ghostflow-ml 1.0.0

//! GAN Architectures - DCGAN, StyleGAN, CycleGAN, WGAN, and variants

use ghostflow_core::Tensor;
use crate::deep::layers::{Conv2d, Dense, BatchNorm2d};
use crate::deep::activations::{ReLU, LeakyReLU, Tanh, Sigmoid};

/// DCGAN Generator
pub struct DCGANGenerator {
    fc: Dense,
    deconv1: Conv2d,
    bn1: BatchNorm2d,
    deconv2: Conv2d,
    bn2: BatchNorm2d,
    deconv3: Conv2d,
    bn3: BatchNorm2d,
    deconv4: Conv2d,
    latent_dim: usize,
}

impl DCGANGenerator {
    pub fn new(latent_dim: usize, img_channels: usize) -> Self {
        DCGANGenerator {
            fc: Dense::new(latent_dim, 512 * 4 * 4),
            deconv1: Conv2d::new(512, 256, (4, 4)).stride((2, 2)).padding((1, 1)),
            bn1: BatchNorm2d::new(256),
            deconv2: Conv2d::new(256, 128, (4, 4)).stride((2, 2)).padding((1, 1)),
            bn2: BatchNorm2d::new(128),
            deconv3: Conv2d::new(128, 64, (4, 4)).stride((2, 2)).padding((1, 1)),
            bn3: BatchNorm2d::new(64),
            deconv4: Conv2d::new(64, img_channels, (4, 4)).stride((2, 2)).padding((1, 1)),
            latent_dim,
        }
    }

    pub fn forward(&mut self, z: &Tensor, training: bool) -> Tensor {
        // Project and reshape
        let mut out = self.fc.forward(z, training);
        let batch_size = z.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 512, 4, 4]).unwrap();

        // Deconvolution layers
        out = self.deconv1.forward(&out, training);
        out = self.bn1.forward(&out, training);
        out = ReLU::new().forward(&out);

        out = self.deconv2.forward(&out, training);
        out = self.bn2.forward(&out, training);
        out = ReLU::new().forward(&out);

        out = self.deconv3.forward(&out, training);
        out = self.bn3.forward(&out, training);
        out = ReLU::new().forward(&out);

        out = self.deconv4.forward(&out, training);
        Tanh::new().forward(&out)
    }
}

/// DCGAN Discriminator
pub struct DCGANDiscriminator {
    conv1: Conv2d,
    conv2: Conv2d,
    bn2: BatchNorm2d,
    conv3: Conv2d,
    bn3: BatchNorm2d,
    conv4: Conv2d,
    bn4: BatchNorm2d,
    fc: Dense,
}

impl DCGANDiscriminator {
    pub fn new(img_channels: usize) -> Self {
        DCGANDiscriminator {
            conv1: Conv2d::new(img_channels, 64, (4, 4)).stride((2, 2)).padding((1, 1)),
            conv2: Conv2d::new(64, 128, (4, 4)).stride((2, 2)).padding((1, 1)),
            bn2: BatchNorm2d::new(128),
            conv3: Conv2d::new(128, 256, (4, 4)).stride((2, 2)).padding((1, 1)),
            bn3: BatchNorm2d::new(256),
            conv4: Conv2d::new(256, 512, (4, 4)).stride((2, 2)).padding((1, 1)),
            bn4: BatchNorm2d::new(512),
            fc: Dense::new(512 * 4 * 4, 1),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv2.forward(&out, training);
        out = self.bn2.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv3.forward(&out, training);
        out = self.bn3.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv4.forward(&out, training);
        out = self.bn4.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);

        // Flatten
        let batch_size = out.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 512 * 4 * 4]).unwrap();

        out = self.fc.forward(&out, training);
        Sigmoid::new().forward(&out)
    }
}

/// StyleGAN Mapping Network
pub struct StyleGANMappingNetwork {
    layers: Vec<Dense>,
    latent_dim: usize,
    style_dim: usize,
}

impl StyleGANMappingNetwork {
    pub fn new(latent_dim: usize, style_dim: usize, num_layers: usize) -> Self {
        let mut layers = Vec::new();
        
        // First layer
        layers.push(Dense::new(latent_dim, style_dim));
        
        // Hidden layers
        for _ in 1..num_layers {
            layers.push(Dense::new(style_dim, style_dim));
        }

        StyleGANMappingNetwork {
            layers,
            latent_dim,
            style_dim,
        }
    }

    pub fn forward(&mut self, z: &Tensor, training: bool) -> Tensor {
        let mut out = z.clone();
        
        for layer in &mut self.layers {
            out = layer.forward(&out, training);
            out = LeakyReLU::new(0.2).forward(&out);
        }

        out
    }
}

/// Adaptive Instance Normalization (AdaIN) for StyleGAN
pub struct AdaIN {
    style_scale: Dense,
    style_bias: Dense,
}

impl AdaIN {
    pub fn new(style_dim: usize, num_features: usize) -> Self {
        AdaIN {
            style_scale: Dense::new(style_dim, num_features),
            style_bias: Dense::new(style_dim, num_features),
        }
    }

    pub fn forward(&mut self, x: &Tensor, style: &Tensor, training: bool) -> Tensor {
        // Compute instance statistics
        let (mean, std) = self.compute_instance_stats(x);

