import functools

import torch.nn as nn

# Isolated baseline implementation provided by @warvito and should be used as minimum performance
# for any further improvement. This implementation is 3D only. It was extracted from his codebase
# at commit https://github.com/Warvito/Dan_Data/blob/97ccba41d8ee1e811b66e79254fcce6bf3a8d0f9/
# Based on following files
# - Network - https://github.com/Warvito/Dan_Data/blob/97ccba41d8ee1e811b66e79254fcce6bf3a8d0f9/models/discriminator.py


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm") != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)


class BaselineDiscriminator(nn.Module):
    def __init__(self, input_nc=1, ndf=64, n_layers=3):
        """Construct a PatchGAN discriminator
        Parameters:
            input_nc (int)  -- the number of channels in input images
            ndf (int)       -- the number of filters in the last conv layer
            n_layers (int)  -- the number of conv layers in the discriminator
            norm_layer      -- normalization layer
        """
        super(BaselineDiscriminator, self).__init__()
        norm_layer = nn.BatchNorm3d
        if (
            type(norm_layer) == functools.partial
        ):  # no need to use bias as BatchNorm2d has affine parameters
            use_bias = norm_layer.func != nn.BatchNorm3d
        else:
            use_bias = norm_layer != nn.BatchNorm3d

        kw = 4
        padw = 1
        sequence = [
            nn.Conv3d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
            nn.LeakyReLU(0.2, True),
        ]
        nf_mult = 1
        nf_mult_prev = 1
        for n in range(1, n_layers):  # gradually increase the number of filters
            nf_mult_prev = nf_mult
            nf_mult = min(2 ** n, 8)
            sequence += [
                nn.Conv3d(
                    ndf * nf_mult_prev,
                    ndf * nf_mult,
                    kernel_size=kw,
                    stride=2,
                    padding=padw,
                    bias=use_bias,
                ),
                norm_layer(ndf * nf_mult),
                nn.LeakyReLU(0.2, True),
            ]

        nf_mult_prev = nf_mult
        nf_mult = min(2 ** n_layers, 8)
        sequence += [
            nn.Conv3d(
                ndf * nf_mult_prev,
                ndf * nf_mult,
                kernel_size=kw,
                stride=1,
                padding=padw,
                bias=use_bias,
            ),
            norm_layer(ndf * nf_mult),
            nn.LeakyReLU(0.2, True),
        ]

        sequence += [
            nn.Conv3d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
        ]  # output 1 channel prediction map

        self.main = nn.Sequential(*sequence)

        self.apply(weights_init)

    def forward(self, input):
        """Standard forward."""
        return self.main(input)
