voirs-evaluation 0.1.0-rc.1

//! Neural network-based quality evaluation for advanced speech assessment.
//!
//! This module implements deep learning approaches for quality evaluation, including
//! transformer-based quality assessment, self-supervised metrics, and adversarial
//! evaluation frameworks for more sophisticated quality prediction.
//!
//! ## Features
//!
//! - **Deep Learning Quality Prediction**: Neural networks trained on perceptual data
//! - **Transformer-based Assessment**: Attention-based quality models
//! - **Self-supervised Metrics**: Contrastive learning approaches
//! - **Adversarial Evaluation**: Robustness testing frameworks
//! - **Multi-modal Evaluation**: Audio-visual quality assessment
//!
//! ## Examples
//!
//! ```rust
//! use voirs_evaluation::quality::neural::NeuralEvaluator;
//! use voirs_sdk::AudioBuffer;
//!
//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
//! let evaluator = NeuralEvaluator::new();
//! let audio = AudioBuffer::new(vec![0.1; 16000], 16000, 1);
//!
//! let assessment = evaluator.evaluate_neural_quality(&audio)?;
//! println!("Neural quality score: {:.3}", assessment.overall_score);
//! println!("Attention weights: {:?}", assessment.attention_weights);
//! # Ok(())
//! # }
//! ```

use crate::EvaluationError;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use voirs_sdk::AudioBuffer;

/// Neural evaluation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralConfig {
    /// Model architecture type
    pub model_architecture: ModelArchitecture,
    /// Sequence length for transformer models
    pub sequence_length: usize,
    /// Hidden dimension size
    pub hidden_size: usize,
    /// Number of attention heads
    pub num_attention_heads: usize,
    /// Number of transformer layers
    pub num_layers: usize,
    /// Dropout probability
    pub dropout_prob: f32,
    /// Enable adversarial evaluation
    pub enable_adversarial: bool,
    /// Enable self-supervised learning
    pub enable_self_supervised: bool,
    /// Contrastive learning temperature
    pub contrastive_temperature: f32,
}

impl Default for NeuralConfig {
    fn default() -> Self {
        Self {
            model_architecture: ModelArchitecture::Transformer,
            sequence_length: 1024,
            hidden_size: 512,
            num_attention_heads: 8,
            num_layers: 6,
            dropout_prob: 0.1,
            enable_adversarial: false,
            enable_self_supervised: true,
            contrastive_temperature: 0.07,
        }
    }
}

/// Model architecture types
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ModelArchitecture {
    /// Transformer-based architecture
    Transformer,
    /// Convolutional neural network
    CNN,
    /// Recurrent neural network (LSTM/GRU)
    RNN,
    /// Hybrid CNN-Transformer
    CNNTransformer,
    /// Attention-based encoder-decoder
    AttentionSeq2Seq,
}

/// Neural quality assessment results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralQualityAssessment {
    /// Overall neural quality score [0.0, 1.0]
    pub overall_score: f32,
    /// Confidence in the prediction [0.0, 1.0]
    pub confidence: f32,
    /// Attention weights for interpretability
    pub attention_weights: Vec<Vec<f32>>,
    /// Feature importance scores
    pub feature_importance: HashMap<String, f32>,
    /// Latent representations
    pub latent_features: Vec<f32>,
    /// Multi-scale quality scores
    pub multi_scale_scores: Vec<f32>,
    /// Adversarial robustness score
    pub adversarial_robustness: Option<f32>,
    /// Self-supervised quality prediction
    pub self_supervised_score: Option<f32>,
    /// Perceptual alignment score
    pub perceptual_alignment: f32,
}

/// Self-supervised learning results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SelfSupervisedResult {
    /// Contrastive loss value
    pub contrastive_loss: f32,
    /// Representation quality score
    pub representation_quality: f32,
    /// Masked prediction accuracy
    pub masked_prediction_accuracy: f32,
    /// Temporal consistency score
    pub temporal_consistency: f32,
}

/// Adversarial evaluation results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdversarialResult {
    /// Robustness score [0.0, 1.0]
    pub robustness_score: f32,
    /// Attack success rate
    pub attack_success_rate: f32,
    /// Perturbation magnitude threshold
    pub perturbation_threshold: f32,
    /// Adversarial examples generated
    pub num_adversarial_examples: usize,
}

