# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""Generator architecture from the paper
"Alias-Free Generative Adversarial Networks"."""

import numpy as np
import scipy.signal
import scipy.optimize
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import filtered_lrelu
from torch_utils.ops import bias_act

#----------------------------------------------------------------------------
# from pdb import set_trace as st


@misc.profiled_function
def modulated_conv2d(
    x,  # Input tensor: [batch_size, in_channels, in_height, in_width]
    w,  # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
    s,  # Style tensor: [batch_size, in_channels]
    demodulate=True,  # Apply weight demodulation?
    padding=0,  # Padding: int or [padH, padW]
    input_gain=None,  # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
):
    with misc.suppress_tracer_warnings(
    ):  # this value will be treated as a constant
        batch_size = int(x.shape[0])
    out_channels, in_channels, kh, kw = w.shape
    misc.assert_shape(w, [out_channels, in_channels, kh, kw])  # [OIkk]
    misc.assert_shape(x, [batch_size, in_channels, None, None])  # [NIHW]
    misc.assert_shape(s, [batch_size, in_channels])  # [NI]

    # Pre-normalize inputs.
    if demodulate:
        w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt()
        s = s * s.square().mean().rsqrt()

    # Modulate weights.
    w = w.unsqueeze(0)  # [NOIkk]
    w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4)  # [NOIkk]

    # Demodulate weights.
    if demodulate:
        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
        w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4)  # [NOIkk]

    # Apply input scaling.
    if input_gain is not None:
        input_gain = input_gain.expand(batch_size, in_channels)  # [NI]
        w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4)  # [NOIkk]

    # Execute as one fused op using grouped convolution.
    x = x.reshape(1, -1, *x.shape[2:])
    w = w.reshape(-1, in_channels, kh, kw)
    x = conv2d_gradfix.conv2d(input=x,
                              weight=w.to(x.dtype),
                              padding=padding,
                              groups=batch_size)
    x = x.reshape(batch_size, -1, *x.shape[2:])
    return x


#----------------------------------------------------------------------------


@persistence.persistent_class
class FullyConnectedLayer(torch.nn.Module):
    def __init__(
            self,
            in_features,  # Number of input features.
            out_features,  # Number of output features.
            activation='linear',  # Activation function: 'relu', 'lrelu', etc.
            bias=True,  # Apply additive bias before the activation function?
            lr_multiplier=1,  # Learning rate multiplier.
            weight_init=1,  # Initial standard deviation of the weight tensor.
            bias_init=0,  # Initial value of the additive bias.
    ):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.activation = activation
        self.weight = torch.nn.Parameter(
            torch.randn([out_features, in_features]) *
            (weight_init / lr_multiplier))
        bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32),
                                    [out_features])
        self.bias = torch.nn.Parameter(
            torch.from_numpy(bias_init / lr_multiplier)) if bias else None
        self.weight_gain = lr_multiplier / np.sqrt(in_features)
        self.bias_gain = lr_multiplier

    def forward(self, x):
        w = self.weight.to(x.dtype) * self.weight_gain
        b = self.bias
        if b is not None:
            b = b.to(x.dtype)
            if self.bias_gain != 1:
                b = b * self.bias_gain
        if self.activation == 'linear' and b is not None:
            x = torch.addmm(b.unsqueeze(0), x, w.t())
        else:
            x = x.matmul(w.t())
            x = bias_act.bias_act(x, b, act=self.activation)
        return x

    def extra_repr(self):
        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'


#----------------------------------------------------------------------------


@persistence.persistent_class
class MappingNetwork(torch.nn.Module):
    def __init__(
            self,
            z_dim,  # Input latent (Z) dimensionality.
            c_dim,  # Conditioning label (C) dimensionality, 0 = no labels.
            w_dim,  # Intermediate latent (W) dimensionality.
            num_ws,  # Number of intermediate latents to output.
            num_layers=2,  # Number of mapping layers.
            lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
            w_avg_beta=0.998,  # Decay for tracking the moving average of W during training.
    ):
        super().__init__()
        self.z_dim = z_dim
        self.c_dim = c_dim
        self.w_dim = w_dim
        self.num_ws = num_ws
        self.num_layers = num_layers
        self.w_avg_beta = w_avg_beta

