text: # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
  #
  # NVIDIA CORPORATION and its licensors retain all intellectual property
  # and proprietary rights in and to this software, related documentation
  # and any modifications thereto.  Any use, reproduction, disclosure or
  # distribution of this software and related documentation without an express
  # license agreement from NVIDIA CORPORATION 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
  
  #----------------------------------------------------------------------------
  
  @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
  
  #----------------------------------------------------------------------------
  