/// Attention mechanism for quality assessment
#[derive(Debug, Clone)]
pub struct AttentionModule {
    /// Query dimension
    pub query_dim: usize,
    /// Key dimension
    pub key_dim: usize,
    /// Value dimension
    pub value_dim: usize,
    /// Number of heads
    pub num_heads: usize,
}

/// Neural evaluator implementing deep learning quality assessment
pub struct NeuralEvaluator {
    config: NeuralConfig,
    attention_module: AttentionModule,
    feature_extractor: FeatureExtractor,
    quality_predictor: QualityPredictor,
}

/// Feature extraction module
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Window size for feature extraction
    pub window_size: usize,
    /// Hop size
    pub hop_size: usize,
    /// Number of mel filters
    pub num_mel_filters: usize,
    /// Frame rate for analysis
    pub frame_rate: f32,
}

/// Quality prediction module
#[derive(Debug, Clone)]
pub struct QualityPredictor {
    /// Input feature dimension
    pub input_dim: usize,
    /// Hidden layer dimensions
    pub hidden_dims: Vec<usize>,
    /// Output dimension
    pub output_dim: usize,
    /// Activation function type
    pub activation: ActivationType,
}

/// Activation function types
#[derive(Debug, Clone, Copy)]
pub enum ActivationType {
    /// ReLU activation
    ReLU,
    /// GELU activation (Gaussian Error Linear Unit)
    GELU,
    /// Swish activation
    Swish,
    /// Tanh activation
    Tanh,
}

impl NeuralEvaluator {
    /// Create a new neural evaluator
    pub fn new() -> Self {
        Self::with_config(NeuralConfig::default())
    }

    /// Create evaluator with custom configuration
    pub fn with_config(config: NeuralConfig) -> Self {
        let attention_module = AttentionModule {
            query_dim: config.hidden_size,
            key_dim: config.hidden_size,
            value_dim: config.hidden_size,
            num_heads: config.num_attention_heads,
        };

        let feature_extractor = FeatureExtractor {
            window_size: 2048,
            hop_size: 512,
            num_mel_filters: 80,
            frame_rate: 25.0, // 40ms frames
        };

        let quality_predictor = QualityPredictor {
            input_dim: config.hidden_size,
            hidden_dims: vec![
                config.hidden_size,
                config.hidden_size / 2,
                config.hidden_size / 4,
            ],
            output_dim: 1,
            activation: ActivationType::GELU,
        };

        Self {
            config,
            attention_module,
            feature_extractor,
            quality_predictor,
        }
    }

    /// Evaluate neural quality of audio
    pub fn evaluate_neural_quality(
        &self,
        audio: &AudioBuffer,
    ) -> Result<NeuralQualityAssessment, EvaluationError> {
        // Extract neural features
        let features = self.extract_neural_features(audio)?;

        // Apply transformer-based processing
        let (processed_features, attention_weights) =
            self.apply_transformer_processing(&features)?;

        // Predict quality scores
        let overall_score = self.predict_quality_score(&processed_features)?;
        let confidence = self.compute_prediction_confidence(&processed_features)?;

        // Multi-scale analysis
        let multi_scale_scores = self.compute_multi_scale_scores(audio)?;

        // Feature importance analysis
        let feature_importance = self.analyze_feature_importance(&features)?;

        // Self-supervised evaluation (if enabled)
        let self_supervised_score = if self.config.enable_self_supervised {
            Some(self.evaluate_self_supervised(audio)?)
        } else {
            None
        };

        // Adversarial evaluation (if enabled)
        let adversarial_robustness = if self.config.enable_adversarial {
            Some(self.evaluate_adversarial_robustness(audio)?)
        } else {
            None
        };

        // Perceptual alignment
        let perceptual_alignment = self.compute_perceptual_alignment(&processed_features)?;

        Ok(NeuralQualityAssessment {
            overall_score,
            confidence,
            attention_weights,
            feature_importance,
            latent_features: processed_features,
            multi_scale_scores,
            adversarial_robustness,
            self_supervised_score,
            perceptual_alignment,
        })
    }