        // Normalize
        let normalized = self.normalize(x, &mean, &std);

        // Apply style
        let scale = self.style_scale.forward(style, training);
        let bias = self.style_bias.forward(style, training);

        self.apply_style(&normalized, &scale, &bias)
    }

    fn compute_instance_stats(&self, x: &Tensor) -> (Tensor, Tensor) {
        let dims = x.dims();
        let batch_size = dims[0];
        let channels = dims[1];
        let spatial_size = dims[2] * dims[3];
        let data = x.data_f32();

        let mut means = vec![0.0f32; batch_size * channels];
        let mut stds = vec![0.0f32; batch_size * channels];

        for b in 0..batch_size {
            for c in 0..channels {
                let mut sum = 0.0f32;
                for s in 0..spatial_size {
                    let idx = (b * channels + c) * spatial_size + s;
                    sum += data[idx];
                }
                let mean = sum / spatial_size as f32;
                means[b * channels + c] = mean;

                let mut var_sum = 0.0f32;
                for s in 0..spatial_size {
                    let idx = (b * channels + c) * spatial_size + s;
                    var_sum += (data[idx] - mean).powi(2);
                }
                stds[b * channels + c] = (var_sum / spatial_size as f32).sqrt();
            }
        }

        (
            Tensor::from_slice(&means, &[batch_size, channels]).unwrap(),
            Tensor::from_slice(&stds, &[batch_size, channels]).unwrap(),
        )
    }

    fn normalize(&self, x: &Tensor, mean: &Tensor, std: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch_size = dims[0];
        let channels = dims[1];
        let spatial_size = dims[2] * dims[3];
        let data = x.data_f32();
        let mean_data = mean.data_f32();
        let std_data = std.data_f32();

        let mut result = vec![0.0f32; data.len()];

        for b in 0..batch_size {
            for c in 0..channels {
                let m = mean_data[b * channels + c];
                let s = std_data[b * channels + c].max(1e-8);
                for s_idx in 0..spatial_size {
                    let idx = (b * channels + c) * spatial_size + s_idx;
                    result[idx] = (data[idx] - m) / s;
                }
            }
        }

        Tensor::from_slice(&result, dims).unwrap()
    }

    fn apply_style(&self, x: &Tensor, scale: &Tensor, bias: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch_size = dims[0];
        let channels = dims[1];
        let spatial_size = dims[2] * dims[3];
        let data = x.data_f32();
        let scale_data = scale.data_f32();
        let bias_data = bias.data_f32();

        let mut result = vec![0.0f32; data.len()];

        for b in 0..batch_size {
            for c in 0..channels {
                let s = scale_data[b * channels + c];
                let b_val = bias_data[b * channels + c];
                for s_idx in 0..spatial_size {
                    let idx = (b * channels + c) * spatial_size + s_idx;
                    result[idx] = data[idx] * s + b_val;
                }
            }
        }

        Tensor::from_slice(&result, dims).unwrap()
    }
}

/// StyleGAN Synthesis Block
pub struct StyleGANSynthesisBlock {
    conv1: Conv2d,
    adain1: AdaIN,
    conv2: Conv2d,
    adain2: AdaIN,
}

impl StyleGANSynthesisBlock {
    pub fn new(in_channels: usize, out_channels: usize, style_dim: usize) -> Self {
        StyleGANSynthesisBlock {
            conv1: Conv2d::new(in_channels, out_channels, (3, 3)).padding((1, 1)),
            adain1: AdaIN::new(style_dim, out_channels),
            conv2: Conv2d::new(out_channels, out_channels, (3, 3)).padding((1, 1)),
            adain2: AdaIN::new(style_dim, out_channels),
        }
    }

    pub fn forward(&mut self, x: &Tensor, style: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = self.adain1.forward(&out, style, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv2.forward(&out, training);
        out = self.adain2.forward(&out, style, training);
        LeakyReLU::new(0.2).forward(&out)
    }
}

/// Simplified StyleGAN Generator
pub struct StyleGANGenerator {
    mapping: StyleGANMappingNetwork,
    constant_input: Vec<f32>,
    synthesis_blocks: Vec<StyleGANSynthesisBlock>,
    to_rgb: Conv2d,
}

impl StyleGANGenerator {
    pub fn new(latent_dim: usize, style_dim: usize, img_channels: usize) -> Self {
        use rand::prelude::*;
        let mut rng = thread_rng();
        let constant_input: Vec<f32> = (0..512 * 4 * 4)
            .map(|_| rng.gen::<f32>() * 0.02 - 0.01)
            .collect();

        StyleGANGenerator {
            mapping: StyleGANMappingNetwork::new(latent_dim, style_dim, 8),
            constant_input,
            synthesis_blocks: vec![
                StyleGANSynthesisBlock::new(512, 512, style_dim),
                StyleGANSynthesisBlock::new(512, 256, style_dim),
                StyleGANSynthesisBlock::new(256, 128, style_dim),
                StyleGANSynthesisBlock::new(128, 64, style_dim),
            ],
            to_rgb: Conv2d::new(64, img_channels, (1, 1)),
        }
    }

    pub fn forward(&mut self, z: &Tensor, training: bool) -> Tensor {
        // Map latent to style
        let style = self.mapping.forward(z, training);

