voirs_conversion/

models.rs

//! Conversion models and neural network implementations

use crate::{Error, Result};
use candle_core::{Device, Module, Tensor};
use candle_nn::{linear, Linear, VarBuilder};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;
use tracing::{debug, info, warn};

/// Model types for voice conversion
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ModelType {
    /// Neural voice conversion model
    NeuralVC,
    /// CycleGAN-based model
    CycleGAN,
    /// AutoVC model
    AutoVC,
    /// StarGAN-VC model
    StarGAN,
    /// WaveNet-based model
    WaveNet,
    /// Transformer-based model
    Transformer,
    /// Custom model
    Custom,
}

impl ModelType {
    /// Get default model configuration
    pub fn default_config(&self) -> ModelConfig {
        match self {
            ModelType::NeuralVC => ModelConfig {
                input_dim: 80,
                hidden_dim: 256,
                output_dim: 80,
                num_layers: 4,
                dropout: 0.1,
                activation: ActivationType::ReLU,
                normalization: NormalizationType::BatchNorm,
                model_specific: HashMap::new(),
            },
            ModelType::CycleGAN => ModelConfig {
                input_dim: 80,
                hidden_dim: 512,
                output_dim: 80,
                num_layers: 6,
                dropout: 0.0,
                activation: ActivationType::LeakyReLU,
                normalization: NormalizationType::InstanceNorm,
                model_specific: HashMap::from([
                    ("discriminator_layers".to_string(), 3.0),
                    ("lambda_cycle".to_string(), 10.0),
                ]),
            },
            ModelType::AutoVC => ModelConfig {
                input_dim: 80,
                hidden_dim: 512,
                output_dim: 80,
                num_layers: 8,
                dropout: 0.1,
                activation: ActivationType::ReLU,
                normalization: NormalizationType::BatchNorm,
                model_specific: HashMap::from([
                    ("bottleneck_dim".to_string(), 32.0),
                    ("speaker_embedding_dim".to_string(), 256.0),
                ]),
            },
            ModelType::StarGAN => ModelConfig {
                input_dim: 80,
                hidden_dim: 512,
                output_dim: 80,
                num_layers: 6,
                dropout: 0.0,
                activation: ActivationType::ReLU,
                normalization: NormalizationType::InstanceNorm,
                model_specific: HashMap::from([
                    ("domain_embedding_dim".to_string(), 8.0),
                    ("num_domains".to_string(), 4.0),
                ]),
            },
            ModelType::WaveNet => ModelConfig {
                input_dim: 1,
                hidden_dim: 256,
                output_dim: 256,
                num_layers: 30,
                dropout: 0.0,
                activation: ActivationType::Tanh,
                normalization: NormalizationType::None,
                model_specific: HashMap::from([
                    ("dilation_channels".to_string(), 32.0),
                    ("residual_channels".to_string(), 32.0),
                    ("skip_channels".to_string(), 256.0),
                ]),
            },
            ModelType::Transformer => ModelConfig {
                input_dim: 80,
                hidden_dim: 512,
                output_dim: 80,
                num_layers: 6,
                dropout: 0.1,
                activation: ActivationType::GELU,
                normalization: NormalizationType::LayerNorm,
                model_specific: HashMap::from([
                    ("num_heads".to_string(), 8.0),
                    ("ff_dim".to_string(), 2048.0),
                ]),
            },
            ModelType::Custom => ModelConfig::default(),
        }
    }

    /// Check if model supports real-time processing
    pub fn supports_realtime(&self) -> bool {
        match self {
            ModelType::NeuralVC => true,
            ModelType::AutoVC => true,
            ModelType::WaveNet => false, // Too computationally expensive
            ModelType::Transformer => true,
            ModelType::CycleGAN => false, // Requires adversarial training
            ModelType::StarGAN => false,  // Complex multi-domain training
            ModelType::Custom => false,   // Conservative default
        }
    }
}

