import torch
import torch.nn as nn
from torchvision.ops import deform_conv2d


class DeformableConv2d(nn.Module):
    def __init__(
        self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False
    ):

        super(DeformableConv2d, self).__init__()

        assert type(kernel_size) == tuple or type(kernel_size) == int

        kernel_size = (
            kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
        )
        self.stride = stride if type(stride) == tuple else (stride, stride)
        self.padding = padding

        self.offset_conv = nn.Conv2d(
            in_channels,
            2 * kernel_size[0] * kernel_size[1],
            kernel_size=kernel_size,
            stride=stride,
            padding=self.padding,
            bias=True,
        )

        nn.init.constant_(self.offset_conv.weight, 0.0)
        nn.init.constant_(self.offset_conv.bias, 0.0)

        self.modulator_conv = nn.Conv2d(
            in_channels,
            1 * kernel_size[0] * kernel_size[1],
            kernel_size=kernel_size,
            stride=stride,
            padding=self.padding,
            bias=True,
        )

        nn.init.constant_(self.modulator_conv.weight, 0.0)
        nn.init.constant_(self.modulator_conv.bias, 0.0)

        self.regular_conv = nn.Conv2d(
            in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=self.padding,
            bias=bias,
        )

    def forward(self, x):
        # h, w = x.shape[2:]
        # max_offset = max(h, w)/4.

        offset = self.offset_conv(x)  # .clamp(-max_offset, max_offset)
        modulator = 2.0 * torch.sigmoid(self.modulator_conv(x))

        x = deform_conv2d(
            input=x,
            offset=offset,
            weight=self.regular_conv.weight,
            bias=self.regular_conv.bias,
            padding=self.padding,
            mask=modulator,
            stride=self.stride,
        )
        return x