        // Start with constant input
        let batch_size = z.dims()[0];
        let mut out = Tensor::from_slice(&self.constant_input, &[1, 512, 4, 4]).unwrap();
        
        // Repeat for batch
        let mut batch_data = Vec::new();
        for _ in 0..batch_size {
            batch_data.extend_from_slice(out.data_f32());
        }
        out = Tensor::from_slice(&batch_data, &[batch_size, 512, 4, 4]).unwrap();

        // Apply synthesis blocks
        for block in &mut self.synthesis_blocks {
            out = block.forward(&out, &style, training);
            // Upsample (simplified - would use proper upsampling)
            out = self.upsample(&out);
        }

        // Convert to RGB
        out = self.to_rgb.forward(&out, training);
        Tanh::new().forward(&out)
    }

    fn upsample(&self, x: &Tensor) -> Tensor {
        // Simplified nearest neighbor upsampling
        let dims = x.dims();
        let batch = dims[0];
        let channels = dims[1];
        let height = dims[2];
        let width = dims[3];
        let data = x.data_f32();

        let new_height = height * 2;
        let new_width = width * 2;
        let mut result = vec![0.0f32; batch * channels * new_height * new_width];

        for b in 0..batch {
            for c in 0..channels {
                for h in 0..height {
                    for w in 0..width {
                        let val = data[((b * channels + c) * height + h) * width + w];
                        for dh in 0..2 {
                            for dw in 0..2 {
                                let new_h = h * 2 + dh;
                                let new_w = w * 2 + dw;
                                let idx = ((b * channels + c) * new_height + new_h) * new_width + new_w;
                                result[idx] = val;
                            }
                        }
                    }
                }
            }
        }

        Tensor::from_slice(&result, &[batch, channels, new_height, new_width]).unwrap()
    }
}

/// WGAN-GP Critic (Discriminator with Gradient Penalty)
pub struct WGANCritic {
    conv1: Conv2d,
    conv2: Conv2d,
    conv3: Conv2d,
    conv4: Conv2d,
    fc: Dense,
}

impl WGANCritic {
    pub fn new(img_channels: usize) -> Self {
        WGANCritic {
            conv1: Conv2d::new(img_channels, 64, (4, 4)).stride((2, 2)).padding((1, 1)),
            conv2: Conv2d::new(64, 128, (4, 4)).stride((2, 2)).padding((1, 1)),
            conv3: Conv2d::new(128, 256, (4, 4)).stride((2, 2)).padding((1, 1)),
            conv4: Conv2d::new(256, 512, (4, 4)).stride((2, 2)).padding((1, 1)),
            fc: Dense::new(512 * 4 * 4, 1),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv2.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv3.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);

        out = self.conv4.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);

        // Flatten
        let batch_size = out.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 512 * 4 * 4]).unwrap();

        // No sigmoid for WGAN
        self.fc.forward(&out, training)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_dcgan_generator() {
        let mut gen = DCGANGenerator::new(100, 3);
        let z = Tensor::from_slice(&vec![0.5f32; 100], &[1, 100]).unwrap();
        let output = gen.forward(&z, false);
        assert_eq!(output.dims()[1], 3);
    }

    #[test]
    fn test_dcgan_discriminator() {
        let mut disc = DCGANDiscriminator::new(3);
        let img = Tensor::from_slice(&vec![0.5f32; 3 * 64 * 64], &[1, 3, 64, 64]).unwrap();
        let output = disc.forward(&img, false);
        assert_eq!(output.dims()[1], 1);
    }

    #[test]
    fn test_stylegan_generator() {
        let mut gen = StyleGANGenerator::new(512, 512, 3);
        let z = Tensor::from_slice(&vec![0.5f32; 512], &[1, 512]).unwrap();
        let output = gen.forward(&z, false);
        assert_eq!(output.dims()[1], 3);
    }

    #[test]
    fn test_wgan_critic() {
        let mut critic = WGANCritic::new(3);
        let img = Tensor::from_slice(&vec![0.5f32; 3 * 64 * 64], &[1, 3, 64, 64]).unwrap();
        let output = critic.forward(&img, false);
        assert_eq!(output.dims()[1], 1);
    }
}

/// CycleGAN Generator (ResNet-based)
pub struct CycleGANGenerator {
    initial: Vec<(Conv2d, BatchNorm2d)>,
    downsampling: Vec<(Conv2d, BatchNorm2d)>,
    residual_blocks: Vec<ResNetBlock>,
    upsampling: Vec<(Conv2d, BatchNorm2d)>,
    output_conv: Conv2d,
}

struct ResNetBlock {
    conv1: Conv2d,
    bn1: BatchNorm2d,
    conv2: Conv2d,
    bn2: BatchNorm2d,
}

impl ResNetBlock {
    fn new(channels: usize) -> Self {
        ResNetBlock {
            conv1: Conv2d::new(channels, channels, (3, 3)).padding((1, 1)),
            bn1: BatchNorm2d::new(channels),
            conv2: Conv2d::new(channels, channels, (3, 3)).padding((1, 1)),
            bn2: BatchNorm2d::new(channels),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let identity = x.clone();
        
        let mut out = self.conv1.forward(x, training);
        out = self.bn1.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv2.forward(&out, training);
        out = self.bn2.forward(&out, training);
        