/// Model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelConfig {
    /// Input dimension
    pub input_dim: usize,
    /// Hidden dimension
    pub hidden_dim: usize,
    /// Output dimension
    pub output_dim: usize,
    /// Number of layers
    pub num_layers: usize,
    /// Dropout rate
    pub dropout: f32,
    /// Activation function
    pub activation: ActivationType,
    /// Normalization type
    pub normalization: NormalizationType,
    /// Model-specific parameters
    pub model_specific: HashMap<String, f32>,
}

impl Default for ModelConfig {
    fn default() -> Self {
        Self {
            input_dim: 80,
            hidden_dim: 256,
            output_dim: 80,
            num_layers: 4,
            dropout: 0.1,
            activation: ActivationType::ReLU,
            normalization: NormalizationType::BatchNorm,
            model_specific: HashMap::new(),
        }
    }
}

/// Activation function types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ActivationType {
    /// Rectified Linear Unit activation
    ReLU,
    /// Leaky Rectified Linear Unit activation
    LeakyReLU,
    /// Hyperbolic tangent activation
    Tanh,
    /// Sigmoid activation function
    Sigmoid,
    /// Gaussian Error Linear Unit activation
    GELU,
    /// Swish activation function
    Swish,
}

/// Normalization types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NormalizationType {
    /// No normalization applied
    None,
    /// Batch normalization
    BatchNorm,
    /// Layer normalization
    LayerNorm,
    /// Instance normalization
    InstanceNorm,
    /// Group normalization
    GroupNorm,
}

/// Main conversion model interface
#[derive(Debug)]
pub struct ConversionModel {
    /// Model type
    pub model_type: ModelType,
    /// Model configuration
    pub config: ModelConfig,
    /// Neural network implementation
    network: Box<dyn NeuralNetwork>,
    /// Model device
    device: Device,
    /// Model parameters loaded
    parameters_loaded: bool,
    /// Model metadata
    metadata: ModelMetadata,
}

/// Model metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetadata {
    /// Model name
    pub name: String,
    /// Model version
    pub version: String,
    /// Training dataset
    pub training_dataset: Option<String>,
    /// Training epochs
    pub training_epochs: Option<u32>,
    /// Model size in parameters
    pub parameter_count: Option<u64>,
    /// Sample rate the model was trained on
    pub sample_rate: u32,
    /// Creation timestamp
    pub created_at: Option<std::time::SystemTime>,
}

impl Default for ModelMetadata {
    fn default() -> Self {
        Self {
            name: "Untitled Model".to_string(),
            version: "1.0.0".to_string(),
            training_dataset: None,
            training_epochs: None,
            parameter_count: None,
            sample_rate: 22050,
            created_at: Some(std::time::SystemTime::now()),
        }
    }
}

/// Neural network trait for voice conversion models
pub trait NeuralNetwork: std::fmt::Debug + Send + Sync {
    /// Forward pass through the network
    fn forward(&self, input: &Tensor) -> Result<Tensor>;

    /// Get input shape requirements
    fn input_shape(&self) -> &[usize];

    /// Get output shape
    fn output_shape(&self) -> &[usize];

    /// Load model weights from buffer
    fn load_weights(&mut self, weights: &[u8]) -> Result<()>;

    /// Save model weights to buffer
    fn save_weights(&self) -> Result<Vec<u8>>;

    /// Get parameter count
    fn parameter_count(&self) -> u64;

    /// Set training mode
    fn set_training(&mut self, training: bool);

    /// Clone the network
    fn clone_network(&self) -> Box<dyn NeuralNetwork>;
}

impl ConversionModel {
    /// Create new model with default configuration
    pub fn new(model_type: ModelType) -> Self {
        let config = model_type.default_config();
        Self::with_config(model_type, config)
    }

    /// Create model with custom configuration
    pub fn with_config(model_type: ModelType, config: ModelConfig) -> Self {
        let device = Device::Cpu; // Default to CPU, can be changed later
        let network =
            Self::create_network(model_type, &config, &device).expect("operation should succeed");

        Self {
            model_type,
            config,
            network,
            device,
            parameters_loaded: false,
            metadata: ModelMetadata::default(),
        }
    }

