import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair

__all__ = ['ResNest', 'resnest50', 'resnest101', 'resnest200', 'resnest269']


class SplAtConv2d(nn.Module):
    """Split-Attention Conv2d
    """

    def __init__(self,
                 in_channels,
                 channels,
                 kernel_size,
                 stride=(1, 1),
                 padding=(0, 0),
                 dilation=(1, 1),
                 groups=1,
                 bias=True,
                 radix=2,
                 reduction_factor=4,
                 rectify=False,
                 rectify_avg=False,
                 norm_layer=None,
                 dropblock_prob=0.0,
                 **kwargs):
        super(SplAtConv2d, self).__init__()
        padding = _pair(padding)
        self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
        self.rectify_avg = rectify_avg
        inter_channels = max(in_channels * radix // reduction_factor, 32)
        self.radix = radix
        self.cardinality = groups
        self.channels = channels
        self.dropblock_prob = dropblock_prob
        if self.rectify:
            from rfconv import RFConv2d
            self.conv = RFConv2d(in_channels,
                                 channels * radix,
                                 kernel_size,
                                 stride,
                                 padding,
                                 dilation,
                                 groups=groups * radix,
                                 bias=bias,
                                 average_mode=rectify_avg,
                                 **kwargs)
        else:
            self.conv = nn.Conv2d(in_channels,
                                  channels * radix,
                                  kernel_size,
                                  stride,
                                  padding,
                                  dilation,
                                  groups=groups * radix,
                                  bias=bias,
                                  **kwargs)
        self.use_bn = norm_layer is not None
        if self.use_bn:
            self.bn0 = norm_layer(channels * radix)
        self.relu = nn.ReLU(inplace=True)
        self.fc1 = nn.Conv2d(channels,
                             inter_channels,
                             1,
                             groups=self.cardinality)
        if self.use_bn:
            self.bn1 = norm_layer(inter_channels)
        self.fc2 = nn.Conv2d(inter_channels,
                             channels * radix,
                             1,
                             groups=self.cardinality)
        if dropblock_prob > 0.0:
            self.dropblock = DropBlock2D(dropblock_prob, 3)
        self.rsoftmax = rSoftMax(radix, groups)

    def forward(self, x):
        x = self.conv(x)
        if self.use_bn:
            x = self.bn0(x)
        if self.dropblock_prob > 0.0:
            x = self.dropblock(x)
        x = self.relu(x)

        batch, rchannel = x.shape[:2]
        if self.radix > 1:
            if torch.__version__ < '1.5':
                splited = torch.split(x, int(rchannel // self.radix), dim=1)
            else:
                splited = torch.split(x, rchannel // self.radix, dim=1)
            gap = sum(splited)
        else:
            gap = x
        gap = F.adaptive_avg_pool2d(gap, 1)
        gap = self.fc1(gap)

        if self.use_bn:
            gap = self.bn1(gap)
        gap = self.relu(gap)

        atten = self.fc2(gap)
        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)

        if self.radix > 1:
            if torch.__version__ < '1.5':
                attens = torch.split(atten, int(rchannel // self.radix), dim=1)
            else:
                attens = torch.split(atten, rchannel // self.radix, dim=1)
            out = sum([att * split for (att, split) in zip(attens, splited)])
        else:
            out = atten * x
        return out.contiguous()


class rSoftMax(nn.Module):

    def __init__(self, radix, cardinality):
        super().__init__()
        self.radix = radix
        self.cardinality = cardinality

    def forward(self, x):
        batch = x.size(0)
        if self.radix > 1:
            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
            x = F.softmax(x, dim=1)
            x = x.reshape(batch, -1)
        else:
            x = torch.sigmoid(x)
        return x


class DropBlock2D(object):

    def __init__(self, *args, **kwargs):
        raise NotImplementedError


class GlobalAvgPool2d(nn.Module):

    def __init__(self):
        """Global average pooling over the input's spatial dimensions"""
        super(GlobalAvgPool2d, self).__init__()

