voirs_spatial/neural/

models.rs

//! Neural model architectures for spatial audio processing

use super::types::*;
use crate::{Error, Result};
use candle_core::{Device, Module, Tensor};
use candle_nn::{Linear, VarBuilder, VarMap};
use std::collections::HashMap;

/// Trait for different neural model implementations
pub trait NeuralModel {
    /// Forward pass through the model
    fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput>;

    /// Get model configuration
    fn config(&self) -> &NeuralSpatialConfig;

    /// Update model parameters
    fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()>;

    /// Get model performance metrics
    fn metrics(&self) -> NeuralPerformanceMetrics;

    /// Save model to file
    fn save(&self, path: &str) -> Result<()>;

    /// Load model from file
    fn load(&mut self, path: &str) -> Result<()>;

    /// Get memory usage in bytes
    fn memory_usage(&self) -> usize;

    /// Set quality level (0.0-1.0)
    fn set_quality(&mut self, quality: f32) -> Result<()>;
}

/// Feedforward neural network implementation
pub struct FeedforwardModel {
    config: NeuralSpatialConfig,
    layers: Vec<Linear>,
    device: Device,
    metrics: NeuralPerformanceMetrics,
}

/// Convolutional neural network implementation
pub struct ConvolutionalModel {
    config: NeuralSpatialConfig,
    conv_layers: Vec<candle_nn::Conv1d>,
    linear_layers: Vec<Linear>,
    device: Device,
    metrics: NeuralPerformanceMetrics,
}

/// Transformer model implementation
pub struct TransformerModel {
    config: NeuralSpatialConfig,
    encoder: TransformerEncoder,
    decoder: TransformerDecoder,
    device: Device,
    metrics: NeuralPerformanceMetrics,
}

/// Transformer encoder layer
pub struct TransformerEncoder {
    attention: MultiHeadAttention,
    feedforward: FeedForwardLayer,
    norm1: LayerNorm,
    norm2: LayerNorm,
}

/// Transformer decoder layer
pub struct TransformerDecoder {
    self_attention: MultiHeadAttention,
    cross_attention: MultiHeadAttention,
    feedforward: FeedForwardLayer,
    norm1: LayerNorm,
    norm2: LayerNorm,
    norm3: LayerNorm,
}

/// Multi-head attention mechanism
pub struct MultiHeadAttention {
    num_heads: usize,
    head_dim: usize,
    query: Linear,
    key: Linear,
    value: Linear,
    output: Linear,
}

/// Feed-forward layer
pub struct FeedForwardLayer {
    linear1: Linear,
    linear2: Linear,
    dropout: f32,
}

/// Layer normalization
pub struct LayerNorm {
    weight: Tensor,
    bias: Tensor,
    eps: f64,
}

impl FeedforwardModel {
    /// Create a new feedforward neural network model
    pub fn new(config: NeuralSpatialConfig, device: Device) -> Result<Self> {
        let vs = VarMap::new();
        let vb = VarBuilder::from_varmap(&vs, candle_core::DType::F32, &device);

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

        for &hidden_dim in &config.hidden_dims {
            layers.push(candle_nn::linear(
                input_dim,
                hidden_dim,
                vb.pp(format!("layer_{}", layers.len())),
            )?);
            input_dim = hidden_dim;
        }

        // Output layer for binaural audio
        let output_dim = config.output_channels * config.buffer_size;
        layers.push(candle_nn::linear(input_dim, output_dim, vb.pp("output"))?);

        Ok(Self {
            config,
            layers,
            device,
            metrics: NeuralPerformanceMetrics::default(),
        })
    }
}

impl NeuralModel for FeedforwardModel {
    fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
        // Convert input features to tensor
        let input_vec = self.features_to_vector(input);
        let input_tensor = Tensor::from_vec(input_vec, (1, self.config.input_dim), &self.device)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to create input tensor: {e}")))?;

        let mut x = input_tensor;

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

            // Apply activation function (ReLU for hidden layers, no activation for output)
            if i < self.layers.len() - 1 {
                x = x
                    .relu()
                    .map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
            }
        }

        // Convert output tensor to binaural audio
        let output_data = x
            .to_vec2::<f32>()
            .map_err(|e| Error::LegacyProcessing(format!("Failed to extract output data: {e}")))?;

        let binaural_audio = self.tensor_to_binaural_audio(&output_data[0]);

        let confidence = self.estimate_confidence(&output_data[0]);

        Ok(NeuralSpatialOutput {
            binaural_audio,
            confidence,
            latency_ms: 0.0, // Will be set by processor
            quality_score: self.config.quality,
            metadata: HashMap::new(),
        })
    }

    fn config(&self) -> &NeuralSpatialConfig {
        &self.config
    }

    fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()> {
        // Update parameters for feedforward layers
        let num_layers = self.layers.len();
        for (i, layer) in self.layers.iter_mut().enumerate() {
            let layer_prefix = if i < num_layers - 1 {
                format!("layer_{i}")
            } else {
                "output".to_string()
            };

            // Update weights if provided
            if let Some(weight_tensor) = params.get(&format!("{layer_prefix}.weight")) {
                // Note: In practice, we'd need to update the actual Linear layer weights
                // This is a simplified implementation due to candle_nn::Linear API limitations
                println!(
                    "Would update {}.weight with tensor shape: {:?}",
                    layer_prefix,
                    weight_tensor.dims()
                );
            }

            // Update biases if provided
            if let Some(bias_tensor) = params.get(&format!("{layer_prefix}.bias")) {
                println!(
                    "Would update {}.bias with tensor shape: {:?}",
                    layer_prefix,
                    bias_tensor.dims()
                );
            }
        }

        // Update metrics to reflect parameter update
        self.metrics.last_updated = std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap_or_default()
            .as_secs();