    /// Load model from file path
    pub async fn load_from_path<P: AsRef<Path>>(path: P) -> Result<Self> {
        let path = path.as_ref();
        info!("Loading conversion model from: {:?}", path);

        // Check if path exists
        if !path.exists() {
            return Err(Error::model(format!("Model file not found: {path:?}")));
        }

        // For now, create a default model
        // In a real implementation, this would parse the model file format
        let model_type = ModelType::NeuralVC;
        let mut model = Self::new(model_type);

        // Try to load model weights if available
        if let Some(weights_path) = Self::find_weights_file(path) {
            model.load_weights_file(&weights_path).await?;
        }

        // Load metadata if available
        if let Some(metadata_path) = Self::find_metadata_file(path) {
            model.load_metadata_file(&metadata_path).await?;
        }

        info!("Successfully loaded model: {}", model.metadata.name);
        Ok(model)
    }

    /// Load model from bytes
    pub async fn load_from_bytes(bytes: &[u8], model_type: ModelType) -> Result<Self> {
        debug!("Loading model from {} bytes", bytes.len());

        let mut model = Self::new(model_type);
        model.network.load_weights(bytes)?;
        model.parameters_loaded = true;

        Ok(model)
    }

    /// Process audio tensor with the model
    pub async fn process_tensor(&self, input: &Tensor) -> Result<Tensor> {
        if !self.parameters_loaded {
            warn!("Model parameters not loaded, using uninitialized weights");
        }

        debug!("Processing tensor with shape: {:?}", input.shape());

        // Validate input shape
        let expected_shape = self.network.input_shape();
        let input_shape = input.shape().dims();

        if input_shape.len() < expected_shape.len() {
            return Err(Error::model(format!(
                "Input tensor has insufficient dimensions: expected {expected_shape:?}, got {input_shape:?}"
            )));
        }

        // Process through network
        let output = self.network.forward(input)?;

        debug!("Model output shape: {:?}", output.shape());
        Ok(output)
    }

    /// Process audio samples
    pub async fn process(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Convert audio to tensor
        let input_tensor = self.audio_to_tensor(input)?;

        // Process through model
        let output_tensor = self.process_tensor(&input_tensor).await?;

        // Convert back to audio
        self.tensor_to_audio(&output_tensor)
    }

    /// Set model device
    pub fn set_device(&mut self, device: Device) -> Result<()> {
        info!("Moving model to device: {:?}", device);
        self.device = device;
        // In a real implementation, this would move the model parameters to the new device
        Ok(())
    }

    /// Get model information
    pub fn info(&self) -> ModelInfo {
        ModelInfo {
            model_type: self.model_type,
            config: self.config.clone(),
            metadata: self.metadata.clone(),
            device: format!("{:?}", self.device),
            parameters_loaded: self.parameters_loaded,
            parameter_count: self.network.parameter_count(),
            supports_realtime: self.model_type.supports_realtime(),
        }
    }

    /// Save model to file
    pub async fn save_to_path<P: AsRef<Path>>(&self, path: P) -> Result<()> {
        let path = path.as_ref();
        info!("Saving model to: {:?}", path);

        // Create directory if it doesn't exist
        if let Some(parent) = path.parent() {
            std::fs::create_dir_all(parent)?;
        }

        // Save weights
        let weights = self.network.save_weights()?;
        let weights_path = path.with_extension("weights");
        std::fs::write(&weights_path, weights)?;

        // Save metadata
        let metadata_json = serde_json::to_string_pretty(&self.metadata)?;
        let metadata_path = path.with_extension("json");
        std::fs::write(&metadata_path, metadata_json)?;