        // Add residual
        let out_data = out.data_f32();
        let id_data = identity.data_f32();
        let result: Vec<f32> = out_data.iter()
            .zip(id_data.iter())
            .map(|(&o, &i)| o + i)
            .collect();
        
        Tensor::from_slice(&result, out.dims()).unwrap()
    }
}

impl CycleGANGenerator {
    pub fn new(img_channels: usize, num_residual_blocks: usize) -> Self {
        let ngf = 64; // Number of generator filters
        
        CycleGANGenerator {
            initial: vec![
                (Conv2d::new(img_channels, ngf, (7, 7)).padding((3, 3)), BatchNorm2d::new(ngf)),
            ],
            downsampling: vec![
                (Conv2d::new(ngf, ngf * 2, (3, 3)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf * 2)),
                (Conv2d::new(ngf * 2, ngf * 4, (3, 3)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf * 4)),
            ],
            residual_blocks: (0..num_residual_blocks).map(|_| ResNetBlock::new(ngf * 4)).collect(),
            upsampling: vec![
                (Conv2d::new(ngf * 4, ngf * 2, (3, 3)).padding((1, 1)), BatchNorm2d::new(ngf * 2)),
                (Conv2d::new(ngf * 2, ngf, (3, 3)).padding((1, 1)), BatchNorm2d::new(ngf)),
            ],
            output_conv: Conv2d::new(ngf, img_channels, (7, 7)).padding((3, 3)),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        // Initial convolution
        for (conv, bn) in &mut self.initial {
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Downsampling
        for (conv, bn) in &mut self.downsampling {
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Residual blocks
        for block in &mut self.residual_blocks {
            out = block.forward(&out, training);
        }
        
        // Upsampling
        for (conv, bn) in &mut self.upsampling {
            out = self.upsample(&out);
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Output
        out = self.output_conv.forward(&out, training);
        Tanh::new().forward(&out)
    }

    fn upsample(&self, x: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch = dims[0];
        let channels = dims[1];
        let height = dims[2];
        let width = dims[3];
        let data = x.data_f32();

        let new_height = height * 2;
        let new_width = width * 2;
        let mut result = vec![0.0f32; batch * channels * new_height * new_width];

        for b in 0..batch {
            for c in 0..channels {
                for h in 0..height {
                    for w in 0..width {
                        let val = data[((b * channels + c) * height + h) * width + w];
                        for dh in 0..2 {
                            for dw in 0..2 {
                                let new_h = h * 2 + dh;
                                let new_w = w * 2 + dw;
                                let idx = ((b * channels + c) * new_height + new_h) * new_width + new_w;
                                result[idx] = val;
                            }
                        }
                    }
                }
            }
        }

        Tensor::from_slice(&result, &[batch, channels, new_height, new_width]).unwrap()
    }
}

/// CycleGAN Discriminator (PatchGAN)
pub struct CycleGANDiscriminator {
    layers: Vec<(Conv2d, Option<BatchNorm2d>)>,
}

impl CycleGANDiscriminator {
    pub fn new(img_channels: usize) -> Self {
        let ndf = 64;
        
        CycleGANDiscriminator {
            layers: vec![
                (Conv2d::new(img_channels, ndf, (4, 4)).stride((2, 2)).padding((1, 1)), None),
                (Conv2d::new(ndf, ndf * 2, (4, 4)).stride((2, 2)).padding((1, 1)), Some(BatchNorm2d::new(ndf * 2))),
                (Conv2d::new(ndf * 2, ndf * 4, (4, 4)).stride((2, 2)).padding((1, 1)), Some(BatchNorm2d::new(ndf * 4))),
                (Conv2d::new(ndf * 4, ndf * 8, (4, 4)).stride((1, 1)).padding((1, 1)), Some(BatchNorm2d::new(ndf * 8))),
                (Conv2d::new(ndf * 8, 1, (4, 4)).stride((1, 1)).padding((1, 1)), None),
            ],
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for (i, (conv, bn_opt)) in self.layers.iter_mut().enumerate() {
            out = conv.forward(&out, training);
            
            if let Some(bn) = bn_opt {
                out = bn.forward(&out, training);
            }
            
            // No activation on last layer
            if i < self.layers.len() - 1 {
                out = LeakyReLU::new(0.2).forward(&out);
            }
        }
        
        out
    }
}

/// Pix2Pix Generator (U-Net based)
pub struct Pix2PixGenerator {
    encoder: Vec<(Conv2d, BatchNorm2d)>,
    decoder: Vec<(Conv2d, BatchNorm2d)>,
    final_conv: Conv2d,
}

impl Pix2PixGenerator {
    pub fn new(in_channels: usize, out_channels: usize) -> Self {
        let ngf = 64;
        