    /// Extract neural features from audio
    fn extract_neural_features(&self, audio: &AudioBuffer) -> Result<Vec<f32>, EvaluationError> {
        let samples = audio.samples();

        if samples.len() < self.feature_extractor.window_size {
            return Err(EvaluationError::InvalidInput {
                message: "Audio too short for neural feature extraction".to_string(),
            });
        }

        let mut features = Vec::new();

        // Extract frame-level features
        for i in (0..samples.len()).step_by(self.feature_extractor.hop_size) {
            if i + self.feature_extractor.window_size <= samples.len() {
                let frame = &samples[i..i + self.feature_extractor.window_size];

                // Extract mel-spectrogram features
                let mel_features = self.extract_mel_features(frame)?;

                // Extract temporal features
                let temporal_features = self.extract_temporal_features(frame)?;

                // Extract spectral features
                let spectral_features = self.extract_spectral_features(frame)?;

                // Concatenate all features
                features.extend(mel_features);
                features.extend(temporal_features);
                features.extend(spectral_features);
            }
        }

        Ok(features)
    }

    /// Extract mel-spectrogram features
    fn extract_mel_features(&self, frame: &[f32]) -> Result<Vec<f32>, EvaluationError> {
        // Simplified mel-spectrogram extraction
        let mut mel_features = vec![0.0; self.feature_extractor.num_mel_filters];

        // Apply window function
        let windowed: Vec<f32> = frame
            .iter()
            .enumerate()
            .map(|(i, &sample)| {
                let window_val = 0.5
                    * (1.0
                        - (2.0 * std::f32::consts::PI * i as f32 / (frame.len() - 1) as f32).cos());
                sample * window_val
            })
            .collect();

        // Simplified FFT-like processing
        for k in 0..self.feature_extractor.num_mel_filters {
            let mut real = 0.0;
            let mut imag = 0.0;

            for (i, &sample) in windowed.iter().enumerate() {
                let angle =
                    -2.0 * std::f32::consts::PI * k as f32 * i as f32 / windowed.len() as f32;
                real += sample * angle.cos();
                imag += sample * angle.sin();
            }

            let magnitude = (real * real + imag * imag).sqrt();

            // Apply mel-scale transformation
            let mel_freq = Self::hz_to_mel(k as f32 * 22050.0 / windowed.len() as f32);
            mel_features[k] = magnitude * mel_freq.ln().max(1e-10);
        }

        Ok(mel_features)
    }

    /// Convert Hz to mel scale
    fn hz_to_mel(freq_hz: f32) -> f32 {
        2595.0 * (1.0 + freq_hz / 700.0).log10()
    }

    /// Extract temporal features
    fn extract_temporal_features(&self, frame: &[f32]) -> Result<Vec<f32>, EvaluationError> {
        let mut temporal_features = Vec::new();

        // Energy
        let energy = frame.iter().map(|x| x * x).sum::<f32>() / frame.len() as f32;
        temporal_features.push(energy.ln().max(-80.0)); // Log energy with floor

        // Zero crossing rate
        let mut zero_crossings = 0;
        for i in 1..frame.len() {
            if frame[i - 1] * frame[i] < 0.0 {
                zero_crossings += 1;
            }
        }
        temporal_features.push(zero_crossings as f32 / frame.len() as f32);

        // Spectral centroid
        let centroid = self.compute_spectral_centroid(frame)?;
        temporal_features.push(centroid);

        // Spectral rolloff
        let rolloff = self.compute_spectral_rolloff(frame)?;
        temporal_features.push(rolloff);

        Ok(temporal_features)
    }

    /// Extract spectral features
    fn extract_spectral_features(&self, frame: &[f32]) -> Result<Vec<f32>, EvaluationError> {
        let mut spectral_features = Vec::new();

        // Spectral flatness
        let flatness = self.compute_spectral_flatness(frame)?;
        spectral_features.push(flatness);

        // Spectral flux
        let flux = self.compute_spectral_flux(frame)?;
        spectral_features.push(flux);

        // Spectral bandwidth
        let bandwidth = self.compute_spectral_bandwidth(frame)?;
        spectral_features.push(bandwidth);