        // Save config
        let config_json = serde_json::to_string_pretty(&self.config)?;
        let config_path = path.with_extension("config.json");
        std::fs::write(&config_path, config_json)?;

        info!("Model saved successfully");
        Ok(())
    }

    /// Convert audio to tensor
    fn audio_to_tensor(&self, audio: &[f32]) -> Result<Tensor> {
        // Reshape audio based on model requirements
        let input_shape = self.network.input_shape();

        match input_shape.len() {
            1 => {
                // 1D input (raw audio) - add batch dimension for neural network
                let feature_size = input_shape[0];
                if audio.len() != feature_size {
                    return Err(Error::model(format!(
                        "Input audio length {} doesn't match expected feature size {}",
                        audio.len(),
                        feature_size
                    )));
                }
                Tensor::from_vec(audio.to_vec(), (1, audio.len()), &self.device)
            }
            2 => {
                // 2D input (batch x features or time x features)
                let _batch_size = 1;
                let feature_size = input_shape[1];
                let time_steps = audio.len() / feature_size;

                if !audio.len().is_multiple_of(feature_size) {
                    // Pad audio to match feature size
                    let mut padded_audio = audio.to_vec();
                    let padding_needed = feature_size - (audio.len() % feature_size);
                    padded_audio.extend(vec![0.0; padding_needed]);

                    let new_time_steps = padded_audio.len() / feature_size;
                    Tensor::from_vec(padded_audio, (new_time_steps, feature_size), &self.device)
                } else {
                    Tensor::from_vec(audio.to_vec(), (time_steps, feature_size), &self.device)
                }
            }
            3 => {
                // 3D input (batch x time x features)
                let batch_size = 1;
                let feature_size = input_shape[2];
                let time_steps = audio.len() / feature_size;

                if !audio.len().is_multiple_of(feature_size) {
                    let mut padded_audio = audio.to_vec();
                    let padding_needed = feature_size - (audio.len() % feature_size);
                    padded_audio.extend(vec![0.0; padding_needed]);

                    let new_time_steps = padded_audio.len() / feature_size;
                    Tensor::from_vec(
                        padded_audio,
                        (batch_size, new_time_steps, feature_size),
                        &self.device,
                    )
                } else {
                    Tensor::from_vec(
                        audio.to_vec(),
                        (batch_size, time_steps, feature_size),
                        &self.device,
                    )
                }
            }
            _ => {
                return Err(Error::model(format!(
                    "Unsupported input shape dimensionality: {}",
                    input_shape.len()
                )));
            }
        }
        .map_err(|e| Error::model(format!("Failed to create input tensor: {e}")))
    }

    /// Convert tensor to audio
    fn tensor_to_audio(&self, tensor: &Tensor) -> Result<Vec<f32>> {
        match tensor.shape().dims().len() {
            1 => {
                // 1D tensor - direct conversion
                tensor
                    .to_vec1::<f32>()
                    .map_err(|e| Error::model(format!("Failed to convert tensor to audio: {e}")))
            }
            2 => {
                // 2D tensor - remove batch dimension and convert
                let squeezed = tensor
                    .squeeze(0)
                    .map_err(|e| Error::model(format!("Failed to squeeze tensor: {e}")))?;
                squeezed
                    .to_vec1::<f32>()
                    .map_err(|e| Error::model(format!("Failed to convert tensor to audio: {e}")))
            }
            _ => Err(Error::model(format!(
                "Unsupported tensor shape for audio conversion: {:?}",
                tensor.shape()
            ))),
        }
    }

    /// Create neural network implementation
    fn create_network(
        model_type: ModelType,
        config: &ModelConfig,
        device: &Device,
    ) -> Result<Box<dyn NeuralNetwork>> {
        match model_type {
            ModelType::NeuralVC => Ok(Box::new(NeuralVCNetwork::new(config, device)?)),
            ModelType::AutoVC => Ok(Box::new(AutoVCNetwork::new(config, device)?)),
            ModelType::Transformer => Ok(Box::new(TransformerNetwork::new(config, device)?)),
            _ => {
                // For unsupported models, use a simple feedforward network
                warn!(
                    "Model type {:?} not fully implemented, using simple feedforward network",
                    model_type
                );
                Ok(Box::new(SimpleNetwork::new(config, device)?))
            }
        }
    }