        # Construct layers.
        self.embed = FullyConnectedLayer(
            self.c_dim, self.w_dim) if self.c_dim > 0 else None
        features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)
                    ] + [self.w_dim] * self.num_layers
        for idx, in_features, out_features in zip(range(num_layers),
                                                  features[:-1], features[1:]):
            layer = FullyConnectedLayer(in_features,
                                        out_features,
                                        activation='lrelu',
                                        lr_multiplier=lr_multiplier)
            setattr(self, f'fc{idx}', layer)
        self.register_buffer('w_avg', torch.zeros([w_dim]))

    def forward(self,
                z,
                c,
                truncation_psi=1,
                truncation_cutoff=None,
                update_emas=False):
        misc.assert_shape(z, [None, self.z_dim])
        if truncation_cutoff is None:
            truncation_cutoff = self.num_ws

        # Embed, normalize, and concatenate inputs.
        x = z.to(torch.float32)
        x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
        if self.c_dim > 0:
            misc.assert_shape(c, [None, self.c_dim])
            y = self.embed(c.to(torch.float32))
            y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
            x = torch.cat([x, y], dim=1) if x is not None else y

        # Execute layers.
        for idx in range(self.num_layers):
            x = getattr(self, f'fc{idx}')(x)

        # Update moving average of W.
        if update_emas:
            self.w_avg.copy_(x.detach().mean(dim=0).lerp(
                self.w_avg, self.w_avg_beta))

        # Broadcast and apply truncation.
        x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
        if truncation_psi != 1:
            x[:, :truncation_cutoff] = self.w_avg.lerp(
                x[:, :truncation_cutoff], truncation_psi)
        return x

    def extra_repr(self):
        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'


#----------------------------------------------------------------------------


@persistence.persistent_class
class SynthesisInput(torch.nn.Module):
    def __init__(
            self,
            w_dim,  # Intermediate latent (W) dimensionality.
            channels,  # Number of output channels.
            size,  # Output spatial size: int or [width, height].
            sampling_rate,  # Output sampling rate.
            bandwidth,  # Output bandwidth.
    ):
        super().__init__()
        self.w_dim = w_dim
        self.channels = channels
        self.size = np.broadcast_to(np.asarray(size), [2])
        self.sampling_rate = sampling_rate
        self.bandwidth = bandwidth

        # Draw random frequencies from uniform 2D disc.
        freqs = torch.randn([self.channels, 2])
        radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
        freqs /= radii * radii.square().exp().pow(0.25)
        freqs *= bandwidth
        phases = torch.rand([self.channels]) - 0.5

        # Setup parameters and buffers.
        self.weight = torch.nn.Parameter(
            torch.randn([self.channels, self.channels]))
        self.affine = FullyConnectedLayer(w_dim,
                                          4,
                                          weight_init=0,
                                          bias_init=[1, 0, 0, 0])
        self.register_buffer('transform', torch.eye(
            3, 3))  # User-specified inverse transform wrt. resulting image.
        self.register_buffer('freqs', freqs)
        self.register_buffer('phases', phases)

    def forward(self, w):
        # Introduce batch dimension.
        transforms = self.transform.unsqueeze(0)  # [batch, row, col]
        freqs = self.freqs.unsqueeze(0)  # [batch, channel, xy]
        phases = self.phases.unsqueeze(0)  # [batch, channel]

        # Apply learned transformation.
        t = self.affine(w)  # t = (r_c, r_s, t_x, t_y)
        t = t / t[:, :2].norm(dim=1,
                              keepdim=True)  # t' = (r'_c, r'_s, t'_x, t'_y)
        m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat(
            [w.shape[0], 1, 1])  # Inverse rotation wrt. resulting image.
        m_r[:, 0, 0] = t[:, 0]  # r'_c
        m_r[:, 0, 1] = -t[:, 1]  # r'_s
        m_r[:, 1, 0] = t[:, 1]  # r'_s
        m_r[:, 1, 1] = t[:, 0]  # r'_c
        m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat(
            [w.shape[0], 1, 1])  # Inverse translation wrt. resulting image.
        m_t[:, 0, 2] = -t[:, 2]  # t'_x
        m_t[:, 1, 2] = -t[:, 3]  # t'_y
        transforms = m_r @ m_t @ transforms  # First rotate resulting image, then translate, and finally apply user-specified transform.