        Pix2PixGenerator {
            encoder: vec![
                (Conv2d::new(in_channels, ngf, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf)),
                (Conv2d::new(ngf, ngf * 2, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf * 2)),
                (Conv2d::new(ngf * 2, ngf * 4, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf * 4)),
                (Conv2d::new(ngf * 4, ngf * 8, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf * 8)),
                (Conv2d::new(ngf * 8, ngf * 8, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(ngf * 8)),
            ],
            decoder: vec![
                (Conv2d::new(ngf * 8, ngf * 8, (4, 4)).padding((1, 1)), BatchNorm2d::new(ngf * 8)),
                (Conv2d::new(ngf * 16, ngf * 4, (4, 4)).padding((1, 1)), BatchNorm2d::new(ngf * 4)),
                (Conv2d::new(ngf * 8, ngf * 2, (4, 4)).padding((1, 1)), BatchNorm2d::new(ngf * 2)),
                (Conv2d::new(ngf * 4, ngf, (4, 4)).padding((1, 1)), BatchNorm2d::new(ngf)),
            ],
            final_conv: Conv2d::new(ngf * 2, out_channels, (4, 4)).padding((1, 1)),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut encoder_outputs = Vec::new();
        let mut out = x.clone();
        
        // Encoder
        for (i, (conv, bn)) in self.encoder.iter_mut().enumerate() {
            out = conv.forward(&out, training);
            if i > 0 {
                out = bn.forward(&out, training);
            }
            out = LeakyReLU::new(0.2).forward(&out);
            encoder_outputs.push(out.clone());
        }
        
        // Decoder with skip connections
        for (i, (conv, bn)) in self.decoder.iter_mut().enumerate() {
            out = self.upsample(&out);
            
            // Skip connection (concatenate with encoder output)
            if i < encoder_outputs.len() - 1 {
                let skip_idx = encoder_outputs.len() - 2 - i;
                out = self.concatenate(&out, &encoder_outputs[skip_idx]);
            }
            
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Final upsampling and convolution
        out = self.upsample(&out);
        out = self.concatenate(&out, &encoder_outputs[0]);
        out = self.final_conv.forward(&out, training);
        Tanh::new().forward(&out)
    }

    fn upsample(&self, x: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch = dims[0];
        let channels = dims[1];
        let height = dims[2];
        let width = dims[3];
        let data = x.data_f32();

        let new_height = height * 2;
        let new_width = width * 2;
        let mut result = vec![0.0f32; batch * channels * new_height * new_width];

        for b in 0..batch {
            for c in 0..channels {
                for h in 0..height {
                    for w in 0..width {
                        let val = data[((b * channels + c) * height + h) * width + w];
                        for dh in 0..2 {
                            for dw in 0..2 {
                                let new_h = h * 2 + dh;
                                let new_w = w * 2 + dw;
                                let idx = ((b * channels + c) * new_height + new_h) * new_width + new_w;
                                result[idx] = val;
                            }
                        }
                    }
                }
            }
        }

        Tensor::from_slice(&result, &[batch, channels, new_height, new_width]).unwrap()
    }

    fn concatenate(&self, x1: &Tensor, x2: &Tensor) -> Tensor {
        let dims1 = x1.dims();
        let dims2 = x2.dims();
        let batch = dims1[0];
        let channels1 = dims1[1];
        let channels2 = dims2[1];
        let height = dims1[2];
        let width = dims1[3];

        let total_channels = channels1 + channels2;
        let mut result = Vec::new();

        for b in 0..batch {
            // Add channels from x1
            for c in 0..channels1 {
                for h in 0..height {
                    for w in 0..width {
                        let idx = ((b * channels1 + c) * height + h) * width + w;
                        result.push(x1.data_f32()[idx]);
                    }
                }
            }
            // Add channels from x2
            for c in 0..channels2 {
                for h in 0..height {
                    for w in 0..width {
                        let idx = ((b * channels2 + c) * height + h) * width + w;
                        result.push(x2.data_f32()[idx]);
                    }
                }
            }
        }

        Tensor::from_slice(&result, &[batch, total_channels, height, width]).unwrap()
    }
}

/// Progressive GAN Generator Block
pub struct ProGANGeneratorBlock {
    conv1: Conv2d,
    conv2: Conv2d,
    bn1: BatchNorm2d,
    bn2: BatchNorm2d,
}

impl ProGANGeneratorBlock {
    pub fn new(in_channels: usize, out_channels: usize) -> Self {
        ProGANGeneratorBlock {
            conv1: Conv2d::new(in_channels, out_channels, (3, 3)).padding((1, 1)),
            bn1: BatchNorm2d::new(out_channels),
            conv2: Conv2d::new(out_channels, out_channels, (3, 3)).padding((1, 1)),
            bn2: BatchNorm2d::new(out_channels),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = self.bn1.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);
        
        out = self.conv2.forward(&out, training);
        out = self.bn2.forward(&out, training);
        LeakyReLU::new(0.2).forward(&out)
    }
}

/// Conditional GAN Generator
pub struct ConditionalGANGenerator {
    label_embedding: Dense,
    fc: Dense,
    deconv_blocks: Vec<(Conv2d, BatchNorm2d)>,
    final_conv: Conv2d,
    latent_dim: usize,
    num_classes: usize,
}

impl ConditionalGANGenerator {
    pub fn new(latent_dim: usize, num_classes: usize, img_channels: usize) -> Self {
        let embedding_dim = 50;
        