    // Helper methods for file operations

    fn find_weights_file(base_path: &Path) -> Option<std::path::PathBuf> {
        let weights_path = base_path.with_extension("weights");
        if weights_path.exists() {
            Some(weights_path)
        } else {
            None
        }
    }

    fn find_metadata_file(base_path: &Path) -> Option<std::path::PathBuf> {
        let metadata_path = base_path.with_extension("json");
        if metadata_path.exists() {
            Some(metadata_path)
        } else {
            None
        }
    }

    async fn load_weights_file(&mut self, path: &Path) -> Result<()> {
        let weights = std::fs::read(path)?;
        self.network.load_weights(&weights)?;
        self.parameters_loaded = true;
        Ok(())
    }

    async fn load_metadata_file(&mut self, path: &Path) -> Result<()> {
        let metadata_json = std::fs::read_to_string(path)?;
        self.metadata = serde_json::from_str(&metadata_json)?;
        Ok(())
    }
}

impl Default for ConversionModel {
    fn default() -> Self {
        Self::new(ModelType::NeuralVC)
    }
}

/// Model information structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelInfo {
    /// Type of the conversion model
    pub model_type: ModelType,
    /// Model configuration parameters
    pub config: ModelConfig,
    /// Model metadata information
    pub metadata: ModelMetadata,
    /// Device where the model is loaded (e.g., "cpu", "cuda:0")
    pub device: String,
    /// Whether model parameters are loaded into memory
    pub parameters_loaded: bool,
    /// Total number of model parameters
    pub parameter_count: u64,
    /// Whether the model supports real-time processing
    pub supports_realtime: bool,
}

// Neural network implementations

/// Simple feedforward neural network
#[derive(Debug)]
struct SimpleNetwork {
    layers: Vec<Linear>,
    config: ModelConfig,
    input_shape: Vec<usize>,
    output_shape: Vec<usize>,
    training: bool,
}

impl SimpleNetwork {
    fn new(config: &ModelConfig, device: &Device) -> Result<Self> {
        let varmap = candle_nn::VarMap::new();
        let vs = VarBuilder::from_varmap(&varmap, candle_core::DType::F32, device);

        let mut layers = Vec::new();
        let mut current_dim = config.input_dim;

        // Create hidden layers
        for i in 0..config.num_layers - 1 {
            let layer = linear(current_dim, config.hidden_dim, vs.pp(format!("layer_{i}")))
                .map_err(|e| Error::model(format!("Failed to create layer {i}: {e}")))?;
            layers.push(layer);
            current_dim = config.hidden_dim;
        }

        // Output layer
        let output_layer = linear(current_dim, config.output_dim, vs.pp("output"))
            .map_err(|e| Error::model(format!("Failed to create output layer: {e}")))?;
        layers.push(output_layer);

        Ok(Self {
            layers,
            config: config.clone(),
            input_shape: vec![config.input_dim],
            output_shape: vec![config.output_dim],
            training: false,
        })
    }
}

impl NeuralNetwork for SimpleNetwork {
    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let mut x = input.clone();

        for (i, layer) in self.layers.iter().enumerate() {
            x = layer
                .forward(&x)
                .map_err(|e| Error::model(format!("Forward pass failed at layer {i}: {e}")))?;

            // Apply activation (except for output layer)
            if i < self.layers.len() - 1 {
                x = match self.config.activation {
                    ActivationType::ReLU => x.relu()?,
                    ActivationType::LeakyReLU => {
                        let scaled = (x.clone() * 0.01)?;
                        x.maximum(&scaled)?
                    }
                    ActivationType::Tanh => x.tanh()?,
                    ActivationType::Sigmoid => {
                        // Implement sigmoid as 1 / (1 + exp(-x))
                        let neg_x = x.neg()?;
                        let exp_neg_x = neg_x.exp()?;
                        let one_plus_exp = (exp_neg_x + 1.0)?;
                        one_plus_exp.recip()?
                    }
                    ActivationType::GELU => x.gelu()?,
                    ActivationType::Swish => x.silu()?,
                };
            }
        }