        # Transform frequencies.
        phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
        freqs = freqs @ transforms[:, :2, :2]

        # Dampen out-of-band frequencies that may occur due to the user-specified transform.
        amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) /
                      (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)

        # Construct sampling grid.
        theta = torch.eye(2, 3, device=w.device)
        theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
        theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
        grids = torch.nn.functional.affine_grid(
            theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]],
            align_corners=False)

        # Compute Fourier features.
        x = (grids.unsqueeze(3) @ freqs.permute(
            0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(
                3)  # [batch, height, width, channel]
        x = x + phases.unsqueeze(1).unsqueeze(2)
        x = torch.sin(x * (np.pi * 2))
        x = x * amplitudes.unsqueeze(1).unsqueeze(2)

        # Apply trainable mapping.
        weight = self.weight / np.sqrt(self.channels)
        x = x @ weight.t()

        # Ensure correct shape.
        x = x.permute(0, 3, 1, 2)  # [batch, channel, height, width]
        misc.assert_shape(
            x,
            [w.shape[0], self.channels,
             int(self.size[1]),
             int(self.size[0])])
        return x

    def extra_repr(self):
        return '\n'.join([
            f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
            f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'
        ])


#----------------------------------------------------------------------------


@persistence.persistent_class
class SynthesisLayer(torch.nn.Module):
    def __init__(
            self,
            w_dim,  # Intermediate latent (W) dimensionality.
            is_torgb,  # Is this the final ToRGB layer?
            is_critically_sampled,  # Does this layer use critical sampling?
            use_fp16,  # Does this layer use FP16?

            # Input & output specifications.
        in_channels,  # Number of input channels.
            out_channels,  # Number of output channels.
            in_size,  # Input spatial size: int or [width, height].
            out_size,  # Output spatial size: int or [width, height].
            in_sampling_rate,  # Input sampling rate (s).
            out_sampling_rate,  # Output sampling rate (s).
            in_cutoff,  # Input cutoff frequency (f_c).
            out_cutoff,  # Output cutoff frequency (f_c).
            in_half_width,  # Input transition band half-width (f_h).
            out_half_width,  # Output Transition band half-width (f_h).

            # Hyperparameters.
        conv_kernel=3,  # Convolution kernel size. Ignored for final the ToRGB layer.
            filter_size=6,  # Low-pass filter size relative to the lower resolution when up/downsampling.
            lrelu_upsampling=2,  # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
            use_radial_filters=False,  # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
            conv_clamp=256,  # Clamp the output to [-X, +X], None = disable clamping.
            magnitude_ema_beta=0.999,  # Decay rate for the moving average of input magnitudes.
    ):
        super().__init__()
        self.w_dim = w_dim
        self.is_torgb = is_torgb
        self.is_critically_sampled = is_critically_sampled
        self.use_fp16 = use_fp16
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.in_size = np.broadcast_to(np.asarray(in_size), [2])
        self.out_size = np.broadcast_to(np.asarray(out_size), [2])
        self.in_sampling_rate = in_sampling_rate
        self.out_sampling_rate = out_sampling_rate
        self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (
            1 if is_torgb else lrelu_upsampling)
        self.in_cutoff = in_cutoff
        self.out_cutoff = out_cutoff
        self.in_half_width = in_half_width
        self.out_half_width = out_half_width
        self.conv_kernel = 1 if is_torgb else conv_kernel
        self.conv_clamp = conv_clamp
        self.magnitude_ema_beta = magnitude_ema_beta

        # Setup parameters and buffers.
        self.affine = FullyConnectedLayer(self.w_dim,
                                          self.in_channels,
                                          bias_init=1)
        self.weight = torch.nn.Parameter(
            torch.randn([
                self.out_channels, self.in_channels, self.conv_kernel,
                self.conv_kernel
            ]))
        self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
        self.register_buffer('magnitude_ema', torch.ones([]))

        # Design upsampling filter.
        self.up_factor = int(
            np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
        assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
        self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
        self.register_buffer(
            'up_filter',
            self.design_lowpass_filter(numtaps=self.up_taps,
                                       cutoff=self.in_cutoff,
                                       width=self.in_half_width * 2,
                                       fs=self.tmp_sampling_rate))

        # Design downsampling filter.
        self.down_factor = int(
            np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
        assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
        self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
        self.down_radial = use_radial_filters and not self.is_critically_sampled
        self.register_buffer(
            'down_filter',
            self.design_lowpass_filter(numtaps=self.down_taps,
                                       cutoff=self.out_cutoff,
                                       width=self.out_half_width * 2,
                                       fs=self.tmp_sampling_rate,
                                       radial=self.down_radial))

        # Compute padding.
        pad_total = (
            self.out_size - 1
        ) * self.down_factor + 1  # Desired output size before downsampling.
        pad_total -= (self.in_size + self.conv_kernel -
                      1) * self.up_factor  # Input size after upsampling.
        pad_total += self.up_taps + self.down_taps - 2  # Size reduction caused by the filters.
        pad_lo = (
            pad_total + self.up_factor
        ) // 2  # Shift sample locations according to the symmetric interpretation (Appendix C.3).
        pad_hi = pad_total - pad_lo
        self.padding = [
            int(pad_lo[0]),
            int(pad_hi[0]),
            int(pad_lo[1]),
            int(pad_hi[1])
        ]

    def forward(self,
                x,
                w,
                noise_mode='random',
                force_fp32=False,
                update_emas=False):
        assert noise_mode in ['random', 'const', 'none']  # unused
        misc.assert_shape(x, [
            None, self.in_channels,
            int(self.in_size[1]),
            int(self.in_size[0])
        ])
        misc.assert_shape(w, [x.shape[0], self.w_dim])

