# This implementation is adopted from MiDaS
# made publicly available under the MIT license
# https://github.com/isl-org/MiDaS
import torch
import torch.nn as nn

from .vit import (_make_pretrained_vitb16_384, _make_pretrained_vitb_rn50_384,
                  _make_pretrained_vitl16_384, forward_vit)


def _make_encoder(
    backbone,
    features,
    use_pretrained,
    groups=1,
    expand=False,
    exportable=True,
    hooks=None,
    use_vit_only=False,
    use_readout='ignore',
):
    if backbone == 'vitl16_384':
        pretrained = _make_pretrained_vitl16_384(
            use_pretrained, hooks=hooks, use_readout=use_readout)
        scratch = _make_scratch(
            [256, 512, 1024, 1024], features, groups=groups,
            expand=expand)  # ViT-L/16 - 85.0% Top1 (backbone)
    elif backbone == 'vitb_rn50_384':
        pretrained = _make_pretrained_vitb_rn50_384(
            use_pretrained,
            hooks=hooks,
            use_vit_only=use_vit_only,
            use_readout=use_readout,
        )
        scratch = _make_scratch(
            [256, 512, 768, 768], features, groups=groups,
            expand=expand)  # ViT-H/16 - 85.0% Top1 (backbone)
    elif backbone == 'vitb16_384':
        pretrained = _make_pretrained_vitb16_384(
            use_pretrained, hooks=hooks, use_readout=use_readout)
        scratch = _make_scratch(
            [96, 192, 384, 768], features, groups=groups,
            expand=expand)  # ViT-B/16 - 84.6% Top1 (backbone)
    elif backbone == 'resnext101_wsl':
        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
        scratch = _make_scratch([256, 512, 1024, 2048],
                                features,
                                groups=groups,
                                expand=expand)  # efficientnet_lite3
    elif backbone == 'efficientnet_lite3':
        pretrained = _make_pretrained_efficientnet_lite3(
            use_pretrained, exportable=exportable)
        scratch = _make_scratch([32, 48, 136, 384],
                                features,
                                groups=groups,
                                expand=expand)  # efficientnet_lite3
    else:
        print(f"Backbone '{backbone}' not implemented")
        assert False

    return pretrained, scratch


def _make_scratch(in_shape, out_shape, groups=1, expand=False):
    scratch = nn.Module()

    out_shape1 = out_shape
    out_shape2 = out_shape
    out_shape3 = out_shape
    out_shape4 = out_shape
    if expand is True:
        out_shape1 = out_shape
        out_shape2 = out_shape * 2
        out_shape3 = out_shape * 4
        out_shape4 = out_shape * 8

    scratch.layer1_rn = nn.Conv2d(
        in_shape[0],
        out_shape1,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups)
    scratch.layer2_rn = nn.Conv2d(
        in_shape[1],
        out_shape2,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups)
    scratch.layer3_rn = nn.Conv2d(
        in_shape[2],
        out_shape3,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups)
    scratch.layer4_rn = nn.Conv2d(
        in_shape[3],
        out_shape4,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups)

    return scratch


def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
    efficientnet = torch.hub.load(
        'rwightman/gen-efficientnet-pytorch',
        'tf_efficientnet_lite3',
        pretrained=use_pretrained,
        exportable=exportable)
    return _make_efficientnet_backbone(efficientnet)


def _make_efficientnet_backbone(effnet):
    pretrained = nn.Module()

    pretrained.layer1 = nn.Sequential(effnet.conv_stem, effnet.bn1,
                                      effnet.act1, *effnet.blocks[0:2])
    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])

    return pretrained


def _make_resnet_backbone(resnet):
    pretrained = nn.Module()
    pretrained.layer1 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
                                      resnet.maxpool, resnet.layer1)

    pretrained.layer2 = resnet.layer2
    pretrained.layer3 = resnet.layer3
    pretrained.layer4 = resnet.layer4

    return pretrained


def _make_pretrained_resnext101_wsl(use_pretrained):
    resnet = torch.hub.load('facebookresearch/WSL-Images',
                            'resnext101_32x8d_wsl')
    return _make_resnet_backbone(resnet)


class Interpolate(nn.Module):
    """Interpolation module.
    """

    def __init__(self, scale_factor, mode, align_corners=False):
        """Init.