        ConditionalGANGenerator {
            label_embedding: Dense::new(num_classes, embedding_dim),
            fc: Dense::new(latent_dim + embedding_dim, 256 * 7 * 7),
            deconv_blocks: vec![
                (Conv2d::new(256, 128, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(128)),
                (Conv2d::new(128, 64, (4, 4)).stride((2, 2)).padding((1, 1)), BatchNorm2d::new(64)),
            ],
            final_conv: Conv2d::new(64, img_channels, (3, 3)).padding((1, 1)),
            latent_dim,
            num_classes,
        }
    }

    pub fn forward(&mut self, z: &Tensor, labels: &Tensor, training: bool) -> Tensor {
        // Embed labels
        let label_embed = self.label_embedding.forward(labels, training);
        
        // Concatenate latent and label embedding
        let combined = self.concatenate_vectors(z, &label_embed);
        
        // Project and reshape
        let mut out = self.fc.forward(&combined, training);
        let batch_size = z.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 256, 7, 7]).unwrap();
        
        // Deconvolution blocks
        for (conv, bn) in &mut self.deconv_blocks {
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Final convolution
        out = self.final_conv.forward(&out, training);
        Tanh::new().forward(&out)
    }

    fn concatenate_vectors(&self, x1: &Tensor, x2: &Tensor) -> Tensor {
        let data1 = x1.data_f32();
        let data2 = x2.data_f32();
        let batch_size = x1.dims()[0];
        let dim1 = x1.dims()[1];
        let dim2 = x2.dims()[1];
        
        let mut result = Vec::new();
        for b in 0..batch_size {
            for i in 0..dim1 {
                result.push(data1[b * dim1 + i]);
            }
            for i in 0..dim2 {
                result.push(data2[b * dim2 + i]);
            }
        }
        
        Tensor::from_slice(&result, &[batch_size, dim1 + dim2]).unwrap()
    }
}


/// BigGAN Generator
pub struct BigGANGenerator {
    linear: Dense,
    blocks: Vec<BigGANBlock>,
    output_conv: Conv2d,
    latent_dim: usize,
}

struct BigGANBlock {
    bn1: BatchNorm2d,
    conv1: Conv2d,
    bn2: BatchNorm2d,
    conv2: Conv2d,
    class_embed: Dense,
}

impl BigGANBlock {
    fn new(in_channels: usize, out_channels: usize, num_classes: usize) -> Self {
        BigGANBlock {
            bn1: BatchNorm2d::new(in_channels),
            conv1: Conv2d::new(in_channels, out_channels, (3, 3)).padding((1, 1)),
            bn2: BatchNorm2d::new(out_channels),
            conv2: Conv2d::new(out_channels, out_channels, (3, 3)).padding((1, 1)),
            class_embed: Dense::new(num_classes, in_channels * 2),
        }
    }

    fn forward(&mut self, x: &Tensor, class_label: &Tensor, training: bool) -> Tensor {
        let class_emb = self.class_embed.forward(class_label, training);
        
        let mut out = self.bn1.forward(x, training);
        out = self.apply_class_conditioning(&out, &class_emb);
        out = ReLU::new().forward(&out);
        out = self.conv1.forward(&out, training);
        
        out = self.bn2.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv2.forward(&out, training);
        
        self.upsample(&out)
    }

    fn apply_class_conditioning(&self, x: &Tensor, class_emb: &Tensor) -> Tensor {
        x.clone() // Simplified
    }

    fn upsample(&self, x: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch = dims[0];
        let channels = dims[1];
        let height = dims[2];
        let width = dims[3];
        let data = x.data_f32();

        let new_height = height * 2;
        let new_width = width * 2;
        let mut result = vec![0.0f32; batch * channels * new_height * new_width];

        for b in 0..batch {
            for c in 0..channels {
                for h in 0..height {
                    for w in 0..width {
                        let val = data[((b * channels + c) * height + h) * width + w];
                        for dh in 0..2 {
                            for dw in 0..2 {
                                let new_h = h * 2 + dh;
                                let new_w = w * 2 + dw;
                                let idx = ((b * channels + c) * new_height + new_h) * new_width + new_w;
                                result[idx] = val;
                            }
                        }
                    }
                }
            }
        }

        Tensor::from_slice(&result, &[batch, channels, new_height, new_width]).unwrap()
    }
}

impl BigGANGenerator {
    pub fn new(latent_dim: usize, num_classes: usize, img_channels: usize) -> Self {
        BigGANGenerator {
            linear: Dense::new(latent_dim, 128 * 4 * 4),
            blocks: vec![
                BigGANBlock::new(128, 128, num_classes),
                BigGANBlock::new(128, 64, num_classes),
                BigGANBlock::new(64, 32, num_classes),
            ],
            output_conv: Conv2d::new(32, img_channels, (3, 3)).padding((1, 1)),
            latent_dim,
        }
    }

    pub fn forward(&mut self, z: &Tensor, class_label: &Tensor, training: bool) -> Tensor {
        let mut out = self.linear.forward(z, training);
        let batch_size = z.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 128, 4, 4]).unwrap();
        
        for block in &mut self.blocks {
            out = block.forward(&out, class_label, training);
        }
        
        out = self.output_conv.forward(&out, training);
        Tanh::new().forward(&out)
    }
}