    def forward(self, inputs):
        return nn.functional.adaptive_avg_pool2d(inputs,
                                                 1).view(inputs.size(0), -1)


class Bottleneck(nn.Module):
    """ResNet Bottleneck
    """
    # pylint: disable=unused-argument
    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 downsample=None,
                 radix=1,
                 cardinality=1,
                 bottleneck_width=64,
                 avd=False,
                 avd_first=False,
                 dilation=1,
                 is_first=False,
                 rectified_conv=False,
                 rectify_avg=False,
                 norm_layer=None,
                 dropblock_prob=0.0,
                 last_gamma=False):
        super(Bottleneck, self).__init__()
        group_width = int(planes * (bottleneck_width / 64.)) * cardinality
        self.conv1 = nn.Conv2d(inplanes,
                               group_width,
                               kernel_size=1,
                               bias=False)
        self.bn1 = norm_layer(group_width)
        self.dropblock_prob = dropblock_prob
        self.radix = radix
        self.avd = avd and (stride > 1 or is_first)
        self.avd_first = avd_first

        if self.avd:
            self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
            stride = 1

        if dropblock_prob > 0.0:
            self.dropblock1 = DropBlock2D(dropblock_prob, 3)
            if radix == 1:
                self.dropblock2 = DropBlock2D(dropblock_prob, 3)
            self.dropblock3 = DropBlock2D(dropblock_prob, 3)

        if radix >= 1:
            self.conv2 = SplAtConv2d(group_width,
                                     group_width,
                                     kernel_size=3,
                                     stride=stride,
                                     padding=dilation,
                                     dilation=dilation,
                                     groups=cardinality,
                                     bias=False,
                                     radix=radix,
                                     rectify=rectified_conv,
                                     rectify_avg=rectify_avg,
                                     norm_layer=norm_layer,
                                     dropblock_prob=dropblock_prob)
        elif rectified_conv:
            from rfconv import RFConv2d
            self.conv2 = RFConv2d(group_width,
                                  group_width,
                                  kernel_size=3,
                                  stride=stride,
                                  padding=dilation,
                                  dilation=dilation,
                                  groups=cardinality,
                                  bias=False,
                                  average_mode=rectify_avg)
            self.bn2 = norm_layer(group_width)
        else:
            self.conv2 = nn.Conv2d(group_width,
                                   group_width,
                                   kernel_size=3,
                                   stride=stride,
                                   padding=dilation,
                                   dilation=dilation,
                                   groups=cardinality,
                                   bias=False)
            self.bn2 = norm_layer(group_width)

        self.conv3 = nn.Conv2d(group_width,
                               planes * 4,
                               kernel_size=1,
                               bias=False)
        self.bn3 = norm_layer(planes * 4)

        if last_gamma:
            from torch.nn.init import zeros_
            zeros_(self.bn3.weight)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.dilation = dilation
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        if self.dropblock_prob > 0.0:
            out = self.dropblock1(out)
        out = self.relu(out)

        if self.avd and self.avd_first:
            out = self.avd_layer(out)

        out = self.conv2(out)
        if self.radix == 0:
            out = self.bn2(out)
            if self.dropblock_prob > 0.0:
                out = self.dropblock2(out)
            out = self.relu(out)

        if self.avd and not self.avd_first:
            out = self.avd_layer(out)

        out = self.conv3(out)
        out = self.bn3(out)
        if self.dropblock_prob > 0.0:
            out = self.dropblock3(out)

