import functools

import torch
import torch.nn as nn


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 ActNorm(nn.Module):
    def __init__(self, num_features, logdet=False, affine=True, allow_reverse_init=False):
        assert affine
        super().__init__()
        self.logdet = logdet
        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.allow_reverse_init = allow_reverse_init

        self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))

    def initialize(self, input):
        with torch.no_grad():
            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
            mean = flatten.mean(1).unsqueeze(1).unsqueeze(2).unsqueeze(3).permute(1, 0, 2, 3)
            std = flatten.std(1).unsqueeze(1).unsqueeze(2).unsqueeze(3).permute(1, 0, 2, 3)

            self.loc.data.copy_(-mean)
            self.scale.data.copy_(1 / (std + 1e-6))

    def forward(self, input, reverse=False):
        if reverse:
            return self.reverse(input)
        if len(input.shape) == 2:
            input = input[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        _, _, height, width = input.shape

        if self.training and self.initialized.item() == 0:
            self.initialize(input)
            self.initialized.fill_(1)

        h = self.scale * (input + self.loc)

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)

        if self.logdet:
            log_abs = torch.log(torch.abs(self.scale))
            logdet = height * width * torch.sum(log_abs)
            logdet = logdet * torch.ones(input.shape[0]).to(input)
            return h, logdet

        return h

    def reverse(self, output):
        if self.training and self.initialized.item() == 0:
            if not self.allow_reverse_init:
                raise RuntimeError(
                    "Initializing ActNorm in reverse direction is "
                    "disabled by default. Use allow_reverse_init=True to enable."
                )
            else:
                self.initialize(output)
                self.initialized.fill_(1)

        if len(output.shape) == 2:
            output = output[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        h = output / self.scale - self.loc

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)
        return h


class NLayerDiscriminator(nn.Module):
    """Defines a PatchGAN discriminator as in Pix2Pix."""
    # https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
        """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
        """
        super(NLayerDiscriminator, self).__init__()
        if not use_actnorm:
            norm_layer = nn.BatchNorm2d
        else:
            norm_layer = ActNorm
        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
            use_bias = norm_layer.func != nn.BatchNorm2d
        else:
            use_bias = norm_layer != nn.BatchNorm2d

        kw = 4
        padw = 1
        sequence = [nn.Conv2d(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.Conv2d(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.Conv2d(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.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
        ]  # output 1 channel prediction map
        self.main = nn.Sequential(*sequence)

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


class NLayerDiscriminator3D(nn.Module):
    """Defines a 3D PatchGAN discriminator as in Pix2Pix but for 3D inputs."""
    # https://github.com/PKU-YuanGroup/Open-Sora-Plan/blob/main/opensora/models/causalvideovae/model/losses/discriminator.py
    def __init__(self, input_nc=1, ndf=64, n_layers=3, use_actnorm=False):
        """
        Construct a 3D PatchGAN discriminator

        Parameters:
            input_nc (int)  -- the number of channels in input volumes
            ndf (int)       -- the number of filters in the last conv layer
            n_layers (int)  -- the number of conv layers in the discriminator
            use_actnorm (bool) -- flag to use actnorm instead of batchnorm
        """
        super(NLayerDiscriminator3D, self).__init__()
        if not use_actnorm:
            norm_layer = nn.BatchNorm3d
        else:
            raise NotImplementedError("Not implemented.")
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func != nn.BatchNorm3d
        else:
            use_bias = norm_layer != nn.BatchNorm3d

        kw = 3
        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, kw, kw),
                    stride=(2 if n == 1 else 1, 2, 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, kw, 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)

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