        Args:
            scale_factor (float): scaling
            mode (str): interpolation mode
        """
        super(Interpolate, self).__init__()

        self.interp = nn.functional.interpolate
        self.scale_factor = scale_factor
        self.mode = mode
        self.align_corners = align_corners

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input

        Returns:
            tensor: interpolated data
        """

        x = self.interp(
            x,
            scale_factor=self.scale_factor,
            mode=self.mode,
            align_corners=self.align_corners)

        return x


class ResidualConvUnit(nn.Module):
    """Residual convolution module.
    """

    def __init__(self, features):
        """Init.

        Args:
            features (int): number of features
        """
        super().__init__()

        self.conv1 = nn.Conv2d(
            features, features, kernel_size=3, stride=1, padding=1, bias=True)

        self.conv2 = nn.Conv2d(
            features, features, kernel_size=3, stride=1, padding=1, bias=True)

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input

        Returns:
            tensor: output
        """
        out = self.relu(x)
        out = self.conv1(out)
        out = self.relu(out)
        out = self.conv2(out)

        return out + x


class FeatureFusionBlock(nn.Module):
    """Feature fusion block.
    """

    def __init__(self, features):
        """Init.

        Args:
            features (int): number of features
        """
        super(FeatureFusionBlock, self).__init__()

        self.resConfUnit1 = ResidualConvUnit(features)
        self.resConfUnit2 = ResidualConvUnit(features)

    def forward(self, *xs):
        """Forward pass.

        Returns:
            tensor: output
        """
        output = xs[0]

        if len(xs) == 2:
            output += self.resConfUnit1(xs[1])

        output = self.resConfUnit2(output)

        output = nn.functional.interpolate(
            output, scale_factor=2, mode='bilinear', align_corners=True)

        return output


class ResidualConvUnit_custom(nn.Module):
    """Residual convolution module.
    """

    def __init__(self, features, activation, bn):
        """Init.

        Args:
            features (int): number of features
        """
        super().__init__()

        self.bn = bn

        self.groups = 1

        self.conv1 = nn.Conv2d(
            features,
            features,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=True,
            groups=self.groups)

        self.conv2 = nn.Conv2d(
            features,
            features,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=True,
            groups=self.groups)

        if self.bn is True:
            self.bn1 = nn.BatchNorm2d(features)
            self.bn2 = nn.BatchNorm2d(features)

        self.activation = activation

        self.skip_add = nn.quantized.FloatFunctional()

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input

        Returns:
            tensor: output
        """

        out = self.activation(x)
        out = self.conv1(out)
        if self.bn is True:
            out = self.bn1(out)

        out = self.activation(out)
        out = self.conv2(out)
        if self.bn is True:
            out = self.bn2(out)

        if self.groups > 1:
            out = self.conv_merge(out)

        return self.skip_add.add(out, x)

        # return out + x


class FeatureFusionBlock_custom(nn.Module):
    """Feature fusion block.
    """

    def __init__(self,
                 features,
                 activation,
                 deconv=False,
                 bn=False,
                 expand=False,
                 align_corners=True):
        """Init.

        Args:
            features (int): number of features
        """
        super(FeatureFusionBlock_custom, self).__init__()

        self.deconv = deconv
        self.align_corners = align_corners

        self.groups = 1

        self.expand = expand
        out_features = features
        if self.expand is True:
            out_features = features // 2

        self.out_conv = nn.Conv2d(
            features,
            out_features,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=True,
            groups=1)

        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)

        self.skip_add = nn.quantized.FloatFunctional()

    def forward(self, *xs):
        """Forward pass.

        Returns:
            tensor: output
        """
        output = xs[0]

        if len(xs) == 2:
            res = self.resConfUnit1(xs[1])
            output = self.skip_add.add(output, res)
            # output += res

        output = self.resConfUnit2(output)

        output = nn.functional.interpolate(
            output,
            scale_factor=2,
            mode='bilinear',
            align_corners=self.align_corners)

        output = self.out_conv(output)

        return output