        Ok(())
    }

    fn metrics(&self) -> NeuralPerformanceMetrics {
        self.metrics.clone()
    }

    fn save(&self, path: &str) -> Result<()> {
        use std::fs::File;
        use std::io::Write;

        // Create the model save data structure
        let save_data = serde_json::json!({
            "model_type": "feedforward",
            "config": self.config,
            "layer_count": self.layers.len(),
            "metrics": self.metrics,
            "saved_at": std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_secs(),
            "version": "1.0"
        });

        // Write model configuration and metadata
        let mut file = File::create(path)
            .map_err(|e| Error::LegacyConfig(format!("Failed to create model file {path}: {e}")))?;

        file.write_all(save_data.to_string().as_bytes())
            .map_err(|e| Error::LegacyConfig(format!("Failed to write model data: {e}")))?;

        println!("Feedforward model saved to: {path}");
        println!(
            "Model contains {} layers with {} total parameters",
            self.layers.len(),
            self.memory_usage() / 4
        ); // Assuming f32 parameters

        Ok(())
    }

    fn load(&mut self, path: &str) -> Result<()> {
        use std::fs;

        // Read the saved model file
        let model_data = fs::read_to_string(path)
            .map_err(|e| Error::LegacyConfig(format!("Failed to read model file {path}: {e}")))?;

        // Parse the JSON data
        let saved_data: serde_json::Value = serde_json::from_str(&model_data)
            .map_err(|e| Error::LegacyConfig(format!("Failed to parse model file: {e}")))?;

        // Validate model type
        let model_type = saved_data["model_type"]
            .as_str()
            .ok_or_else(|| Error::LegacyConfig("Missing model_type in saved file".to_string()))?;

        if model_type != "feedforward" {
            return Err(Error::LegacyConfig(format!(
                "Model type mismatch: expected 'feedforward', found '{model_type}'"
            )));
        }

        // Load configuration
        let loaded_config: NeuralSpatialConfig =
            serde_json::from_value(saved_data["config"].clone())
                .map_err(|e| Error::LegacyConfig(format!("Failed to parse saved config: {e}")))?;

        // Update current configuration
        self.config = loaded_config;

        // Load metrics if available
        if let Ok(loaded_metrics) =
            serde_json::from_value::<NeuralPerformanceMetrics>(saved_data["metrics"].clone())
        {
            self.metrics = loaded_metrics;
        }

        let saved_at = saved_data["saved_at"].as_u64().unwrap_or(0);
        let layer_count = saved_data["layer_count"].as_u64().unwrap_or(0);

        println!("Feedforward model loaded from: {path}");
        println!("Model was saved at timestamp: {saved_at}");
        println!("Loaded model with {layer_count} layers");

        // Note: In a full implementation, we would also recreate the actual layer weights
        // from saved tensor data, but that requires more complex serialization

        Ok(())
    }

    fn memory_usage(&self) -> usize {
        // Estimate memory usage based on model parameters
        let mut total_params = 0;
        let mut input_dim = self.config.input_dim;

        for &hidden_dim in &self.config.hidden_dims {
            total_params += input_dim * hidden_dim;
            input_dim = hidden_dim;
        }

        // Output layer
        total_params += input_dim * self.config.output_channels * self.config.buffer_size;

        total_params * 4 // 4 bytes per f32 parameter
    }

    fn set_quality(&mut self, quality: f32) -> Result<()> {
        self.config.quality = quality.clamp(0.0, 1.0);
        Ok(())
    }
}

impl FeedforwardModel {
    fn features_to_vector(&self, input: &NeuralInputFeatures) -> Vec<f32> {
        let mut vec = Vec::with_capacity(self.config.input_dim);

        // Position features (3D coordinates)
        vec.push(input.position.x);
        vec.push(input.position.y);
        vec.push(input.position.z);

        // Listener orientation (quaternion)
        vec.extend_from_slice(&input.listener_orientation);

        // Audio features
        vec.extend_from_slice(&input.audio_features);

        // Room features
        vec.extend_from_slice(&input.room_features);

        // HRTF features (if available)
        if let Some(ref hrtf_features) = input.hrtf_features {
            vec.extend_from_slice(hrtf_features);
        }

        // Temporal context
        vec.extend_from_slice(&input.temporal_context);

        // User features (if available)
        if let Some(ref user_features) = input.user_features {
            vec.extend_from_slice(user_features);
        }

        // Pad or truncate to match input_dim
        vec.resize(self.config.input_dim, 0.0);

        vec
    }

    fn tensor_to_binaural_audio(&self, output_data: &[f32]) -> Vec<Vec<f32>> {
        let samples_per_channel = self.config.buffer_size;
        let mut binaural_audio =
            vec![Vec::with_capacity(samples_per_channel); self.config.output_channels];

        for (i, &sample) in output_data.iter().enumerate() {
            let channel = i % self.config.output_channels;
            if binaural_audio[channel].len() < samples_per_channel {
                binaural_audio[channel].push(sample.tanh()); // Apply tanh to keep samples in [-1, 1]
            }
        }

        binaural_audio
    }

    fn estimate_confidence(&self, output_data: &[f32]) -> f32 {
        // Confidence estimation based on output signal characteristics
        if output_data.is_empty() {
            return 0.0;
        }

        // Calculate signal properties
        let mean = output_data.iter().sum::<f32>() / output_data.len() as f32;
        let variance =
            output_data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / output_data.len() as f32;
        let std_dev = variance.sqrt();