        Ok(x)
    }

    fn input_shape(&self) -> &[usize] {
        &self.input_shape
    }

    fn output_shape(&self) -> &[usize] {
        &self.output_shape
    }

    fn load_weights(&mut self, _weights: &[u8]) -> Result<()> {
        // Placeholder implementation
        Ok(())
    }

    fn save_weights(&self) -> Result<Vec<u8>> {
        // Placeholder implementation
        Ok(vec![0; 1024])
    }

    fn parameter_count(&self) -> u64 {
        let mut count = 0;
        let mut current_dim = self.config.input_dim;

        for _ in 0..self.config.num_layers - 1 {
            count += (current_dim * self.config.hidden_dim + self.config.hidden_dim) as u64;
            current_dim = self.config.hidden_dim;
        }

        // Output layer
        count += (current_dim * self.config.output_dim + self.config.output_dim) as u64;

        count
    }

    fn set_training(&mut self, training: bool) {
        self.training = training;
    }

    fn clone_network(&self) -> Box<dyn NeuralNetwork> {
        Box::new(SimpleNetwork {
            layers: Vec::new(), // Can't easily clone Linear layers
            config: self.config.clone(),
            input_shape: self.input_shape.clone(),
            output_shape: self.output_shape.clone(),
            training: self.training,
        })
    }
}

/// Neural voice conversion network
#[derive(Debug)]
struct NeuralVCNetwork {
    encoder: Vec<Linear>,
    decoder: Vec<Linear>,
    config: ModelConfig,
    input_shape: Vec<usize>,
    output_shape: Vec<usize>,
    training: bool,
}

impl NeuralVCNetwork {
    fn new(config: &ModelConfig, device: &Device) -> Result<Self> {
        let varmap = candle_nn::VarMap::new();
        let vs = VarBuilder::from_varmap(&varmap, candle_core::DType::F32, device);

        // Create encoder
        let mut encoder = Vec::new();
        let mut current_dim = config.input_dim;

        for i in 0..config.num_layers / 2 {
            let layer = linear(
                current_dim,
                config.hidden_dim,
                vs.pp(format!("encoder_{i}")),
            )
            .map_err(|e| Error::model(format!("Failed to create encoder layer {i}: {e}")))?;
            encoder.push(layer);
            current_dim = config.hidden_dim;
        }

        // Create decoder
        let mut decoder = Vec::new();
        for i in 0..config.num_layers / 2 {
            let output_dim = if i == config.num_layers / 2 - 1 {
                config.output_dim
            } else {
                config.hidden_dim
            };

            let layer = linear(current_dim, output_dim, vs.pp(format!("decoder_{i}")))
                .map_err(|e| Error::model(format!("Failed to create decoder layer {i}: {e}")))?;
            decoder.push(layer);
            current_dim = output_dim;
        }

        Ok(Self {
            encoder,
            decoder,
            config: config.clone(),
            input_shape: vec![config.input_dim],
            output_shape: vec![config.output_dim],
            training: false,
        })
    }
}

impl NeuralNetwork for NeuralVCNetwork {
    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let mut x = input.clone();

        // Encoder forward pass
        for (i, layer) in self.encoder.iter().enumerate() {
            x = layer.forward(&x).map_err(|e| {
                Error::model(format!("Encoder forward pass failed at layer {i}: {e}"))
            })?;

            x = match self.config.activation {
                ActivationType::ReLU => x.relu()?,
                ActivationType::LeakyReLU => {
                    let scaled = (x.clone() * 0.01)?;
                    x.maximum(&scaled)?
                }
                ActivationType::Tanh => x.tanh()?,
                ActivationType::Sigmoid => {
                    // Implement sigmoid as 1 / (1 + exp(-x))
                    let neg_x = x.neg()?;
                    let exp_neg_x = neg_x.exp()?;
                    let one_plus_exp = (exp_neg_x + 1.0)?;
                    one_plus_exp.recip()?
                }
                ActivationType::GELU => x.gelu()?,
                ActivationType::Swish => x.silu()?,
            };
        }

        // Decoder forward pass
        for (i, layer) in self.decoder.iter().enumerate() {
            x = layer.forward(&x).map_err(|e| {
                Error::model(format!("Decoder forward pass failed at layer {i}: {e}"))
            })?;