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

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

        return out


class ResNest(nn.Module):

    def __init__(self,
                 block,
                 layers,
                 radix=1,
                 groups=1,
                 bottleneck_width=64,
                 num_classes=1000,
                 dilated=False,
                 dilation=1,
                 deep_stem=False,
                 stem_width=64,
                 avg_down=False,
                 rectified_conv=False,
                 rectify_avg=False,
                 avd=False,
                 avd_first=False,
                 final_drop=0.0,
                 dropblock_prob=0,
                 last_gamma=False,
                 norm_layer=nn.BatchNorm2d):
        self.cardinality = groups
        self.bottleneck_width = bottleneck_width
        # ResNet-D params
        self.inplanes = stem_width * 2 if deep_stem else 64
        self.avg_down = avg_down
        self.last_gamma = last_gamma
        # ResNeSt params
        self.radix = radix
        self.avd = avd
        self.avd_first = avd_first

        super(ResNest, self).__init__()
        self.rectified_conv = rectified_conv
        self.rectify_avg = rectify_avg
        if rectified_conv:
            from rfconv import RFConv2d
            conv_layer = RFConv2d
        else:
            conv_layer = nn.Conv2d
        conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
        if deep_stem:
            self.conv1 = nn.Sequential(
                conv_layer(3,
                           stem_width,
                           kernel_size=3,
                           stride=2,
                           padding=1,
                           bias=False,
                           **conv_kwargs),
                norm_layer(stem_width),
                nn.ReLU(inplace=True),
                conv_layer(stem_width,
                           stem_width,
                           kernel_size=3,
                           stride=1,
                           padding=1,
                           bias=False,
                           **conv_kwargs),
                norm_layer(stem_width),
                nn.ReLU(inplace=True),
                conv_layer(stem_width,
                           stem_width * 2,
                           kernel_size=3,
                           stride=1,
                           padding=1,
                           bias=False,
                           **conv_kwargs),
            )
        else:
            self.conv1 = conv_layer(3,
                                    64,
                                    kernel_size=7,
                                    stride=2,
                                    padding=3,
                                    bias=False,
                                    **conv_kwargs)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block,
                                       64,
                                       layers[0],
                                       norm_layer=norm_layer,
                                       is_first=False)
        self.layer2 = self._make_layer(block,
                                       128,
                                       layers[1],
                                       stride=2,
                                       norm_layer=norm_layer)
        if dilated or dilation == 4:
            self.layer3 = self._make_layer(block,
                                           256,
                                           layers[2],
                                           stride=1,
                                           dilation=2,
                                           norm_layer=norm_layer,
                                           dropblock_prob=dropblock_prob)
            self.layer4 = self._make_layer(block,
                                           512,
                                           layers[3],
                                           stride=1,
                                           dilation=4,
                                           norm_layer=norm_layer,
                                           dropblock_prob=dropblock_prob)
        elif dilation == 2:
            self.layer3 = self._make_layer(block,
                                           256,
                                           layers[2],
                                           stride=2,
                                           dilation=1,
                                           norm_layer=norm_layer,
                                           dropblock_prob=dropblock_prob)
            self.layer4 = self._make_layer(block,
                                           512,
                                           layers[3],
                                           stride=1,
                                           dilation=2,
                                           norm_layer=norm_layer,
                                           dropblock_prob=dropblock_prob)
        else:
            self.layer3 = self._make_layer(block,
                                           256,
                                           layers[2],
                                           stride=2,
                                           norm_layer=norm_layer,
                                           dropblock_prob=dropblock_prob)
            self.layer4 = self._make_layer(block,
                                           512,
                                           layers[3],
                                           stride=2,
                                           norm_layer=norm_layer,
                                           dropblock_prob=dropblock_prob)
        self.avgpool = GlobalAvgPool2d()
        self.drop = nn.Dropout(final_drop) if final_drop > 0.0 else None
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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, norm_layer):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self,
                    block,
                    planes,
                    blocks,
                    stride=1,
                    dilation=1,
                    norm_layer=None,
                    dropblock_prob=0.0,
                    is_first=True):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            down_layers = []
            if self.avg_down:
                if dilation == 1:
                    down_layers.append(
                        nn.AvgPool2d(kernel_size=stride,
                                     stride=stride,
                                     ceil_mode=True,
                                     count_include_pad=False))
                else:
                    down_layers.append(
                        nn.AvgPool2d(kernel_size=1,
                                     stride=1,
                                     ceil_mode=True,
                                     count_include_pad=False))
                down_layers.append(
                    nn.Conv2d(self.inplanes,
                              planes * block.expansion,
                              kernel_size=1,
                              stride=1,
                              bias=False))
            else:
                down_layers.append(
                    nn.Conv2d(self.inplanes,
                              planes * block.expansion,
                              kernel_size=1,
                              stride=stride,
                              bias=False))
            down_layers.append(norm_layer(planes * block.expansion))
            downsample = nn.Sequential(*down_layers)