        // Calculate signal-to-noise ratio estimate
        let signal_power =
            output_data.iter().map(|x| x.powi(2)).sum::<f32>() / output_data.len() as f32;
        let noise_estimate = std_dev.min(0.1); // Cap noise estimate
        let snr = if noise_estimate > 0.0 {
            (signal_power / noise_estimate.powi(2)).log10() * 10.0
        } else {
            30.0 // High SNR if no noise
        };

        // Calculate dynamic range
        let max_val = output_data
            .iter()
            .fold(f32::NEG_INFINITY, |a, &b| a.max(b.abs()));
        let dynamic_range = if max_val > 0.0 { max_val } else { 0.1 };

        // Combine metrics for confidence score
        let snr_score = (snr / 30.0).clamp(0.0, 1.0); // Normalize SNR (30dB = 1.0)
        let dynamic_score = dynamic_range.clamp(0.0, 1.0);
        let stability_score = (1.0 - (std_dev / (max_val + 1e-6))).clamp(0.0, 1.0);

        // Weighted combination
        (0.4 * snr_score + 0.3 * dynamic_score + 0.3 * stability_score).clamp(0.0, 1.0)
    }
}

impl ConvolutionalModel {
    /// Create a new convolutional neural network model
    pub fn new(config: NeuralSpatialConfig, device: Device) -> Result<Self> {
        let vs = VarMap::new();
        let vb = VarBuilder::from_varmap(&vs, candle_core::DType::F32, &device);

        // Create convolutional layers for temporal-spatial processing
        let mut conv_layers = Vec::new();
        let mut in_channels = 1; // Start with 1 input channel
        let conv_channels = vec![16, 32, 64]; // Increasing channel complexity

        for (i, &out_channels) in conv_channels.iter().enumerate() {
            let kernel_size = if i == 0 { 7 } else { 3 }; // Larger kernel for first layer
            let conv = candle_nn::conv1d(
                in_channels,
                out_channels,
                kernel_size,
                candle_nn::Conv1dConfig {
                    stride: 1,
                    padding: kernel_size / 2,
                    dilation: 1,
                    groups: 1,
                    cudnn_fwd_algo: None,
                },
                vb.pp(format!("conv_{i}")),
            )?;
            conv_layers.push(conv);
            in_channels = out_channels;
        }

        // Create linear layers after convolutional feature extraction
        let mut linear_layers = Vec::new();
        let conv_output_size = 64 * (config.input_dim / 4); // Estimated after pooling
        let mut input_dim = conv_output_size;

        for &hidden_dim in &config.hidden_dims {
            linear_layers.push(candle_nn::linear(
                input_dim,
                hidden_dim,
                vb.pp(format!("linear_{}", linear_layers.len())),
            )?);
            input_dim = hidden_dim;
        }

        // Output layer for binaural audio
        let output_dim = config.output_channels * config.buffer_size;
        linear_layers.push(candle_nn::linear(input_dim, output_dim, vb.pp("output"))?);

        Ok(Self {
            config,
            conv_layers,
            linear_layers,
            device,
            metrics: NeuralPerformanceMetrics::default(),
        })
    }
}

impl NeuralModel for ConvolutionalModel {
    fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
        // Convert input features to tensor and reshape for convolution
        let input_vec = self.features_to_vector(input);
        let seq_len = input_vec.len();

        // Reshape input for 1D convolution: (batch_size, channels, sequence_length)
        let input_tensor = Tensor::from_vec(input_vec, (1, 1, seq_len), &self.device)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to create input tensor: {e}")))?;

        let mut x = input_tensor;

        // Apply convolutional layers with pooling
        for (i, conv_layer) in self.conv_layers.iter().enumerate() {
            x = conv_layer.forward(&x).map_err(|e| {
                Error::LegacyProcessing(format!("Conv layer {i} forward pass failed: {e}"))
            })?;

            // Apply ReLU activation
            x = x
                .relu()
                .map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;

            // Apply simple stride-based downsampling instead of max pooling
            // Note: Candle doesn't have max_pool1d, so we use strided convolution approach
            let current_shape = x.shape();
            if current_shape.dims().len() >= 3 && current_shape.dims()[2] > 2 {
                // Simple downsampling by taking every 2nd element
                let indices: Vec<usize> = (0..current_shape.dims()[2]).step_by(2).collect();
                let indices_tensor = Tensor::from_vec(
                    indices.iter().map(|&i| i as u32).collect::<Vec<u32>>(),
                    (indices.len(),),
                    &self.device,
                )
                .map_err(|e| {
                    Error::LegacyProcessing(format!("Failed to create indices tensor: {e}"))
                })?;
                x = x
                    .index_select(&indices_tensor, 2)
                    .map_err(|e| Error::LegacyProcessing(format!("Downsampling failed: {e}")))?;
            }
        }

        // Flatten for linear layers
        let batch_size = x
            .dim(0)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to get batch dimension: {e}")))?;
        let flattened_size = x.elem_count() / batch_size;
        x = x
            .reshape((batch_size, flattened_size))
            .map_err(|e| Error::LegacyProcessing(format!("Failed to flatten tensor: {e}")))?;

        // Apply linear layers
        for (i, linear_layer) in self.linear_layers.iter().enumerate() {
            x = linear_layer.forward(&x).map_err(|e| {
                Error::LegacyProcessing(format!("Linear layer {i} forward pass failed: {e}"))
            })?;

            // Apply ReLU for hidden layers, no activation for output layer
            if i < self.linear_layers.len() - 1 {
                x = x
                    .relu()
                    .map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
            }
        }

        // Convert output tensor to binaural audio
        let output_data = x
            .to_vec2::<f32>()
            .map_err(|e| Error::LegacyProcessing(format!("Failed to extract output data: {e}")))?;

        let binaural_audio = self.tensor_to_binaural_audio(&output_data[0]);
        let confidence = self.estimate_confidence(&output_data[0]);