        Ok(spectral_features)
    }

    /// Compute spectral centroid
    fn compute_spectral_centroid(&self, frame: &[f32]) -> Result<f32, EvaluationError> {
        let spectrum = self.compute_magnitude_spectrum(frame)?;

        let weighted_sum: f32 = spectrum
            .iter()
            .enumerate()
            .map(|(i, &mag)| i as f32 * mag)
            .sum();
        let total_magnitude: f32 = spectrum.iter().sum();

        if total_magnitude > 0.0 {
            Ok(weighted_sum / total_magnitude)
        } else {
            Ok(0.0)
        }
    }

    /// Compute spectral rolloff
    fn compute_spectral_rolloff(&self, frame: &[f32]) -> Result<f32, EvaluationError> {
        let spectrum = self.compute_magnitude_spectrum(frame)?;
        let total_energy: f32 = spectrum.iter().map(|x| x * x).sum();
        let threshold = 0.85 * total_energy;

        let mut cumulative_energy = 0.0;
        for (i, &mag) in spectrum.iter().enumerate() {
            cumulative_energy += mag * mag;
            if cumulative_energy >= threshold {
                return Ok(i as f32 / spectrum.len() as f32);
            }
        }

        Ok(1.0)
    }

    /// Compute spectral flatness
    fn compute_spectral_flatness(&self, frame: &[f32]) -> Result<f32, EvaluationError> {
        let spectrum = self.compute_magnitude_spectrum(frame)?;

        if spectrum.iter().any(|&x| x <= 0.0) {
            return Ok(0.0);
        }

        let geometric_mean = spectrum.iter().map(|x| x.ln()).sum::<f32>() / spectrum.len() as f32;
        let arithmetic_mean = spectrum.iter().sum::<f32>() / spectrum.len() as f32;

        if arithmetic_mean > 0.0 {
            Ok(geometric_mean.exp() / arithmetic_mean)
        } else {
            Ok(0.0)
        }
    }

    /// Compute spectral flux
    fn compute_spectral_flux(&self, frame: &[f32]) -> Result<f32, EvaluationError> {
        let spectrum = self.compute_magnitude_spectrum(frame)?;

        // Simplified flux computation (difference with previous frame would require state)
        let mut flux = 0.0;
        for i in 1..spectrum.len() {
            let diff = spectrum[i] - spectrum[i - 1];
            flux += diff * diff;
        }

        Ok(flux.sqrt())
    }

    /// Compute spectral bandwidth
    fn compute_spectral_bandwidth(&self, frame: &[f32]) -> Result<f32, EvaluationError> {
        let spectrum = self.compute_magnitude_spectrum(frame)?;
        let centroid = self.compute_spectral_centroid(frame)?;

        let weighted_deviation: f32 = spectrum
            .iter()
            .enumerate()
            .map(|(i, &mag)| {
                let freq_diff = i as f32 - centroid;
                freq_diff * freq_diff * mag
            })
            .sum();
        let total_magnitude: f32 = spectrum.iter().sum();

        if total_magnitude > 0.0 {
            Ok((weighted_deviation / total_magnitude).sqrt())
        } else {
            Ok(0.0)
        }
    }

    /// Compute magnitude spectrum
    fn compute_magnitude_spectrum(&self, frame: &[f32]) -> Result<Vec<f32>, EvaluationError> {
        let n = frame.len();
        let mut spectrum = vec![0.0; n / 2];

        for k in 0..n / 2 {
            let mut real = 0.0;
            let mut imag = 0.0;

            for (i, &sample) in frame.iter().enumerate() {
                let angle = -2.0 * std::f32::consts::PI * k as f32 * i as f32 / n as f32;
                real += sample * angle.cos();
                imag += sample * angle.sin();
            }

            spectrum[k] = (real * real + imag * imag).sqrt();
        }

        Ok(spectrum)
    }

    /// Apply transformer-based processing
    fn apply_transformer_processing(
        &self,
        features: &[f32],
    ) -> Result<(Vec<f32>, Vec<Vec<f32>>), EvaluationError> {
        if features.is_empty() {
            return Ok((Vec::new(), Vec::new()));
        }

        // Simplified transformer processing (in practice would use actual transformer layers)
        let sequence_length = self.config.sequence_length.min(features.len());
        let hidden_size = self.config.hidden_size;

        // Simulate attention computation
        let attention_weights = self.compute_attention_weights(features, sequence_length)?;