        # Track input magnitude.
        if update_emas:
            with torch.autograd.profiler.record_function(
                    'update_magnitude_ema'):
                magnitude_cur = x.detach().to(torch.float32).square().mean()
                self.magnitude_ema.copy_(
                    magnitude_cur.lerp(self.magnitude_ema,
                                       self.magnitude_ema_beta))
        input_gain = self.magnitude_ema.rsqrt()

        # Execute affine layer.
        styles = self.affine(w)
        if self.is_torgb:
            weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel**2))
            styles = styles * weight_gain

        # Execute modulated conv2d.
        dtype = torch.float16 if (self.use_fp16 and not force_fp32 and
                                  x.device.type == 'cuda') else torch.float32
        x = modulated_conv2d(x=x.to(dtype),
                             w=self.weight,
                             s=styles,
                             padding=self.conv_kernel - 1,
                             demodulate=(not self.is_torgb),
                             input_gain=input_gain)

        # Execute bias, filtered leaky ReLU, and clamping.
        gain = 1 if self.is_torgb else np.sqrt(2)
        slope = 1 if self.is_torgb else 0.2
        x = filtered_lrelu.filtered_lrelu(x=x,
                                          fu=self.up_filter,
                                          fd=self.down_filter,
                                          b=self.bias.to(x.dtype),
                                          up=self.up_factor,
                                          down=self.down_factor,
                                          padding=self.padding,
                                          gain=gain,
                                          slope=slope,
                                          clamp=self.conv_clamp)

        # Ensure correct shape and dtype.
        misc.assert_shape(x, [
            None, self.out_channels,
            int(self.out_size[1]),
            int(self.out_size[0])
        ])
        assert x.dtype == dtype
        return x

    @staticmethod
    def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
        assert numtaps >= 1

        # Identity filter.
        if numtaps == 1:
            return None

        # Separable Kaiser low-pass filter.
        if not radial:
            f = scipy.signal.firwin(numtaps=numtaps,
                                    cutoff=cutoff,
                                    width=width,
                                    fs=fs)
            return torch.as_tensor(f, dtype=torch.float32)

        # Radially symmetric jinc-based filter.
        x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
        r = np.hypot(*np.meshgrid(x, x))
        f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
        beta = scipy.signal.kaiser_beta(
            scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
        w = np.kaiser(numtaps, beta)
        f *= np.outer(w, w)
        f /= np.sum(f)
        return torch.as_tensor(f, dtype=torch.float32)

    def extra_repr(self):
        return '\n'.join([
            f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
            f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
            f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
            f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
            f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
            f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'
        ])


#----------------------------------------------------------------------------


@persistence.persistent_class
class SynthesisNetwork(torch.nn.Module):
    def __init__(
            self,
            w_dim,  # Intermediate latent (W) dimensionality.
            img_resolution,  # Output image resolution.
            img_channels,  # Number of color channels.
            channel_base=32768,  # Overall multiplier for the number of channels.
            channel_max=512,  # Maximum number of channels in any layer.
            num_layers=14,  # Total number of layers, excluding Fourier features and ToRGB.
            num_critical=2,  # Number of critically sampled layers at the end.
            first_cutoff=2,  # Cutoff frequency of the first layer (f_{c,0}).
            first_stopband=2**
        2.1,  # Minimum stopband of the first layer (f_{t,0}).
            last_stopband_rel=2**
        0.3,  # Minimum stopband of the last layer, expressed relative to the cutoff.
            margin_size=10,  # Number of additional pixels outside the image.
            output_scale=0.25,  # Scale factor for the output image.
            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
            **layer_kwargs,  # Arguments for SynthesisLayer.
    ):
        super().__init__()
        self.w_dim = w_dim
        self.num_ws = num_layers + 2
        self.img_resolution = img_resolution
        self.img_channels = img_channels
        self.num_layers = num_layers
        self.num_critical = num_critical
        self.margin_size = margin_size
        self.output_scale = output_scale
        self.num_fp16_res = num_fp16_res