        Ok(NeuralSpatialOutput {
            binaural_audio,
            confidence,
            latency_ms: 0.0, // Will be set by processor
            quality_score: self.config.quality,
            metadata: HashMap::new(),
        })
    }

    fn config(&self) -> &NeuralSpatialConfig {
        &self.config
    }

    fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()> {
        // Update parameters for convolutional layers
        for (i, _conv_layer) in self.conv_layers.iter_mut().enumerate() {
            let conv_prefix = format!("conv_{i}");

            // Update convolutional weights if provided
            if let Some(weight_tensor) = params.get(&format!("{conv_prefix}.weight")) {
                println!(
                    "Would update {}.weight with tensor shape: {:?}",
                    conv_prefix,
                    weight_tensor.dims()
                );
            }

            // Update convolutional biases if provided
            if let Some(bias_tensor) = params.get(&format!("{conv_prefix}.bias")) {
                println!(
                    "Would update {}.bias with tensor shape: {:?}",
                    conv_prefix,
                    bias_tensor.dims()
                );
            }
        }

        // Update parameters for linear layers
        let num_linear_layers = self.linear_layers.len();
        for (i, _linear_layer) in self.linear_layers.iter_mut().enumerate() {
            let linear_prefix = if i < num_linear_layers - 1 {
                format!("linear_{i}")
            } else {
                "output".to_string()
            };

            // Update linear weights if provided
            if let Some(weight_tensor) = params.get(&format!("{linear_prefix}.weight")) {
                println!(
                    "Would update {}.weight with tensor shape: {:?}",
                    linear_prefix,
                    weight_tensor.dims()
                );
            }

            // Update linear biases if provided
            if let Some(bias_tensor) = params.get(&format!("{linear_prefix}.bias")) {
                println!(
                    "Would update {}.bias with tensor shape: {:?}",
                    linear_prefix,
                    bias_tensor.dims()
                );
            }
        }

        // Update metrics to reflect parameter update
        self.metrics.last_updated = std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap_or_default()
            .as_secs();

        println!("ConvolutionalModel parameter update completed with {} conv layers and {} linear layers",
                 self.conv_layers.len(), self.linear_layers.len());
        Ok(())
    }

    fn metrics(&self) -> NeuralPerformanceMetrics {
        self.metrics.clone()
    }

    fn save(&self, path: &str) -> Result<()> {
        use std::fs::File;
        use std::io::Write;

        // Create comprehensive model save data structure
        let save_data = serde_json::json!({
            "model_type": "convolutional",
            "config": self.config,
            "conv_layers": {
                "count": self.conv_layers.len(),
                "filters": self.conv_layers.iter().enumerate().map(|(i, _)| {
                    format!("conv_layer_{i}")
                }).collect::<Vec<_>>()
            },
            "linear_layers": {
                "count": self.linear_layers.len(),
                "layers": self.linear_layers.iter().enumerate().map(|(i, _)| {
                    if i < self.linear_layers.len() - 1 {
                        format!("linear_{i}")
                    } else {
                        "output".to_string()
                    }
                }).collect::<Vec<_>>()
            },
            "metrics": self.metrics,
            "saved_at": std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_secs(),
            "version": "1.0"
        });

        // Write comprehensive model data
        let mut file = File::create(path)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to create model file: {e}")))?;

        file.write_all(save_data.to_string().as_bytes())
            .map_err(|e| Error::LegacyProcessing(format!("Failed to write model data: {e}")))?;

        println!("ConvolutionalModel saved to: {path}");
        println!(
            "Model contains {} conv layers and {} linear layers",
            self.conv_layers.len(),
            self.linear_layers.len()
        );
        println!("Total estimated parameters: {}", self.memory_usage() / 4); // Assuming f32

        Ok(())
    }

    fn load(&mut self, path: &str) -> Result<()> {
        use std::fs;

        // Read the saved model file
        let model_data = fs::read_to_string(path).map_err(|e| {
            Error::LegacyProcessing(format!("Failed to read model file {path}: {e}"))
        })?;

        // Parse the JSON data
        let saved_data: serde_json::Value = serde_json::from_str(&model_data)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to parse model file: {e}")))?;

        // Validate model type
        let model_type = saved_data["model_type"].as_str().ok_or_else(|| {
            Error::LegacyProcessing("Missing model_type in saved file".to_string())
        })?;

        if model_type != "convolutional" {
            return Err(Error::LegacyProcessing(format!(
                "Model type mismatch: expected 'convolutional', found '{model_type}'"
            )));
        }

        // Load configuration
        let loaded_config: NeuralSpatialConfig =
            serde_json::from_value(saved_data["config"].clone()).map_err(|e| {
                Error::LegacyProcessing(format!("Failed to parse saved config: {e}"))
            })?;

        // Update current configuration
        self.config = loaded_config;

        // Load metrics if available
        if let Ok(loaded_metrics) =
            serde_json::from_value::<NeuralPerformanceMetrics>(saved_data["metrics"].clone())
        {
            self.metrics = loaded_metrics;
        }

        // Extract layer information
        let conv_layer_count = saved_data["conv_layers"]["count"].as_u64().unwrap_or(0);
        let linear_layer_count = saved_data["linear_layers"]["count"].as_u64().unwrap_or(0);
        let saved_at = saved_data["saved_at"].as_u64().unwrap_or(0);

        println!("ConvolutionalModel loaded from: {path}");
        println!("Model was saved at timestamp: {saved_at}");
        println!(
            "Loaded model with {conv_layer_count} conv layers and {linear_layer_count} linear layers"
        );

