import torch
import torch.nn as nn
import numpy as np
from .model_factory import register_model


__all__ = ['C2D_ResNet', 'c2d_resnet18', 'c2d_resnet34', 'c2d_resnet50', 'c2d_resnet101', 'c2d_resnet103',
           'c2d_resnet152', 'c2d_resnext50_32x4d', 'c2d_resnext101_32x8d',
           'c2d_wide_resnet50_2', 'c2d_wide_resnet101_2']


def conv1x3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """1x3x3 convolution with padding"""
    return nn.Conv3d(in_planes, out_planes, kernel_size=[1, 3, 3], stride=[1, stride, stride],
                     padding=[0, dilation, dilation], groups=groups, bias=False, dilation=[1, dilation, dilation])


def conv3x1x1(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x1x1 convolution with padding"""
    return nn.Conv3d(in_planes, out_planes, kernel_size=[3, 1, 1], stride=[stride, 1, 1],
                     padding=[dilation, 0, 0], groups=groups, bias=False, dilation=[dilation, 1, 1])


def conv1x1x1(in_planes, out_planes, stride=1):
    """1x1x1 convolution"""
    return nn.Conv3d(in_planes, out_planes, kernel_size=1, stride=[1, stride, stride], bias=False)


class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm3d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv1x3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

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

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

        return out


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm3d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv1x3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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.downsample is not None:
            identity = self.downsample(x)

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

        return out


class C2D_ResNet(nn.Module):

    def __init__(self, block, layers, pooling_arch, num_classes=400, dropout_ratio=0.5, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None, deep_stem=False, clip_length=4):
        super(C2D_ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm3d
        self._norm_layer = norm_layer
        self._deep_stem = deep_stem

        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
        if not deep_stem:
            self.conv1 = nn.Conv3d(3, self.inplanes, kernel_size=[4, 7, 7], stride=[4, 2, 2], padding=[0, 3, 3],
                                    bias=False)
            self.bn1 = norm_layer(self.inplanes)
            self.relu = nn.ReLU(inplace=True)
        else:
            self.conv1 = nn.Conv3d(3, self.inplanes, kernel_size=[4, 3, 3], stride=[4, 2, 2], padding=[0, 1, 1],
                                    bias=False)
            self.bn1 = norm_layer(self.inplanes)
            self.relu1 = nn.ReLU(inplace=True)
            self.conv2 = nn.Conv3d(self.inplanes, self.inplanes, kernel_size=[1, 3, 3], stride=[1, 1, 1], padding=[0, 1, 1],
                                    bias=False)
            self.bn2 = norm_layer(self.inplanes)
            self.relu2 = nn.ReLU(inplace=True)
            self.conv3 = nn.Conv3d(self.inplanes, self.inplanes * 2, kernel_size=[1, 3, 3], stride=[1, 1, 1], padding=[0, 1, 1],
                                    bias=False)
            self.bn3 = norm_layer(self.inplanes * 2)
            self.relu3 = nn.ReLU(inplace=True)
            
            self.inplanes *= 2
        self.maxpool = nn.MaxPool3d(kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1])
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.pool = pooling_arch(input_dim=512 * block.expansion)

        self.drop = nn.Dropout(dropout_ratio)

        self.fc = nn.Linear(self.pool.output_dim, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm3d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, 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(
                conv1x1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _forward_impl(self, x, layer):
        # See note [TorchScript super()]
        if not self._deep_stem:
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
        else:
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu1(x)
            x = self.conv2(x)
            x = self.bn2(x)
            x = self.relu2(x)
            x = self.conv3(x)
            x = self.bn3(x)
            x = self.relu3(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        if layer == 5:
            return x
        x_g = self.pool(x)
        if layer == 6:
            return x_g

        x_g = self.drop(x_g)

        x1 = self.fc(x_g)
        return x1

    def forward(self, x, layer=7):
        return self._forward_impl(x, layer)


def transfer_weights(state_dict, early_stride):
    new_state_dict = {}
    for k, v in state_dict.items():
        v = v.detach().numpy()
        if ('conv' in k) or ('downsample.0' in k):
            shape = v.shape
            v = np.reshape(v, newshape=[shape[0], shape[1], 1, shape[2], shape[3]])
            if (not ('layer' in k)) and ('conv1' in k):  # first conv7x7 layer
                if early_stride != 1:
                    s1 = early_stride // 2
                    s2 = early_stride - early_stride // 2 - 1
                    v = np.concatenate((np.zeros(shape=(shape[0], shape[1], s1, shape[2], shape[3])), 
                    v, np.zeros(shape=(shape[0], shape[1], s2, shape[2], shape[3]))), axis=2)
        new_state_dict[k] = torch.from_numpy(v)
    return new_state_dict


def _c2d_resnet(arch, block, layers, pooling_arch, **kwargs):
    model = C2D_ResNet(block, layers, pooling_arch, **kwargs)
    return model


@register_model
def c2d_resnet18(pooling_arch, **kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pooling_arch: choose the pooling architecture for last conv/res layer
    """
    return _c2d_resnet('resnet18', BasicBlock, [2, 2, 2, 2], pooling_arch,
                   **kwargs)


@register_model
def c2d_resnet34(pooling_arch, **kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pooling_arch: choose the pooling architecture for last conv/res layer
    """
    return _c2d_resnet('resnet34', BasicBlock, [3, 4, 6, 3], pooling_arch,
                   **kwargs)


@register_model
def c2d_resnet50(pooling_arch, **kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pooling_arch: choose the pooling architecture for last conv/res layer
    """
    return _c2d_resnet('resnet50', Bottleneck, [3, 4, 6, 3], pooling_arch,
                   **kwargs)


@register_model
def c2d_resnet101(pooling_arch, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pooling_arch: choose the pooling architecture for last conv/res layer
    """
    return _c2d_resnet('resnet101', Bottleneck, [3, 4, 23, 3], pooling_arch,
                   **kwargs)


@register_model
def c2d_resnet103(pooling_arch, **kwargs):
    r"""ResNet-103 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pooling_arch: choose the pooling architecture for last conv/res layer
    """
    return _c2d_resnet('resnet103', Bottleneck, [3, 4, 23, 3], pooling_arch, deep_stem=True,
                   **kwargs)
                   

@register_model
def c2d_resnet152(pooling_arch, **kwargs):
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pooling_arch: choose the pooling architecture for last conv/res layer
    """
    return _c2d_resnet('resnet152', Bottleneck, [3, 8, 36, 3], pooling_arch,
                   **kwargs)


@register_model
def c2d_resnext50_32x4d(pooling_arch, **kwargs):
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return _c2d_resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
                   pooling_arch, **kwargs)


@register_model
def c2d_resnext101_32x8d(pooling_arch, **kwargs):
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _c2d_resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                   pooling_arch, **kwargs)


@register_model
def c2d_wide_resnet50_2(pooling_arch, **kwargs):
    kwargs['width_per_group'] = 64 * 2
    return _c2d_resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
                   pooling_arch, **kwargs)


@register_model
def c2d_wide_resnet101_2(pooling_arch, **kwargs):
    kwargs['width_per_group'] = 64 * 2
    return _c2d_resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                   pooling_arch, **kwargs)