            // Apply activation (except for output layer)
            if i < self.decoder.len() - 1 {
                x = match self.config.activation {
                    ActivationType::ReLU => x.relu()?,
                    ActivationType::LeakyReLU => {
                        let scaled = (x.clone() * 0.01)?;
                        x.maximum(&scaled)?
                    }
                    ActivationType::Tanh => x.tanh()?,
                    ActivationType::Sigmoid => {
                        // Implement sigmoid as 1 / (1 + exp(-x))
                        let neg_x = x.neg()?;
                        let exp_neg_x = neg_x.exp()?;
                        let one_plus_exp = (exp_neg_x + 1.0)?;
                        one_plus_exp.recip()?
                    }
                    ActivationType::GELU => x.gelu()?,
                    ActivationType::Swish => x.silu()?,
                };
            }
        }

        Ok(x)
    }

    fn input_shape(&self) -> &[usize] {
        &self.input_shape
    }

    fn output_shape(&self) -> &[usize] {
        &self.output_shape
    }

    fn load_weights(&mut self, _weights: &[u8]) -> Result<()> {
        Ok(())
    }

    fn save_weights(&self) -> Result<Vec<u8>> {
        Ok(vec![0; 2048])
    }

    fn parameter_count(&self) -> u64 {
        // Estimate based on encoder + decoder architecture
        let encoder_params = (self.config.input_dim * self.config.hidden_dim
            + self.config.hidden_dim) as u64
            * (self.config.num_layers / 2) as u64;
        let decoder_params = (self.config.hidden_dim * self.config.output_dim
            + self.config.output_dim) as u64
            * (self.config.num_layers / 2) as u64;
        encoder_params + decoder_params
    }

    fn set_training(&mut self, training: bool) {
        self.training = training;
    }

    fn clone_network(&self) -> Box<dyn NeuralNetwork> {
        Box::new(NeuralVCNetwork {
            encoder: Vec::new(),
            decoder: Vec::new(),
            config: self.config.clone(),
            input_shape: self.input_shape.clone(),
            output_shape: self.output_shape.clone(),
            training: self.training,
        })
    }
}

/// AutoVC network implementation
type AutoVCNetwork = NeuralVCNetwork; // Simplified alias

/// Transformer network implementation  
type TransformerNetwork = SimpleNetwork; // Simplified alias for now

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

    #[test]
    fn test_model_type_properties() {
        assert!(ModelType::NeuralVC.supports_realtime());
        assert!(!ModelType::CycleGAN.supports_realtime());
        assert!(ModelType::Transformer.supports_realtime());
    }

    #[test]
    fn test_model_config_creation() {
        let config = ModelType::NeuralVC.default_config();
        assert_eq!(config.input_dim, 80);
        assert_eq!(config.hidden_dim, 256);
        assert_eq!(config.output_dim, 80);
    }

    #[test]
    fn test_model_creation() {
        let model = ConversionModel::new(ModelType::NeuralVC);
        assert_eq!(model.model_type, ModelType::NeuralVC);
        assert!(!model.parameters_loaded);
    }

    #[tokio::test]
    async fn test_model_processing() {
        let model = ConversionModel::new(ModelType::NeuralVC);
        // Create input with 80 features to match model's expected input dimension
        let input = vec![0.1; 80];

        let result = model.process(&input).await;
        match &result {
            Ok(output) => {
                println!("Test passed successfully, output length: {}", output.len());
                assert_eq!(output.len(), 80, "Output should have same length as input");
            }
            Err(e) => {
                println!("Test failed with error: {e:?}");
            }
        }
        assert!(
            result.is_ok(),
            "Model processing should succeed: {:?}",
            result.err()
        );
    }

    #[test]
    fn test_model_info() {
        let model = ConversionModel::new(ModelType::AutoVC);
        let info = model.info();

        assert_eq!(info.model_type, ModelType::AutoVC);
        assert!(!info.parameters_loaded);
        assert!(info.parameter_count > 0);
    }
}