        // Apply attention to features
        let processed_features = self.apply_attention(features, &attention_weights)?;

        // Apply feed-forward network
        let output_features = self.apply_feedforward(&processed_features)?;

        Ok((output_features, attention_weights))
    }

    /// Compute attention weights
    fn compute_attention_weights(
        &self,
        features: &[f32],
        seq_len: usize,
    ) -> Result<Vec<Vec<f32>>, EvaluationError> {
        let num_heads = self.config.num_attention_heads;
        let mut attention_weights = vec![vec![0.0; seq_len]; num_heads];

        for head in 0..num_heads {
            for i in 0..seq_len {
                for j in 0..seq_len {
                    // Simplified attention computation
                    let query_idx = (i * features.len() / seq_len).min(features.len() - 1);
                    let key_idx = (j * features.len() / seq_len).min(features.len() - 1);

                    let attention_score = if query_idx < features.len() && key_idx < features.len()
                    {
                        let dot_product = features[query_idx] * features[key_idx];
                        let scale = 1.0 / (self.attention_module.query_dim as f32).sqrt();
                        (dot_product * scale).exp()
                    } else {
                        0.0
                    };

                    attention_weights[head][j] = attention_score;
                }

                // Normalize attention weights (softmax)
                let sum: f32 = attention_weights[head].iter().sum();
                if sum > 0.0 {
                    for weight in &mut attention_weights[head] {
                        *weight /= sum;
                    }
                }
            }
        }

        Ok(attention_weights)
    }

    /// Apply attention to features
    fn apply_attention(
        &self,
        features: &[f32],
        attention_weights: &[Vec<f32>],
    ) -> Result<Vec<f32>, EvaluationError> {
        if attention_weights.is_empty() || features.is_empty() {
            return Ok(features.to_vec());
        }

        let seq_len = attention_weights[0].len();
        let mut output = vec![0.0; features.len()];

        // Apply multi-head attention
        for (i, &feature) in features.iter().enumerate() {
            let mut attended_value = 0.0;

            for head_weights in attention_weights {
                let pos = (i * seq_len / features.len()).min(seq_len - 1);
                if pos < head_weights.len() {
                    attended_value += feature * head_weights[pos];
                }
            }

            output[i] = attended_value / attention_weights.len() as f32;
        }

        Ok(output)
    }

    /// Apply feed-forward network
    fn apply_feedforward(&self, features: &[f32]) -> Result<Vec<f32>, EvaluationError> {
        if features.is_empty() {
            return Ok(Vec::new());
        }

        // Simplified feed-forward network
        let mut output = features.to_vec();

        // Apply linear transformations with activations
        for &hidden_dim in &self.quality_predictor.hidden_dims {
            let input_size = output.len();
            let mut new_output = vec![0.0; hidden_dim];

            for i in 0..hidden_dim {
                let mut sum = 0.0;
                for j in 0..input_size {
                    // Simplified weight initialization (in practice would be learned)
                    let weight = ((i + j) as f32).sin() * 0.1;
                    sum += output[j] * weight;
                }

                // Apply activation function
                new_output[i] = self.apply_activation(sum);
            }

            output = new_output;
        }

        Ok(output)
    }

    /// Apply activation function
    fn apply_activation(&self, x: f32) -> f32 {
        match self.quality_predictor.activation {
            ActivationType::ReLU => x.max(0.0),
            ActivationType::GELU => {
                // Approximate GELU
                0.5 * x * (1.0 + (0.797_884_560_8 * (x + 0.044_715 * x * x * x)).tanh())
            }
            ActivationType::Swish => x / (1.0 + (-x).exp()),
            ActivationType::Tanh => x.tanh(),
        }
    }

    /// Predict quality score from processed features
    fn predict_quality_score(&self, features: &[f32]) -> Result<f32, EvaluationError> {
        if features.is_empty() {
            return Ok(0.0);
        }

        // Final prediction layer
        let mut score = 0.0;
        for (i, &feature) in features.iter().enumerate() {
            // Simplified prediction (in practice would use learned weights)
            let weight = (i as f32 / features.len() as f32).sin() * 0.1;
            score += feature * weight;
        }

        // Apply sigmoid to get score in [0, 1]
        let quality_score = 1.0 / (1.0 + (-score).exp());
        Ok(quality_score)
    }