        layers = []
        if dilation == 1 or dilation == 2:
            layers.append(
                block(self.inplanes,
                      planes,
                      stride,
                      downsample=downsample,
                      radix=self.radix,
                      cardinality=self.cardinality,
                      bottleneck_width=self.bottleneck_width,
                      avd=self.avd,
                      avd_first=self.avd_first,
                      dilation=1,
                      is_first=is_first,
                      rectified_conv=self.rectified_conv,
                      rectify_avg=self.rectify_avg,
                      norm_layer=norm_layer,
                      dropblock_prob=dropblock_prob,
                      last_gamma=self.last_gamma))
        elif dilation == 4:
            layers.append(
                block(self.inplanes,
                      planes,
                      stride,
                      downsample=downsample,
                      radix=self.radix,
                      cardinality=self.cardinality,
                      bottleneck_width=self.bottleneck_width,
                      avd=self.avd,
                      avd_first=self.avd_first,
                      dilation=2,
                      is_first=is_first,
                      rectified_conv=self.rectified_conv,
                      rectify_avg=self.rectify_avg,
                      norm_layer=norm_layer,
                      dropblock_prob=dropblock_prob,
                      last_gamma=self.last_gamma))
        else:
            raise RuntimeError("=> unknown dilation size: {}".format(dilation))

        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(
                block(self.inplanes,
                      planes,
                      radix=self.radix,
                      cardinality=self.cardinality,
                      bottleneck_width=self.bottleneck_width,
                      avd=self.avd,
                      avd_first=self.avd_first,
                      dilation=dilation,
                      rectified_conv=self.rectified_conv,
                      rectify_avg=self.rectify_avg,
                      norm_layer=norm_layer,
                      dropblock_prob=dropblock_prob,
                      last_gamma=self.last_gamma))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

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

        x = self.avgpool(x)
        # x = x.view(x.size(0), -1)
        x = torch.flatten(x, 1)
        if self.drop:
            x = self.drop(x)
        x = self.fc(x)

        return x


def resnest50(pretrained=False, **kwargs):
    model = ResNest(Bottleneck, [3, 4, 6, 3],
                    radix=2,
                    groups=1,
                    bottleneck_width=64,
                    deep_stem=True,
                    stem_width=32,
                    avg_down=True,
                    avd=True,
                    avd_first=False,
                    **kwargs)
    return model


def resnest101(pretrained=False, **kwargs):
    model = ResNest(Bottleneck, [3, 4, 23, 3],
                    radix=2,
                    groups=1,
                    bottleneck_width=64,
                    deep_stem=True,
                    stem_width=64,
                    avg_down=True,
                    avd=True,
                    avd_first=False,
                    **kwargs)
    return model


def resnest200(pretrained=False, **kwargs):
    model = ResNest(Bottleneck, [3, 24, 36, 3],
                    radix=2,
                    groups=1,
                    bottleneck_width=64,
                    deep_stem=True,
                    stem_width=64,
                    avg_down=True,
                    avd=True,
                    avd_first=False,
                    **kwargs)
    return model


def resnest269(pretrained=False, **kwargs):
    model = ResNest(Bottleneck, [3, 30, 48, 8],
                    radix=2,
                    groups=1,
                    bottleneck_width=64,
                    deep_stem=True,
                    stem_width=64,
                    avg_down=True,
                    avd=True,
                    avd_first=False,
                    **kwargs)
    return model