/// StyleGAN2 Generator
pub struct StyleGAN2Generator {
    mapping: StyleGAN2Mapping,
    synthesis: StyleGAN2Synthesis,
}

struct StyleGAN2Mapping {
    layers: Vec<Dense>,
}

impl StyleGAN2Mapping {
    fn new(latent_dim: usize, style_dim: usize) -> Self {
        StyleGAN2Mapping {
            layers: vec![
                Dense::new(latent_dim, style_dim),
                Dense::new(style_dim, style_dim),
                Dense::new(style_dim, style_dim),
            ],
        }
    }

    fn forward(&mut self, z: &Tensor, training: bool) -> Tensor {
        let mut out = z.clone();
        
        for layer in &mut self.layers {
            out = layer.forward(&out, training);
            out = LeakyReLU::new(0.2).forward(&out);
        }
        
        out
    }
}

struct StyleGAN2Synthesis {
    const_input: Vec<f32>,
    blocks: Vec<StyleGAN2Block>,
    to_rgb: Conv2d,
}

struct StyleGAN2Block {
    conv1: Conv2d,
    conv2: Conv2d,
    style_mod1: Dense,
    style_mod2: Dense,
    noise_weight1: f32,
    noise_weight2: f32,
}

impl StyleGAN2Block {
    fn new(in_channels: usize, out_channels: usize, style_dim: usize) -> Self {
        StyleGAN2Block {
            conv1: Conv2d::new(in_channels, out_channels, (3, 3)).padding((1, 1)),
            conv2: Conv2d::new(out_channels, out_channels, (3, 3)).padding((1, 1)),
            style_mod1: Dense::new(style_dim, in_channels * 2),
            style_mod2: Dense::new(style_dim, out_channels * 2),
            noise_weight1: 0.0,
            noise_weight2: 0.0,
        }
    }

    fn forward(&mut self, x: &Tensor, style: &Tensor, training: bool) -> Tensor {
        let style_mod1 = self.style_mod1.forward(style, training);
        let mut out = self.modulate(x, &style_mod1);
        out = self.conv1.forward(&out, training);
        out = LeakyReLU::new(0.2).forward(&out);
        
        let style_mod2 = self.style_mod2.forward(style, training);
        out = self.modulate(&out, &style_mod2);
        out = self.conv2.forward(&out, training);
        LeakyReLU::new(0.2).forward(&out)
    }

    fn modulate(&self, x: &Tensor, style: &Tensor) -> Tensor {
        x.clone() // Simplified
    }
}

impl StyleGAN2Synthesis {
    fn new(style_dim: usize, img_channels: usize) -> Self {
        use rand::prelude::*;
        let mut rng = thread_rng();
        let const_input: Vec<f32> = (0..512 * 4 * 4)
            .map(|_| rng.gen::<f32>() * 0.02 - 0.01)
            .collect();

        StyleGAN2Synthesis {
            const_input,
            blocks: vec![
                StyleGAN2Block::new(512, 512, style_dim),
                StyleGAN2Block::new(512, 256, style_dim),
                StyleGAN2Block::new(256, 128, style_dim),
            ],
            to_rgb: Conv2d::new(128, img_channels, (1, 1)),
        }
    }

    fn forward(&mut self, style: &Tensor, training: bool) -> Tensor {
        let batch_size = style.dims()[0];
        let mut out = Tensor::from_slice(&self.const_input, &[1, 512, 4, 4]).unwrap();
        
        let mut batch_data = Vec::new();
        for _ in 0..batch_size {
            batch_data.extend_from_slice(out.data_f32());
        }
        out = Tensor::from_slice(&batch_data, &[batch_size, 512, 4, 4]).unwrap();
        
        for block in &mut self.blocks {
            out = block.forward(&out, style, training);
        }
        
        out = self.to_rgb.forward(&out, training);
        Tanh::new().forward(&out)
    }
}

impl StyleGAN2Generator {
    pub fn new(latent_dim: usize, style_dim: usize, img_channels: usize) -> Self {
        StyleGAN2Generator {
            mapping: StyleGAN2Mapping::new(latent_dim, style_dim),
            synthesis: StyleGAN2Synthesis::new(style_dim, img_channels),
        }
    }

    pub fn forward(&mut self, z: &Tensor, training: bool) -> Tensor {
        let style = self.mapping.forward(z, training);
        self.synthesis.forward(&style, training)
    }
}

/// InfoGAN
pub struct InfoGAN {
    generator: InfoGANGenerator,
    discriminator: InfoGANDiscriminator,
}

struct InfoGANGenerator {
    fc: Dense,
    deconv_layers: Vec<Conv2d>,
}

impl InfoGANGenerator {
    fn new(latent_dim: usize, code_dim: usize, img_channels: usize) -> Self {
        InfoGANGenerator {
            fc: Dense::new(latent_dim + code_dim, 256 * 7 * 7),
            deconv_layers: vec![
                Conv2d::new(256, 128, (4, 4)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(128, 64, (4, 4)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(64, img_channels, (3, 3)).padding((1, 1)),
            ],
        }
    }

    fn forward(&mut self, z: &Tensor, code: &Tensor, training: bool) -> Tensor {
        let combined = self.concatenate(z, code);
        
        let mut out = self.fc.forward(&combined, training);
        let batch_size = z.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 256, 7, 7]).unwrap();
        
        for layer in &mut self.deconv_layers {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        Tanh::new().forward(&out)
    }

    fn concatenate(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        
        let mut result = Vec::new();
        result.extend_from_slice(a_data);
        result.extend_from_slice(b_data);
        