        # Geometric progression of layer cutoffs and min. stopbands.
        last_cutoff = self.img_resolution / 2  # f_{c,N}
        last_stopband = last_cutoff * last_stopband_rel  # f_{t,N}
        exponents = np.minimum(
            np.arange(self.num_layers + 1) /
            (self.num_layers - self.num_critical), 1)
        cutoffs = first_cutoff * (last_cutoff /
                                  first_cutoff)**exponents  # f_c[i]
        stopbands = first_stopband * (last_stopband /
                                      first_stopband)**exponents  # f_t[i]

        # Compute remaining layer parameters.
        sampling_rates = np.exp2(
            np.ceil(np.log2(np.minimum(stopbands * 2,
                                       self.img_resolution))))  # s[i]
        half_widths = np.maximum(stopbands,
                                 sampling_rates / 2) - cutoffs  # f_h[i]
        sizes = sampling_rates + self.margin_size * 2
        sizes[-2:] = self.img_resolution
        channels = np.rint(
            np.minimum((channel_base / 2) / cutoffs, channel_max))
        channels[-1] = self.img_channels

        # Construct layers.
        self.input = SynthesisInput(w_dim=self.w_dim,
                                    channels=int(channels[0]),
                                    size=int(sizes[0]),
                                    sampling_rate=sampling_rates[0],
                                    bandwidth=cutoffs[0])
        self.layer_names = []
        for idx in range(self.num_layers + 1):
            prev = max(idx - 1, 0)
            is_torgb = (idx == self.num_layers)
            is_critically_sampled = (idx >=
                                     self.num_layers - self.num_critical)
            use_fp16 = (sampling_rates[idx] *
                        (2**self.num_fp16_res) > self.img_resolution)
            layer = SynthesisLayer(w_dim=self.w_dim,
                                   is_torgb=is_torgb,
                                   is_critically_sampled=is_critically_sampled,
                                   use_fp16=use_fp16,
                                   in_channels=int(channels[prev]),
                                   out_channels=int(channels[idx]),
                                   in_size=int(sizes[prev]),
                                   out_size=int(sizes[idx]),
                                   in_sampling_rate=int(sampling_rates[prev]),
                                   out_sampling_rate=int(sampling_rates[idx]),
                                   in_cutoff=cutoffs[prev],
                                   out_cutoff=cutoffs[idx],
                                   in_half_width=half_widths[prev],
                                   out_half_width=half_widths[idx],
                                   **layer_kwargs)
            name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
            setattr(self, name, layer)
            self.layer_names.append(name)

    def forward(self, ws, **layer_kwargs):
        misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
        ws = ws.to(torch.float32).unbind(dim=1)

        # Execute layers.
        x = self.input(ws[0])
        for name, w in zip(self.layer_names, ws[1:]):
            x = getattr(self, name)(x, w, **layer_kwargs)
        if self.output_scale != 1:
            x = x * self.output_scale

        # Ensure correct shape and dtype.
        misc.assert_shape(x, [
            None, self.img_channels, self.img_resolution, self.img_resolution
        ])
        x = x.to(torch.float32)
        return x

    def extra_repr(self):
        return '\n'.join([
            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
            f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
            f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'
        ])


#----------------------------------------------------------------------------


@persistence.persistent_class
class Generator(torch.nn.Module):
    def __init__(
            self,
            z_dim,  # Input latent (Z) dimensionality.
            c_dim,  # Conditioning label (C) dimensionality.
            w_dim,  # Intermediate latent (W) dimensionality.
            img_resolution,  # Output resolution.
            img_channels,  # Number of output color channels.
            mapping_kwargs={},  # Arguments for MappingNetwork.
            **synthesis_kwargs,  # Arguments for SynthesisNetwork.
    ):
        super().__init__()
        self.z_dim = z_dim
        self.c_dim = c_dim
        self.w_dim = w_dim
        self.img_resolution = img_resolution
        self.img_channels = img_channels
        self.synthesis = SynthesisNetwork(w_dim=w_dim,
                                          img_resolution=img_resolution,
                                          img_channels=img_channels,
                                          **synthesis_kwargs)
        self.num_ws = self.synthesis.num_ws
        self.mapping = MappingNetwork(z_dim=z_dim,
                                      c_dim=c_dim,
                                      w_dim=w_dim,
                                      num_ws=self.num_ws,
                                      **mapping_kwargs)

    def forward(self,
                z,
                c,
                truncation_psi=1,
                truncation_cutoff=None,
                update_emas=False,
                **synthesis_kwargs):
        ws = self.mapping(z,
                          c,
                          truncation_psi=truncation_psi,
                          truncation_cutoff=truncation_cutoff,
                          update_emas=update_emas)
        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
        return img


#----------------------------------------------------------------------------