import torch
import torch.nn as nn
from torch.utils.model_zoo import load_url as load_state_dict_from_url
from network.instance_whitening import InstanceWhitening
import network.mynn as mynn


__all__ = [
    'ShuffleNetV2', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0',
    'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0'
]

model_urls = {
    'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
    'shufflenetv2_x1.0': 'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
    'shufflenetv2_x1.5': None,
    'shufflenetv2_x2.0': None,
}


def channel_shuffle(x, groups):
    # type: (torch.Tensor, int) -> torch.Tensor
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups,
               channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, iw=0):
        super(InvertedResidual, self).__init__()

        if not (1 <= stride <= 3):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = oup // 2
        assert (self.stride != 1) or (inp == branch_features << 1)

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(inp),
                nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True),
            )
        else:
            self.branch1 = nn.Sequential()

        self.branch2 = nn.Sequential(
            nn.Conv2d(inp if (self.stride > 1) else branch_features,
                      branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_features),
            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
        )
        self.iw = iw
        if iw == 1:
            self.instance_norm_layer = InstanceWhitening(oup)
        elif iw == 2:
            self.instance_norm_layer = InstanceWhitening(oup)
        elif iw == 3:
            self.instance_norm_layer = nn.InstanceNorm2d(oup, affine=False)
        elif iw == 4:
            self.instance_norm_layer = nn.InstanceNorm2d(oup, affine=True)
        else:
            self.instance_norm_layer = nn.Sequential()


    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)

    def forward(self, x_tuple):
        if len(x_tuple) == 2:
            w_arr = x_tuple[1]
            x = x_tuple[0]
        else:
            print("error in invert residual forward path")
            return

        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

        out = channel_shuffle(out, 2)

        if self.iw >= 1:
            if self.iw == 1 or self.iw == 2:
                out, w, c = self.instance_norm_layer(out)
                w_arr.append(w)
            else:
                out = self.instance_norm_layer(out)
        return [out, w_arr]


class ShuffleNetV2(nn.Module):
    def __init__(self, stages_repeats, stages_out_channels, num_classes=1000, inverted_residual=InvertedResidual,
                 iw=[0, 0, 0, 0, 0, 0, 0]):
        super(ShuffleNetV2, self).__init__()

        if len(stages_repeats) != 3:
            raise ValueError('expected stages_repeats as list of 3 positive ints')
        if len(stages_out_channels) != 5:
            raise ValueError('expected stages_out_channels as list of 5 positive ints')
        self._stage_out_channels = stages_out_channels

        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True),
        )

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        iw_count = 2

        if iw[iw_count] == 1:
            self.instance_norm_layer1 = InstanceWhitening(output_channels)
        elif iw[iw_count] == 2:
            self.instance_norm_layer1 = InstanceWhitening(output_channels)
        elif iw[iw_count] == 3:
            self.instance_norm_layer1 = nn.InstanceNorm2d(output_channels, affine=False)
        elif iw[iw_count] == 4:
            self.instance_norm_layer1 = nn.InstanceNorm2d(output_channels, affine=True)
        else:
            self.instance_norm_layer1 = nn.Sequential()

        iw_count += 1

        input_channels = output_channels

        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(
                stage_names, stages_repeats, self._stage_out_channels[1:]):
            seq = [inverted_residual(input_channels, output_channels, 2)]
            for i in range(repeats - 1):
                if i == repeats - 2:
                    seq.append(inverted_residual(output_channels, output_channels, 1, iw=iw[iw_count]))
                    iw_count += 1
                else:
                    seq.append(inverted_residual(output_channels, output_channels, 1, iw=0))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels

        output_channels = self._stage_out_channels[-1]

        self.conv5 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True),
        )
        if iw[iw_count] == 1:
            self.instance_norm_layer2 = InstanceWhitening(output_channels)
        elif iw[iw_count] == 2:
            self.instance_norm_layer2 = InstanceWhitening(output_channels)
        elif iw[iw_count] == 3:
            self.instance_norm_layer2 = nn.InstanceNorm2d(output_channels, affine=False)
        elif iw[iw_count] == 4:
            self.instance_norm_layer2 = nn.InstanceNorm2d(output_channels, affine=True)
        else:
            self.instance_norm_layer2 = nn.Sequential()

        self.fc = nn.Linear(output_channels, num_classes)


    def _forward_impl(self, x):
        # See note [TorchScript super()]
        """
        x = self.conv1(x)
        x = self.maxpool(x)
        """
        x = self.layer0(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.layer4(x)
        x = x.mean([2, 3])  # globalpool
        x = self.fc(x)
        return x

    def forward(self, x):
        return self._forward_impl(x)


def _shufflenetv2(arch, pretrained, progress, *args, **kwargs):
    model = ShuffleNetV2(*args, **kwargs)

    if pretrained:
        model_url = model_urls[arch]
        if model_url is None:
            raise NotImplementedError('pretrained {} is not supported as of now'.format(arch))
        else:
            state_dict = load_state_dict_from_url(model_url, progress=progress)
            mynn.forgiving_state_restore(model, state_dict)
            ### model.load_state_dict(state_dict)

    return model


def shufflenet_v2_x0_5(pretrained=False, progress=True, **kwargs):
    """
    Constructs a ShuffleNetV2 with 0.5x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _shufflenetv2('shufflenetv2_x0.5', pretrained, progress,
                         [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)


def shufflenet_v2_x1_0(pretrained=False, progress=True, **kwargs):
    """
    Constructs a ShuffleNetV2 with 1.0x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _shufflenetv2('shufflenetv2_x1.0', pretrained, progress,
                         [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)


def shufflenet_v2_x1_5(pretrained=False, progress=True, **kwargs):
    """
    Constructs a ShuffleNetV2 with 1.5x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _shufflenetv2('shufflenetv2_x1.5', pretrained, progress,
                         [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)


def shufflenet_v2_x2_0(pretrained=False, progress=True, **kwargs):
    """
    Constructs a ShuffleNetV2 with 2.0x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _shufflenetv2('shufflenetv2_x2.0', pretrained, progress,
                         [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)
