import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Conv2d, Module, ReLU
from torch.nn.modules.utils import _pair

__all__ = ['SplAtConv2d', 'DropBlock2D']


class DropBlock2D(object):
    def __init__(self, *args, **kwargs):
        raise NotImplementedError


class SplAtConv2d(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 = 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 = ReLU(inplace=True)
        self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
        if self.use_bn:
            self.bn1 = norm_layer(inter_channels)
        self.fc2 = 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
