from __future__ import absolute_import
import torch

from torch import nn
from torch.nn import functional as F

__all__ = [
    "FPNBottleneckBlock",
    "FPNBottleneckBlockBN",
    "FPNPredictionBlock",
    "FPNFuse",
    "FPNFuseSum",
    "UpsampleAdd",
    "UpsampleAddConv",
]


class FPNBottleneckBlock(nn.Module):
    def __init__(self, input_channels, output_channels):
        super().__init__()
        self.conv = nn.Conv2d(input_channels, output_channels, kernel_size=1)

    def forward(self, x):
        x = self.conv(x)
        return x


class FPNBottleneckBlockBN(nn.Module):
    def __init__(self, input_channels, output_channels):
        super().__init__()
        self.conv = nn.Conv2d(
            input_channels, output_channels, kernel_size=1, bias=False
        )
        self.bn = nn.BatchNorm2d(output_channels)

    def forward(self, x):
        x = self.conv(x)
        return x


class FPNPredictionBlock(nn.Module):
    def __init__(self, input_channels, output_channels, mode="nearest"):
        super().__init__()
        self.input_channels = input_channels
        self.output_channels = output_channels
        self.conv = nn.Conv2d(
            self.input_channels, self.output_channels, kernel_size=3, padding=1
        )
        self.mode = mode

    def forward(self, x, y=None):
        if y is not None:
            x = x + F.interpolate(
                y,
                size=x.size()[2:],
                mode=self.mode,
                align_corners=False if self.mode == "bilinear" else None,
            )

        x = self.conv(x)
        return x


class UpsampleAdd(nn.Module):
    """
    Compute pixelwise sum of first tensor and upsampled second tensor.
    """

    def __init__(
        self, filters: int, upsample_scale=None, mode="nearest", align_corners=None
    ):
        super().__init__()
        self.interpolation_mode = mode
        self.upsample_scale = upsample_scale
        self.align_corners = align_corners

    def forward(self, x, y=None):
        if y is not None:
            if self.upsample_scale is not None:
                y = F.interpolate(
                    y,
                    scale_factor=self.upsample_scale,
                    mode=self.interpolation_mode,
                    align_corners=self.align_corners,
                )
            else:
                y = F.interpolate(
                    y,
                    size=(x.size(2), x.size(3)),
                    mode=self.interpolation_mode,
                    align_corners=self.align_corners,
                )

            x = x + y

        return x


class UpsampleAddConv(nn.Module):
    """
    Compute pixelwise sum of first tensor and upsampled second tensor and convolve with 3x3 kernel
    to smooth aliasing artifacts
    """

    def __init__(
        self, filters: int, upsample_scale=None, mode="nearest", align_corners=None
    ):
        super().__init__()
        self.interpolation_mode = mode
        self.upsample_scale = upsample_scale
        self.align_corners = align_corners
        self.conv = nn.Conv2d(filters, filters, kernel_size=3, padding=1)

    def forward(self, x, y=None):
        if y is not None:
            if self.upsample_scale is not None:
                y = F.interpolate(
                    y,
                    scale_factor=self.upsample_scale,
                    mode=self.interpolation_mode,
                    align_corners=self.align_corners,
                )
            else:
                y = F.interpolate(
                    y,
                    size=(x.size(2), x.size(3)),
                    mode=self.interpolation_mode,
                    align_corners=self.align_corners,
                )

            x = x + y

        x = self.conv(x)
        return x


class FPNFuse(nn.Module):
    def __init__(self, mode="bilinear", align_corners=False):
        super().__init__()
        self.mode = mode
        self.align_corners = align_corners

    def forward(self, features):
        layers = []
        dst_size = features[0].size()[-2:]

        for f in features:
            layers.append(
                F.interpolate(
                    f, size=dst_size, mode=self.mode, align_corners=self.align_corners
                )
            )

        return torch.cat(layers, dim=1)


class FPNFuseSum(nn.Module):
    """Compute a sum of individual FPN layers"""

    def __init__(self, mode="bilinear", align_corners=False):
        super().__init__()
        self.mode = mode
        self.align_corners = align_corners

    def forward(self, features):
        output = features[0]
        dst_size = features[0].size()[-2:]

        for f in features[1:]:
            output = output + F.interpolate(
                f, size=dst_size, mode=self.mode, align_corners=self.align_corners
            )

        return output


class HFF(nn.Module):
    """
    Hierarchical feature fusion

    https://arxiv.org/pdf/1811.11431.pdf
    https://arxiv.org/pdf/1803.06815.pdf
    """

    def __init__(
        self, sizes=None, upsample_scale=2, mode="nearest", align_corners=None
    ):
        super().__init__()
        self.sizes = sizes
        self.interpolation_mode = mode
        self.align_corners = align_corners
        self.upsample_scale = upsample_scale

    def forward(self, features):
        num_feature_maps = len(features)

        current_map = features[-1]
        for feature_map_index in reversed(range(num_feature_maps - 1)):
            if self.sizes is not None:
                prev_upsampled = self._upsample(
                    current_map, self.sizes[feature_map_index]
                )
            else:
                prev_upsampled = self._upsample(current_map)

            current_map = features[feature_map_index] + prev_upsampled

        return current_map

    def _upsample(self, x, output_size=None):
        if output_size is not None:
            x = F.interpolate(
                x,
                size=(output_size[0], output_size[1]),
                mode=self.interpolation_mode,
                align_corners=self.align_corners,
            )
        else:
            x = F.interpolate(
                x,
                scale_factor=self.upsample_scale,
                mode=self.interpolation_mode,
                align_corners=self.align_corners,
            )
        return x
