import torch
import torch.nn as nn
# from .utils import load_state_dict_from_url
import numpy as np
from ..cnsn import CrossNorm, SelfNorm, CNSN


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
           'wide_resnet50_2', 'wide_resnet101_2']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


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


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BottleneckCustom(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion = 4

    def __init__(self, inplanes, planes, pos, beta, crop, cnsn_type,
                 stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BottleneckCustom, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        if cnsn_type is not None:  # for CrossNorm in image space, cnsn_type is None
            assert cnsn_type in ['sn', 'cn', 'cnsn']

            if 'cn' in cnsn_type:
                print('using CrossNorm with crop: {}'.format(crop))
                crossnorm = CrossNorm(crop=crop, beta=beta)
            else:
                crossnorm = None

            if 'sn' in cnsn_type:
                print('using SelfNorm')
                if pos == 'pre':
                    selfnorm = SelfNorm(inplanes)
                else:
                    selfnorm = SelfNorm(planes * self.expansion)
            else:
                selfnorm = None

            self.cnsn = CNSN(crossnorm=crossnorm, selfnorm=selfnorm)

        self.pos = pos
        if pos is not None:  # for CrossNorm in image space, pos is None
            print('{} in residual module: {}'.format(cnsn_type, pos))
            assert pos in ['residual', 'pre', 'post', 'identity']

    def forward(self, x):

        identity = x

        out = x

        if self.pos == 'pre':
            out = self.cnsn(out)

        out = self.conv1(out)
        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.downsample is not None:
            identity = self.downsample(x)

        if self.pos == 'residual':
            out = self.cnsn(out)
        elif self.pos == 'identity':
            identity = self.cnsn(identity)

        out += identity

        if self.pos == 'post':
            out = self.cnsn(out)

        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 active_num=1, pos=None, beta=None, crop=None, cnsn_type=None):
        super(ResNet, self).__init__()

        norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        if beta is not None:
            print('beta: {}'.format(beta))

        if crop is not None:
            print('crop mode: {}'.format(crop))

        self.layer1 = self._make_layer_custom(BottleneckCustom, 64, layers[0],
                                              pos=pos, beta=beta,
                                              crop=crop, cnsn_type=cnsn_type)

        self.layer2 = self._make_layer_custom(BottleneckCustom, 128, layers[1],
                                              pos=pos, beta=beta,
                                              crop=crop, cnsn_type=cnsn_type,
                                              stride=2, dilate=replace_stride_with_dilation[0])

        self.layer3 = self._make_layer_custom(BottleneckCustom, 256, layers[2],
                                              pos=pos, beta=beta,
                                              crop=crop, cnsn_type=cnsn_type,
                                              stride=2, dilate=replace_stride_with_dilation[1])

        self.layer4 = self._make_layer_custom(BottleneckCustom, 512, layers[3],
                                              pos=pos, beta=beta,
                                              crop=crop, cnsn_type=cnsn_type,
                                              stride=2, dilate=replace_stride_with_dilation[2])


        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * BottleneckCustom.expansion, num_classes)

        self.cn_modules = []
        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.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, CrossNorm):
                self.cn_modules.append(m)

        if cnsn_type is not None and 'cn' in cnsn_type:
            self.cn_num = len(self.cn_modules)
            assert self.cn_num > 0
            print('cn_num: {}'.format(self.cn_num))
            self.active_num = active_num
            assert self.active_num > 0
            print('active_num: {}'.format(self.active_num))

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, BottleneckCustom):
                    nn.init.constant_(m.bn3.weight, 0)
                # elif isinstance(m, BasicBlock):
                #     nn.init.constant_(m.bn2.weight, 0)

    def _make_layer_custom(self, block, planes, blocks, pos, beta,
                           crop, cnsn_type, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, pos=pos, beta=beta,
                            crop=crop, cnsn_type=cnsn_type,
                            stride=stride, downsample=downsample,
                            groups=self.groups, base_width=self.base_width,
                            dilation=previous_dilation,
                            norm_layer=norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, pos=pos, beta=beta,
                                crop=crop, cnsn_type=cnsn_type,
                                groups=self.groups, base_width=self.base_width,
                                dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _enable_cross_norm(self):
        active_cn_idxs = np.random.choice(self.cn_num, self.active_num, replace=False).tolist()
        assert len(set(active_cn_idxs)) == self.active_num
        # print('active_cn_idxs: {}'.format(active_cn_idxs))
        for idx in active_cn_idxs:
            self.cn_modules[idx].active = True

    def forward(self, x, aug=False):
        # See note [TorchScript super()]
        if aug:
            # print('forward cross norm...')
            # exit()
            self._enable_cross_norm()

        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 = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    # def forward(self, x, aug=False):
    #     return self._forward_impl(x, aug)


# def _resnet(arch, block, layers, pretrained, progress, **kwargs):
#     model = ResNet(block, layers, **kwargs)
#     if pretrained:
#         state_dict = load_state_dict_from_url(model_urls[arch],
#                                               progress=progress)
#         model.load_state_dict(state_dict)
#     return model


def resnet18(pretrained=False, progress=True, **kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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 _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)


def resnet34(pretrained=False, progress=True, **kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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 _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet50(config):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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 _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
    #                **kwargs)
    model = ResNet([3, 4, 6, 3],
                   active_num=config.active_num, pos=config.pos,
                   beta=config.beta, crop=config.crop,
                   cnsn_type=config.cnsn_type)
    return model


def resnet101(pretrained=False, progress=True, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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 _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                   **kwargs)


def resnet152(pretrained=False, progress=True, **kwargs):
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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 _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
                   **kwargs)


def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)


def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)


def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
    r"""Wide ResNet-50-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_

    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    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
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)


def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
    r"""Wide ResNet-101-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_

    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    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
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)