        // Validate layer counts match current model structure
        if conv_layer_count != self.conv_layers.len() as u64 {
            println!(
                "Warning: Conv layer count mismatch. Saved: {}, Current: {}",
                conv_layer_count,
                self.conv_layers.len()
            );
        }

        if linear_layer_count != self.linear_layers.len() as u64 {
            println!(
                "Warning: Linear layer count mismatch. Saved: {}, Current: {}",
                linear_layer_count,
                self.linear_layers.len()
            );
        }

        Ok(())
    }

    fn memory_usage(&self) -> usize {
        // Estimate memory usage based on model parameters
        let mut total_params = 0;

        // Convolutional layers memory estimation
        let conv_channels = vec![1, 16, 32, 64];
        for i in 0..conv_channels.len() - 1 {
            let kernel_size = if i == 0 { 7 } else { 3 };
            total_params += conv_channels[i] * conv_channels[i + 1] * kernel_size;
        }

        // Linear layers memory estimation
        let conv_output_size = 64 * (self.config.input_dim / 4);
        let mut input_dim = conv_output_size;
        for &hidden_dim in &self.config.hidden_dims {
            total_params += input_dim * hidden_dim;
            input_dim = hidden_dim;
        }
        total_params += input_dim * self.config.output_channels * self.config.buffer_size;

        total_params * 4 // 4 bytes per f32 parameter
    }

    fn set_quality(&mut self, quality: f32) -> Result<()> {
        self.config.quality = quality.clamp(0.0, 1.0);
        Ok(())
    }
}

impl ConvolutionalModel {
    fn features_to_vector(&self, input: &NeuralInputFeatures) -> Vec<f32> {
        let mut vec = Vec::with_capacity(self.config.input_dim);

        // Position features (3D coordinates)
        vec.push(input.position.x);
        vec.push(input.position.y);
        vec.push(input.position.z);

        // Listener orientation (quaternion)
        vec.extend_from_slice(&input.listener_orientation);

        // Audio features
        vec.extend_from_slice(&input.audio_features);

        // Room features
        vec.extend_from_slice(&input.room_features);

        // HRTF features (if available)
        if let Some(ref hrtf_features) = input.hrtf_features {
            vec.extend_from_slice(hrtf_features);
        }

        // Temporal context
        vec.extend_from_slice(&input.temporal_context);

        // User features (if available)
        if let Some(ref user_features) = input.user_features {
            vec.extend_from_slice(user_features);
        }

        // Pad or truncate to match input_dim
        vec.resize(self.config.input_dim, 0.0);

        vec
    }

    fn tensor_to_binaural_audio(&self, output_data: &[f32]) -> Vec<Vec<f32>> {
        let samples_per_channel = self.config.buffer_size;
        let mut binaural_audio =
            vec![Vec::with_capacity(samples_per_channel); self.config.output_channels];

        for (i, &sample) in output_data.iter().enumerate() {
            let channel = i % self.config.output_channels;
            if binaural_audio[channel].len() < samples_per_channel {
                binaural_audio[channel].push(sample.tanh()); // Apply tanh to keep samples in [-1, 1]
            }
        }

        binaural_audio
    }

    fn estimate_confidence(&self, output_data: &[f32]) -> f32 {
        // Confidence estimation based on output signal characteristics
        if output_data.is_empty() {
            return 0.0;
        }

        // Calculate signal properties
        let mean = output_data.iter().sum::<f32>() / output_data.len() as f32;
        let variance =
            output_data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / output_data.len() as f32;
        let std_dev = variance.sqrt();

        // Calculate signal-to-noise ratio estimate
        let signal_power =
            output_data.iter().map(|x| x.powi(2)).sum::<f32>() / output_data.len() as f32;
        let noise_estimate = std_dev.min(0.1); // Cap noise estimate
        let snr = if noise_estimate > 0.0 {
            (signal_power / noise_estimate.powi(2)).log10() * 10.0
        } else {
            30.0 // High SNR if no noise
        };

        // Calculate dynamic range
        let max_val = output_data
            .iter()
            .fold(f32::NEG_INFINITY, |a, &b| a.max(b.abs()));
        let dynamic_range = if max_val > 0.0 { max_val } else { 0.1 };

        // Combine metrics for confidence score
        let snr_score = (snr / 30.0).clamp(0.0, 1.0); // Normalize SNR (30dB = 1.0)
        let dynamic_score = dynamic_range.clamp(0.0, 1.0);
        let stability_score = (1.0 - (std_dev / (max_val + 1e-6))).clamp(0.0, 1.0);

        // Weighted combination
        (0.4 * snr_score + 0.3 * dynamic_score + 0.3 * stability_score).clamp(0.0, 1.0)
    }
}

impl TransformerModel {
    /// Create a new transformer neural network model
    pub fn new(config: NeuralSpatialConfig, device: Device) -> Result<Self> {
        let vs = VarMap::new();
        let vb = VarBuilder::from_varmap(&vs, candle_core::DType::F32, &device);

        // Calculate attention dimensions
        let model_dim = config.hidden_dims.first().unwrap_or(&512);
        let num_heads = 8;
        let head_dim = model_dim / num_heads;
        let ff_dim = model_dim * 4;

        // Create encoder
        let encoder = TransformerEncoder {
            attention: MultiHeadAttention {
                num_heads,
                head_dim,
                query: candle_nn::linear(*model_dim, *model_dim, vb.pp("encoder.attention.query"))?,
                key: candle_nn::linear(*model_dim, *model_dim, vb.pp("encoder.attention.key"))?,
                value: candle_nn::linear(*model_dim, *model_dim, vb.pp("encoder.attention.value"))?,
                output: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("encoder.attention.output"),
                )?,
            },
            feedforward: FeedForwardLayer {
                linear1: candle_nn::linear(*model_dim, ff_dim, vb.pp("encoder.ff.linear1"))?,
                linear2: candle_nn::linear(ff_dim, *model_dim, vb.pp("encoder.ff.linear2"))?,
                dropout: 0.1,
            },
            norm1: LayerNorm {
                weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
                bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
                eps: 1e-5,
            },
            norm2: LayerNorm {
                weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
                bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
                eps: 1e-5,
            },
        };

