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


class FusedLeakyReLU(nn.Module):
    def __init__(self, channel, bias=True, negative_slope=0.2, scale=2**0.5):
        super().__init__()

        if bias:
            self.bias = nn.Parameter(torch.zeros(channel))

        else:
            self.bias = None

        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)


def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2**0.5):
    if input.dtype == torch.float16:
        bias = bias.half()

    if bias is not None:
        rest_dim = [1] * (input.ndim - bias.ndim - 1)
        return F.leaky_relu(
            input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
        ) * scale

    else:
        return F.leaky_relu(input, negative_slope=0.2) * scale


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    up_x, up_y = up, up
    down_x, down_y = down, down
    pad_x0, pad_x1, pad_y0, pad_y1 = pad[0], pad[1], pad[0], pad[1]

    _, channel, in_h, in_w = input.shape
    input = input.reshape(-1, in_h, in_w, 1)

    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape

    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)

    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
    out = out[:,
              max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0),
              max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]

    out = out.permute(0, 3, 1, 2)
    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    out = out.permute(0, 2, 3, 1)
    out = out[:, ::down_y, ::down_x, :]

    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1

    return out.view(-1, channel, out_h, out_w)