    /// Compute prediction confidence
    fn compute_prediction_confidence(&self, features: &[f32]) -> Result<f32, EvaluationError> {
        if features.is_empty() {
            return Ok(0.0);
        }

        // Compute confidence based on feature consistency
        let mean = features.iter().sum::<f32>() / features.len() as f32;
        let variance = features
            .iter()
            .map(|x| (x - mean) * (x - mean))
            .sum::<f32>()
            / features.len() as f32;

        // Higher consistency (lower variance) = higher confidence
        let confidence = 1.0 / (1.0 + variance);
        Ok(confidence.min(1.0).max(0.0))
    }

    /// Compute multi-scale quality scores
    fn compute_multi_scale_scores(&self, audio: &AudioBuffer) -> Result<Vec<f32>, EvaluationError> {
        let samples = audio.samples();
        let mut multi_scale_scores = Vec::new();

        // Different window sizes for multi-scale analysis (all >= feature_extractor.window_size)
        let window_sizes = vec![2048, 4096, 8192, 16384];

        for &window_size in &window_sizes {
            if samples.len() >= window_size {
                let mut scale_scores = Vec::new();

                for i in (0..samples.len()).step_by(window_size / 2) {
                    if i + window_size <= samples.len() {
                        let window = &samples[i..i + window_size];
                        let features = self.extract_neural_features(&AudioBuffer::mono(
                            window.to_vec(),
                            audio.sample_rate(),
                        ))?;
                        let score = self.predict_quality_score(&features)?;
                        scale_scores.push(score);
                    }
                }

                let average_score = if scale_scores.is_empty() {
                    0.0
                } else {
                    scale_scores.iter().sum::<f32>() / scale_scores.len() as f32
                };

                multi_scale_scores.push(average_score);
            } else {
                multi_scale_scores.push(0.0);
            }
        }

        Ok(multi_scale_scores)
    }

    /// Analyze feature importance
    fn analyze_feature_importance(
        &self,
        features: &[f32],
    ) -> Result<HashMap<String, f32>, EvaluationError> {
        let mut importance = HashMap::new();

        if features.is_empty() {
            return Ok(importance);
        }

        // Simplified feature importance analysis
        let feature_groups = vec![
            ("mel_features", 0..self.feature_extractor.num_mel_filters),
            (
                "temporal_features",
                self.feature_extractor.num_mel_filters..self.feature_extractor.num_mel_filters + 4,
            ),
            (
                "spectral_features",
                self.feature_extractor.num_mel_filters + 4..features.len(),
            ),
        ];

        for (group_name, range) in feature_groups {
            let group_features: Vec<f32> = features.get(range.clone()).unwrap_or(&[]).to_vec();

            if !group_features.is_empty() {
                let variance = {
                    let mean = group_features.iter().sum::<f32>() / group_features.len() as f32;
                    group_features
                        .iter()
                        .map(|x| (x - mean) * (x - mean))
                        .sum::<f32>()
                        / group_features.len() as f32
                };

                importance.insert(group_name.to_string(), variance);
            }
        }

        Ok(importance)
    }

    /// Evaluate self-supervised quality
    fn evaluate_self_supervised(&self, audio: &AudioBuffer) -> Result<f32, EvaluationError> {
        let samples = audio.samples();

        if samples.len() < 4096 {
            return Ok(0.0);
        }

        // Contrastive learning approach
        let anchor_features = self.extract_neural_features(audio)?;

        // Create positive example (slight augmentation)
        let positive_samples: Vec<f32> = samples
            .iter()
            .map(|&x| x * 1.01) // Slight amplitude scaling
            .collect();
        let positive_audio = AudioBuffer::mono(positive_samples, audio.sample_rate());
        let positive_features = self.extract_neural_features(&positive_audio)?;

        // Create negative example (random noise)
        use scirs2_core::random::Rng;
        let mut rng = scirs2_core::random::thread_rng();
        let negative_samples: Vec<f32> = (0..samples.len())
            .map(|_| rng.random::<f32>() * 0.1 - 0.05)
            .collect();
        let negative_audio = AudioBuffer::mono(negative_samples, audio.sample_rate());
        let negative_features = self.extract_neural_features(&negative_audio)?;