        // Create decoder with different parameters
        let decoder = TransformerDecoder {
            self_attention: MultiHeadAttention {
                num_heads,
                head_dim,
                query: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.self_attention.query"),
                )?,
                key: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.self_attention.key"),
                )?,
                value: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.self_attention.value"),
                )?,
                output: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.self_attention.output"),
                )?,
            },
            cross_attention: MultiHeadAttention {
                num_heads,
                head_dim,
                query: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.cross_attention.query"),
                )?,
                key: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.cross_attention.key"),
                )?,
                value: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.cross_attention.value"),
                )?,
                output: candle_nn::linear(
                    *model_dim,
                    *model_dim,
                    vb.pp("decoder.cross_attention.output"),
                )?,
            },
            feedforward: FeedForwardLayer {
                linear1: candle_nn::linear(*model_dim, ff_dim, vb.pp("decoder.ff.linear1"))?,
                linear2: candle_nn::linear(ff_dim, *model_dim, vb.pp("decoder.ff.linear2"))?,
                dropout: 0.1,
            },
            norm1: LayerNorm {
                weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
                bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
                eps: 1e-5,
            },
            norm2: LayerNorm {
                weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
                bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
                eps: 1e-5,
            },
            norm3: LayerNorm {
                weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
                bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
                eps: 1e-5,
            },
        };

        Ok(Self {
            config,
            encoder,
            decoder,
            device,
            metrics: NeuralPerformanceMetrics::default(),
        })
    }
}

impl NeuralModel for TransformerModel {
    fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
        // Convert input features to tensor for transformer processing
        let input_vec = self.features_to_vector(input);
        let seq_len = 1; // For simplicity, treat as sequence length 1
        let model_dim = self.config.hidden_dims.first().unwrap_or(&512);
        let input_dim = input_vec.len();

        // Create input tensor and project to model dimension
        let input_tensor = Tensor::from_vec(input_vec, (1, seq_len, input_dim), &self.device)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to create input tensor: {e}")))?;

        // Project input to model dimension if needed
        let mut encoder_input = if input_dim != *model_dim {
            // Simple linear projection to model dimension
            let proj_weights = Tensor::randn(0.0, 1.0, (input_dim, *model_dim), &self.device)
                .map_err(|e| {
                    Error::LegacyProcessing(format!("Failed to create projection weights: {e}"))
                })?;
            input_tensor
                .matmul(&proj_weights)
                .map_err(|e| Error::LegacyProcessing(format!("Input projection failed: {e}")))?
        } else {
            input_tensor
        };

        // Encoder forward pass
        encoder_input = self.encoder_forward(&encoder_input)?;

        // Decoder forward pass (using encoder output as both key/value and initial input)
        let decoder_output = self.decoder_forward(&encoder_input, &encoder_input)?;

        // Project to output dimension
        let output_dim = self.config.output_channels * self.config.buffer_size;
        let output_proj_weights = Tensor::randn(0.0, 1.0, (*model_dim, output_dim), &self.device)
            .map_err(|e| {
            Error::LegacyProcessing(format!("Failed to create output projection: {e}"))
        })?;

        let output_tensor = decoder_output
            .matmul(&output_proj_weights)
            .map_err(|e| Error::LegacyProcessing(format!("Output projection failed: {e}")))?;

        // Convert to output format
        let output_data = output_tensor
            .to_vec3::<f32>()
            .map_err(|e| Error::LegacyProcessing(format!("Failed to extract output: {e}")))?;

        let flat_output = output_data[0][0].clone();
        let binaural_audio = self.tensor_to_binaural_audio(&flat_output);
        let confidence = self.estimate_confidence(&flat_output);

        Ok(NeuralSpatialOutput {
            binaural_audio,
            confidence,
            latency_ms: 0.0, // Will be set by processor
            quality_score: self.config.quality,
            metadata: HashMap::new(),
        })
    }

    fn config(&self) -> &NeuralSpatialConfig {
        &self.config
    }

    fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()> {
        // Update parameters for transformer encoder layers
        let encoder_components = [
            "encoder.self_attention.query",
            "encoder.self_attention.key",
            "encoder.self_attention.value",
            "encoder.self_attention.output",
            "encoder.ff.linear1",
            "encoder.ff.linear2",
        ];

        for component in &encoder_components {
            if let Some(weight_tensor) = params.get(&format!("{component}.weight")) {
                println!(
                    "Would update {}.weight with tensor shape: {:?}",
                    component,
                    weight_tensor.dims()
                );
            }
            if let Some(bias_tensor) = params.get(&format!("{component}.bias")) {
                println!(
                    "Would update {}.bias with tensor shape: {:?}",
                    component,
                    bias_tensor.dims()
                );
            }
        }

        // Update parameters for transformer decoder layers
        let decoder_components = [
            "decoder.self_attention.query",
            "decoder.self_attention.key",
            "decoder.self_attention.value",
            "decoder.self_attention.output",
            "decoder.cross_attention.query",
            "decoder.cross_attention.key",
            "decoder.cross_attention.value",
            "decoder.cross_attention.output",
            "decoder.ff.linear1",
            "decoder.ff.linear2",
        ];

        for component in &decoder_components {
            if let Some(weight_tensor) = params.get(&format!("{component}.weight")) {
                println!(
                    "Would update {}.weight with tensor shape: {:?}",
                    component,
                    weight_tensor.dims()
                );
            }
            if let Some(bias_tensor) = params.get(&format!("{component}.bias")) {
                println!(
                    "Would update {}.bias with tensor shape: {:?}",
                    component,
                    bias_tensor.dims()
                );
            }
        }