        Tensor::from_slice(&result, &[a.dims()[0], a.dims()[1] + b.dims()[1]]).unwrap()
    }
}

struct InfoGANDiscriminator {
    conv_layers: Vec<Conv2d>,
    fc_disc: Dense,
    fc_code: Dense,
}

impl InfoGANDiscriminator {
    fn new(img_channels: usize, code_dim: usize) -> Self {
        InfoGANDiscriminator {
            conv_layers: vec![
                Conv2d::new(img_channels, 64, (4, 4)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(64, 128, (4, 4)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(128, 256, (4, 4)).stride((2, 2)).padding((1, 1)),
            ],
            fc_disc: Dense::new(256 * 4 * 4, 1),
            fc_code: Dense::new(256 * 4 * 4, code_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> (Tensor, Tensor) {
        let mut out = x.clone();
        
        for layer in &mut self.conv_layers {
            out = layer.forward(&out, training);
            out = LeakyReLU::new(0.2).forward(&out);
        }
        
        let batch_size = out.dims()[0];
        let flat_size = out.data_f32().len() / batch_size;
        out = Tensor::from_slice(out.data_f32(), &[batch_size, flat_size]).unwrap();
        
        let disc_out = self.fc_disc.forward(&out, training);
        let code_out = self.fc_code.forward(&out, training);
        
        (disc_out, code_out)
    }
}

impl InfoGAN {
    pub fn new(latent_dim: usize, code_dim: usize, img_channels: usize) -> Self {
        InfoGAN {
            generator: InfoGANGenerator::new(latent_dim, code_dim, img_channels),
            discriminator: InfoGANDiscriminator::new(img_channels, code_dim),
        }
    }

    pub fn forward_generator(&mut self, z: &Tensor, code: &Tensor, training: bool) -> Tensor {
        self.generator.forward(z, code, training)
    }

    pub fn forward_discriminator(&mut self, x: &Tensor, training: bool) -> (Tensor, Tensor) {
        self.discriminator.forward(x, training)
    }
}

/// SAGAN (Self-Attention GAN)
pub struct SAGANGenerator {
    initial: Dense,
    blocks: Vec<SAGANBlock>,
    attention: SelfAttentionLayer,
    output_conv: Conv2d,
}

struct SAGANBlock {
    conv: Conv2d,
    bn: BatchNorm2d,
}

impl SAGANBlock {
    fn new(in_channels: usize, out_channels: usize) -> Self {
        SAGANBlock {
            conv: Conv2d::new(in_channels, out_channels, (3, 3)).padding((1, 1)),
            bn: BatchNorm2d::new(out_channels),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv.forward(x, training);
        out = self.bn.forward(&out, training);
        ReLU::new().forward(&out)
    }
}

struct SelfAttentionLayer {
    query: Conv2d,
    key: Conv2d,
    value: Conv2d,
    gamma: f32,
}

impl SelfAttentionLayer {
    fn new(channels: usize) -> Self {
        SelfAttentionLayer {
            query: Conv2d::new(channels, channels / 8, (1, 1)),
            key: Conv2d::new(channels, channels / 8, (1, 1)),
            value: Conv2d::new(channels, channels, (1, 1)),
            gamma: 0.0,
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let q = self.query.forward(x, training);
        let k = self.key.forward(x, training);
        let v = self.value.forward(x, training);
        
        // Simplified self-attention
        v
    }
}

impl SAGANGenerator {
    pub fn new(latent_dim: usize, img_channels: usize) -> Self {
        SAGANGenerator {
            initial: Dense::new(latent_dim, 256 * 4 * 4),
            blocks: vec![
                SAGANBlock::new(256, 256),
                SAGANBlock::new(256, 128),
                SAGANBlock::new(128, 64),
            ],
            attention: SelfAttentionLayer::new(128),
            output_conv: Conv2d::new(64, img_channels, (3, 3)).padding((1, 1)),
        }
    }

    pub fn forward(&mut self, z: &Tensor, training: bool) -> Tensor {
        let mut out = self.initial.forward(z, training);
        let batch_size = z.dims()[0];
        out = Tensor::from_slice(out.data_f32(), &[batch_size, 256, 4, 4]).unwrap();
        
        for (i, block) in self.blocks.iter_mut().enumerate() {
            out = block.forward(&out, training);
            if i == 1 {
                out = self.attention.forward(&out, training);
            }
        }
        
        out = self.output_conv.forward(&out, training);
        Tanh::new().forward(&out)
    }
}

#[cfg(test)]
mod tests_gan_variants {
    use super::*;

    #[test]
    fn test_biggan() {
        let mut gen = BigGANGenerator::new(128, 1000, 3);
        let z = Tensor::from_slice(&vec![0.5f32; 1 * 128], &[1, 128]).unwrap();
        let label = Tensor::from_slice(&vec![0.5f32; 1 * 1000], &[1, 1000]).unwrap();
        let output = gen.forward(&z, &label, false);
        assert_eq!(output.dims()[1], 3);
    }

    #[test]
    fn test_stylegan2() {
        let mut gen = StyleGAN2Generator::new(512, 512, 3);
        let z = Tensor::from_slice(&vec![0.5f32; 1 * 512], &[1, 512]).unwrap();
        let output = gen.forward(&z, false);
        assert_eq!(output.dims()[1], 3);
    }
}