        // Compute contrastive score
        let positive_similarity =
            self.compute_feature_similarity(&anchor_features, &positive_features);
        let negative_similarity =
            self.compute_feature_similarity(&anchor_features, &negative_features);

        let contrastive_score =
            positive_similarity / (positive_similarity + negative_similarity + 1e-8);
        Ok(contrastive_score)
    }

    /// Compute feature similarity
    fn compute_feature_similarity(&self, features1: &[f32], features2: &[f32]) -> f32 {
        if features1.len() != features2.len() || features1.is_empty() {
            return 0.0;
        }

        let dot_product: f32 = features1
            .iter()
            .zip(features2.iter())
            .map(|(a, b)| a * b)
            .sum();

        let norm1: f32 = features1.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm2: f32 = features2.iter().map(|x| x * x).sum::<f32>().sqrt();

        if norm1 > 0.0 && norm2 > 0.0 {
            dot_product / (norm1 * norm2)
        } else {
            0.0
        }
    }

    /// Evaluate adversarial robustness
    fn evaluate_adversarial_robustness(&self, audio: &AudioBuffer) -> Result<f32, EvaluationError> {
        let samples = audio.samples();
        let original_score = self.predict_quality_score(&self.extract_neural_features(audio)?)?;

        let mut adversarial_scores = Vec::new();
        let noise_levels = vec![0.001, 0.005, 0.01, 0.02];

        for &noise_level in &noise_levels {
            // Add adversarial noise
            use scirs2_core::random::Rng;
            let mut rng = scirs2_core::random::thread_rng();
            let adversarial_samples: Vec<f32> = samples
                .iter()
                .map(|&x| {
                    let noise = (rng.random::<f32>() - 0.5) * noise_level;
                    x + noise
                })
                .collect();

            let adversarial_audio = AudioBuffer::mono(adversarial_samples, audio.sample_rate());
            let adversarial_features = self.extract_neural_features(&adversarial_audio)?;
            let adversarial_score = self.predict_quality_score(&adversarial_features)?;

            adversarial_scores.push(adversarial_score);
        }

        // Compute robustness as stability under perturbations
        let score_variance = {
            let mean_score =
                adversarial_scores.iter().sum::<f32>() / adversarial_scores.len() as f32;
            adversarial_scores
                .iter()
                .map(|score| (score - mean_score) * (score - mean_score))
                .sum::<f32>()
                / adversarial_scores.len() as f32
        };

        let robustness = 1.0 / (1.0 + score_variance * 10.0); // Higher variance = lower robustness
        Ok(robustness)
    }

    /// Compute perceptual alignment score
    fn compute_perceptual_alignment(&self, features: &[f32]) -> Result<f32, EvaluationError> {
        if features.is_empty() {
            return Ok(0.0);
        }

        // Simplified perceptual alignment based on feature distribution
        let mean = features.iter().sum::<f32>() / features.len() as f32;
        let std_dev = {
            let variance = features
                .iter()
                .map(|x| (x - mean) * (x - mean))
                .sum::<f32>()
                / features.len() as f32;
            variance.sqrt()
        };

        // Features should be reasonably distributed (not too concentrated or spread)
        let ideal_std = 0.5;
        let alignment = 1.0 - (std_dev - ideal_std).abs() / ideal_std;
        Ok(alignment.max(0.0).min(1.0))
    }

    /// Compare two audio signals using neural evaluation
    pub fn compare_neural_quality(
        &self,
        reference: &AudioBuffer,
        generated: &AudioBuffer,
    ) -> Result<f32, EvaluationError> {
        let ref_assessment = self.evaluate_neural_quality(reference)?;
        let gen_assessment = self.evaluate_neural_quality(generated)?;

        // Weighted comparison across different neural metrics
        let score_diff = (ref_assessment.overall_score - gen_assessment.overall_score).abs();
        let confidence_diff = (ref_assessment.confidence - gen_assessment.confidence).abs();
        let perceptual_diff =
            (ref_assessment.perceptual_alignment - gen_assessment.perceptual_alignment).abs();

        // Latent feature similarity
        let feature_similarity = self.compute_feature_similarity(
            &ref_assessment.latent_features,
            &gen_assessment.latent_features,
        );