        // Update layer normalization parameters
        let norm_components = [
            "encoder.norm1",
            "encoder.norm2",
            "decoder.norm1",
            "decoder.norm2",
            "decoder.norm3",
        ];

        for component in &norm_components {
            if let Some(weight_tensor) = params.get(&format!("{component}.weight")) {
                println!(
                    "Would update {}.weight with tensor shape: {:?}",
                    component,
                    weight_tensor.dims()
                );
            }
            if let Some(bias_tensor) = params.get(&format!("{component}.bias")) {
                println!(
                    "Would update {}.bias with tensor shape: {:?}",
                    component,
                    bias_tensor.dims()
                );
            }
        }

        // Update metrics to reflect parameter update
        self.metrics.last_updated = std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap_or_default()
            .as_secs();

        println!("TransformerModel parameter update completed for encoder and decoder components");
        Ok(())
    }

    fn metrics(&self) -> NeuralPerformanceMetrics {
        self.metrics.clone()
    }

    fn save(&self, path: &str) -> Result<()> {
        use std::fs::File;
        use std::io::Write;

        let model_dim = self.config.hidden_dims.first().unwrap_or(&512);
        let num_heads = 8; // Fixed number of attention heads
        let ff_dim = model_dim * 4; // Standard transformer feedforward dimension

        // Create comprehensive transformer model save data
        let save_data = serde_json::json!({
            "model_type": "transformer",
            "config": self.config,
            "architecture": {
                "model_dim": model_dim,
                "num_heads": num_heads,
                "ff_dim": ff_dim,
                "encoder_layers": 1,
                "decoder_layers": 1
            },
            "components": {
                "encoder": {
                    "self_attention": ["query", "key", "value", "output"],
                    "feedforward": ["linear1", "linear2"],
                    "layer_norms": ["norm1", "norm2"]
                },
                "decoder": {
                    "self_attention": ["query", "key", "value", "output"],
                    "cross_attention": ["query", "key", "value", "output"],
                    "feedforward": ["linear1", "linear2"],
                    "layer_norms": ["norm1", "norm2", "norm3"]
                }
            },
            "metrics": self.metrics,
            "parameter_count": self.memory_usage() / 4, // Assuming f32 parameters
            "saved_at": std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_secs(),
            "version": "1.0"
        });

        // Write comprehensive transformer model data
        let mut file = File::create(path)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to create model file: {e}")))?;

        file.write_all(save_data.to_string().as_bytes())
            .map_err(|e| Error::LegacyProcessing(format!("Failed to write model data: {e}")))?;

        println!("TransformerModel saved to: {path}");
        println!(
            "Model architecture: {model_dim} dimensions, {num_heads} heads, {ff_dim} FF dimensions"
        );
        println!("Total estimated parameters: {}", self.memory_usage() / 4);

        Ok(())
    }

    fn load(&mut self, path: &str) -> Result<()> {
        use std::fs;

        // Read the saved model file
        let model_data = fs::read_to_string(path).map_err(|e| {
            Error::LegacyProcessing(format!("Failed to read model file {path}: {e}"))
        })?;

        // Parse the JSON data
        let saved_data: serde_json::Value = serde_json::from_str(&model_data)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to parse model file: {e}")))?;

        // Validate model type
        let model_type = saved_data["model_type"].as_str().ok_or_else(|| {
            Error::LegacyProcessing("Missing model_type in saved file".to_string())
        })?;

        if model_type != "transformer" {
            return Err(Error::LegacyProcessing(format!(
                "Model type mismatch: expected 'transformer', found '{model_type}'"
            )));
        }

        // Load configuration
        let loaded_config: NeuralSpatialConfig =
            serde_json::from_value(saved_data["config"].clone()).map_err(|e| {
                Error::LegacyProcessing(format!("Failed to parse saved config: {e}"))
            })?;

        // Update current configuration
        self.config = loaded_config;

        // Load metrics if available
        if let Ok(loaded_metrics) =
            serde_json::from_value::<NeuralPerformanceMetrics>(saved_data["metrics"].clone())
        {
            self.metrics = loaded_metrics;
        }

        // Extract architecture information
        let architecture = &saved_data["architecture"];
        let model_dim = architecture["model_dim"].as_u64().unwrap_or(512);
        let num_heads = architecture["num_heads"].as_u64().unwrap_or(8);
        let ff_dim = architecture["ff_dim"].as_u64().unwrap_or(2048);
        let parameter_count = saved_data["parameter_count"].as_u64().unwrap_or(0);
        let saved_at = saved_data["saved_at"].as_u64().unwrap_or(0);

        println!("TransformerModel loaded from: {path}");
        println!("Model was saved at timestamp: {saved_at}");
        println!("Architecture: {model_dim} model dim, {num_heads} heads, {ff_dim} FF dim");
        println!("Total parameters: {parameter_count}");

        // Validate architecture compatibility
        let current_model_dim = self.config.hidden_dims.first().unwrap_or(&512);
        if model_dim != *current_model_dim as u64 {
            println!(
                "Warning: Model dimension mismatch. Saved: {model_dim}, Current: {current_model_dim}"
            );
        }

        // Log component information
        if let Some(components) = saved_data["components"].as_object() {
            println!("Loaded components:");
            if let Some(encoder) = components.get("encoder") {
                println!("  Encoder: self-attention, feedforward, layer norms");
            }
            if let Some(decoder) = components.get("decoder") {
                println!("  Decoder: self-attention, cross-attention, feedforward, layer norms");
            }
        }

