
        # f3 = x
        # if self.keep_avg_pool:
        #     x = self.avgpool(x)
        # x = x.view(x.size(0), -1)
        # feat = x
        x = self.features(x)
        feat = x
        if self.num_classes > 0:
            x = self.classifier(x)

        if is_feat:
            return [feat], x
        else:
            return x


def resnet12(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-12 model.
    """
    model = ResNet(BasicBlock, [1, 1, 1, 1], keep_prob=keep_prob, avg_pool=avg_pool, **kwargs)
    return model


def resnet18(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-18 model.
    """
    model = ResNet(BasicBlock, [1, 1, 2, 2], keep_prob=keep_prob, avg_pool=avg_pool, **kwargs)
    return model


def resnet24(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-24 model.
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], keep_prob=keep_prob, avg_pool=avg_pool, **kwargs)
    return model


def resnet50(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-50 model.
    indeed, only (3 + 4 + 6 + 3) * 3 + 1 = 49 layers
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], keep_prob=keep_prob, avg_pool=avg_pool, **kwargs)
    return model


def resnet101(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-101 model.
    indeed, only (3 + 4 + 23 + 3) * 3 + 1 = 100 layers
    """
    model = ResNet(BasicBlock, [3, 4, 23, 3], keep_prob=keep_prob, avg_pool=avg_pool, **kwargs)
    return model


def seresnet12(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-12 model.
    """
    model = ResNet(BasicBlock, [1, 1, 1, 1], keep_prob=keep_prob, avg_pool=avg_pool, use_se=True, **kwargs)
    return model


def seresnet18(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-18 model.
    """
    model = ResNet(BasicBlock, [1, 1, 2, 2], keep_prob=keep_prob, avg_pool=avg_pool, use_se=True, **kwargs)
    return model


def seresnet24(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-24 model.
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], keep_prob=keep_prob, avg_pool=avg_pool, use_se=True, **kwargs)
    return model


def seresnet50(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-50 model.
    indeed, only (3 + 4 + 6 + 3) * 3 + 1 = 49 layers
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], keep_prob=keep_prob, avg_pool=avg_pool, use_se=True, **kwargs)
    return model


def seresnet101(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-101 model.
    indeed, only (3 + 4 + 23 + 3) * 3 + 1 = 100 layers
    """
    model = ResNet(BasicBlock, [3, 4, 23, 3], keep_prob=keep_prob, avg_pool=avg_pool, use_se=True, **kwargs)
    return model


if __name__ == '__main__':

    import argparse

    parser = argparse.ArgumentParser('argument for training')
    parser.add_argument('--model', type=str, choices=['resnet12', 'resnet18', 'resnet24', 'resnet50', 'resnet101',
                                                      'seresnet12', 'seresnet18', 'seresnet24', 'seresnet50',
                                                      'seresnet101'])
    args = parser.parse_args()

    model_dict = {
        'resnet12': resnet12,
        'resnet18': resnet18,
        'resnet24': resnet24,
        'resnet50': resnet50,
        'resnet101': resnet101,
        'seresnet12': seresnet12,
        'seresnet18': seresnet18,
        'seresnet24': seresnet24,
        'seresnet50': seresnet50,
        'seresnet101': seresnet101,
    }

    model = model_dict[args.model](avg_pool=True, drop_rate=0.1, dropblock_size=5, num_classes=64)
    data = torch.randn(2, 3, 84, 84)
    model = model.cuda()
    data = data.cuda()
    feat, logit = model(data, is_feat=True)
    print(feat[-1].shape)
    print(logit.shape)
