import torch
import torch.nn as nn

from .lseg_vit import (
    _make_pretrained_clip_vitl16_384,
    _make_pretrained_clip_vitb32_384,
    _make_pretrained_clipRN50x16_vitl16_384,
    forward_vit,
)


def _make_encoder(
    backbone,
    features,
    use_pretrained=True,
    groups=1,
    expand=False,
    exportable=True,
    hooks=None,
    use_vit_only=False,
    use_readout="ignore",
    enable_attention_hooks=False,
):  
    if backbone == "clip_vitl16_384": 
        clip_pretrained, pretrained = _make_pretrained_clip_vitl16_384(
            use_pretrained,
            hooks=hooks,
            use_readout=use_readout,
            enable_attention_hooks=enable_attention_hooks,
        )
        scratch = _make_scratch(
            [256, 512, 1024, 1024], features, groups=groups, expand=expand
        ) 
    elif backbone == "clipRN50x16_vitl16_384":
        clip_pretrained, pretrained = _make_pretrained_clipRN50x16_vitl16_384(
            use_pretrained,
            hooks=hooks,
            use_readout=use_readout,
            enable_attention_hooks=enable_attention_hooks,
        )
        scratch = _make_scratch(
            [256, 512, 1024, 1024], features, groups=groups, expand=expand
        )
    elif backbone == "clip_vitb32_384":
        clip_pretrained, pretrained = _make_pretrained_clip_vitb32_384(
            use_pretrained, 
            hooks=hooks, 
            use_readout=use_readout,
        )
        scratch = _make_scratch(
            [96, 192, 384, 768], features, groups=groups, expand=expand
        ) 
    else:
        print(f"Backbone '{backbone}' not implemented")
        assert False

    return clip_pretrained, 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 == 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


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=not self.bn,
            groups=self.groups,
        )

        self.conv2 = nn.Conv2d(
            features,
            features,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=not self.bn,
            groups=self.groups,
        )

        if self.bn == 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 == True:
            out = self.bn1(out)

        out = self.activation(out)
        out = self.conv2(out)
        if self.bn == 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 == 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