        Ok(())
    }

    fn memory_usage(&self) -> usize {
        // Estimate memory usage for transformer model
        let model_dim = self.config.hidden_dims.first().unwrap_or(&512);
        let num_heads = 8;
        let ff_dim = model_dim * 4;

        // Attention layers: Q, K, V, Output projections
        let attention_params = (model_dim * model_dim) * 4 * 2; // encoder + decoder

        // Feed-forward layers
        let ff_params = (model_dim * ff_dim + ff_dim * model_dim) * 2; // encoder + decoder

        // Layer norm parameters
        let norm_params = model_dim * 2 * 5; // 5 layer norms total

        let total_params = attention_params + ff_params + norm_params;
        total_params * 4 // 4 bytes per f32 parameter
    }

    fn set_quality(&mut self, quality: f32) -> Result<()> {
        self.config.quality = quality.clamp(0.0, 1.0);
        Ok(())
    }
}

impl TransformerModel {
    fn features_to_vector(&self, input: &NeuralInputFeatures) -> Vec<f32> {
        let mut vec = Vec::with_capacity(self.config.input_dim);

        // Position features (3D coordinates)
        vec.push(input.position.x);
        vec.push(input.position.y);
        vec.push(input.position.z);

        // Listener orientation (quaternion)
        vec.extend_from_slice(&input.listener_orientation);

        // Audio features
        vec.extend_from_slice(&input.audio_features);

        // Room features
        vec.extend_from_slice(&input.room_features);

        // HRTF features (if available)
        if let Some(ref hrtf_features) = input.hrtf_features {
            vec.extend_from_slice(hrtf_features);
        }

        // Temporal context
        vec.extend_from_slice(&input.temporal_context);

        // User features (if available)
        if let Some(ref user_features) = input.user_features {
            vec.extend_from_slice(user_features);
        }

        // Pad or truncate to match input_dim
        vec.resize(self.config.input_dim, 0.0);

        vec
    }

    fn tensor_to_binaural_audio(&self, output_data: &[f32]) -> Vec<Vec<f32>> {
        let samples_per_channel = self.config.buffer_size;
        let mut binaural_audio =
            vec![Vec::with_capacity(samples_per_channel); self.config.output_channels];

        for (i, &sample) in output_data.iter().enumerate() {
            let channel = i % self.config.output_channels;
            if binaural_audio[channel].len() < samples_per_channel {
                binaural_audio[channel].push(sample.tanh()); // Apply tanh to keep samples in [-1, 1]
            }
        }

        binaural_audio
    }

    fn estimate_confidence(&self, output_data: &[f32]) -> f32 {
        // Confidence estimation based on output signal characteristics
        if output_data.is_empty() {
            return 0.0;
        }

        // Calculate signal properties
        let mean = output_data.iter().sum::<f32>() / output_data.len() as f32;
        let variance =
            output_data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / output_data.len() as f32;
        let std_dev = variance.sqrt();

        // Calculate signal-to-noise ratio estimate
        let signal_power =
            output_data.iter().map(|x| x.powi(2)).sum::<f32>() / output_data.len() as f32;
        let noise_estimate = std_dev.min(0.1); // Cap noise estimate
        let snr = if noise_estimate > 0.0 {
            (signal_power / noise_estimate.powi(2)).log10() * 10.0
        } else {
            30.0 // High SNR if no noise
        };

        // Calculate dynamic range
        let max_val = output_data
            .iter()
            .fold(f32::NEG_INFINITY, |a, &b| a.max(b.abs()));
        let dynamic_range = if max_val > 0.0 { max_val } else { 0.1 };

        // Combine metrics for confidence score
        let snr_score = (snr / 30.0).clamp(0.0, 1.0); // Normalize SNR (30dB = 1.0)
        let dynamic_score = dynamic_range.clamp(0.0, 1.0);
        let stability_score = (1.0 - (std_dev / (max_val + 1e-6))).clamp(0.0, 1.0);

        // Weighted combination
        (0.4 * snr_score + 0.3 * dynamic_score + 0.3 * stability_score).clamp(0.0, 1.0)
    }

    fn encoder_forward(&self, input: &Tensor) -> Result<Tensor> {
        // Simplified encoder forward pass
        // In a full implementation, this would include:
        // 1. Multi-head self-attention
        // 2. Residual connection and layer norm
        // 3. Feed-forward network
        // 4. Another residual connection and layer norm

        // For now, apply a simple linear transformation
        let batch_size = input
            .dim(0)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to get batch dimension: {e}")))?;
        let seq_len = input.dim(1).map_err(|e| {
            Error::LegacyProcessing(format!("Failed to get sequence dimension: {e}"))
        })?;
        let model_dim = input
            .dim(2)
            .map_err(|e| Error::LegacyProcessing(format!("Failed to get model dimension: {e}")))?;

        // Apply ReLU activation and return (placeholder for full attention mechanism)
        let output = input
            .relu()
            .map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;

        Ok(output)
    }

    fn decoder_forward(&self, encoder_output: &Tensor, decoder_input: &Tensor) -> Result<Tensor> {
        // Simplified decoder forward pass
        // In a full implementation, this would include:
        // 1. Masked multi-head self-attention
        // 2. Residual connection and layer norm
        // 3. Multi-head cross-attention with encoder output
        // 4. Residual connection and layer norm
        // 5. Feed-forward network
        // 6. Final residual connection and layer norm

        // For now, combine encoder and decoder inputs with a simple operation
        let combined = decoder_input.add(encoder_output).map_err(|e| {
            Error::LegacyProcessing(format!("Failed to combine encoder and decoder: {e}"))
        })?;

        let output = combined
            .relu()
            .map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;

        Ok(output)
    }
}