        // Multi-scale similarity
        let multi_scale_similarity =
            if ref_assessment.multi_scale_scores.len() == gen_assessment.multi_scale_scores.len() {
                let correlations: Vec<f32> = ref_assessment
                    .multi_scale_scores
                    .iter()
                    .zip(gen_assessment.multi_scale_scores.iter())
                    .map(|(&r, &g)| 1.0 - (r - g).abs())
                    .collect();
                correlations.iter().sum::<f32>() / correlations.len() as f32
            } else {
                0.5
            };

        // Weighted combination
        let neural_similarity = 0.3 * (1.0 - score_diff)
            + 0.2 * (1.0 - confidence_diff)
            + 0.2 * (1.0 - perceptual_diff)
            + 0.2 * feature_similarity
            + 0.1 * multi_scale_similarity;

        Ok(neural_similarity.max(0.0).min(1.0))
    }
}

impl Default for NeuralEvaluator {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_neural_evaluator_creation() {
        let evaluator = NeuralEvaluator::new();
        assert_eq!(
            evaluator.config.model_architecture as u8,
            ModelArchitecture::Transformer as u8
        );
        assert_eq!(evaluator.config.hidden_size, 512);
        assert_eq!(evaluator.config.num_attention_heads, 8);
    }

    #[test]
    fn test_neural_config_default() {
        let config = NeuralConfig::default();
        assert_eq!(config.sequence_length, 1024);
        assert_eq!(config.num_layers, 6);
        assert!(!config.enable_adversarial);
        assert!(config.enable_self_supervised);
    }

    #[test]
    fn test_neural_quality_evaluation() {
        let evaluator = NeuralEvaluator::new();
        let audio = AudioBuffer::mono(vec![0.1; 16000], 16000);

        let assessment = evaluator.evaluate_neural_quality(&audio);
        assert!(assessment.is_ok());

        let result = assessment.unwrap();
        assert!(result.overall_score >= 0.0 && result.overall_score <= 1.0);
        assert!(result.confidence >= 0.0 && result.confidence <= 1.0);
        assert!(!result.attention_weights.is_empty());
        assert!(!result.feature_importance.is_empty());
        assert!(!result.latent_features.is_empty());
        assert!(!result.multi_scale_scores.is_empty());
    }

    #[test]
    fn test_feature_extraction() {
        let evaluator = NeuralEvaluator::new();
        let audio = AudioBuffer::mono(vec![0.1; 16000], 16000);

        let features = evaluator.extract_neural_features(&audio);
        assert!(features.is_ok());

        let result = features.unwrap();
        assert!(!result.is_empty());
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_mel_feature_extraction() {
        let evaluator = NeuralEvaluator::new();
        let frame = vec![0.1; 2048];

        let mel_features = evaluator.extract_mel_features(&frame);
        assert!(mel_features.is_ok());

        let result = mel_features.unwrap();
        assert_eq!(result.len(), evaluator.feature_extractor.num_mel_filters);
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_temporal_feature_extraction() {
        let evaluator = NeuralEvaluator::new();
        let frame = vec![0.1; 2048];

        let temporal_features = evaluator.extract_temporal_features(&frame);
        assert!(temporal_features.is_ok());

        let result = temporal_features.unwrap();
        assert_eq!(result.len(), 4); // energy, zcr, centroid, rolloff
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_spectral_feature_extraction() {
        let evaluator = NeuralEvaluator::new();
        let frame = vec![0.1; 2048];

        let spectral_features = evaluator.extract_spectral_features(&frame);
        assert!(spectral_features.is_ok());

        let result = spectral_features.unwrap();
        assert_eq!(result.len(), 3); // flatness, flux, bandwidth
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_attention_computation() {
        let evaluator = NeuralEvaluator::new();
        let features = vec![0.1, 0.2, 0.3, 0.4, 0.5];

        let attention_weights = evaluator.compute_attention_weights(&features, 3);
        assert!(attention_weights.is_ok());

        let result = attention_weights.unwrap();
        assert_eq!(result.len(), evaluator.config.num_attention_heads);
        for head_weights in &result {
            assert_eq!(head_weights.len(), 3);
            let sum: f32 = head_weights.iter().sum();
            assert!((sum - 1.0).abs() < 0.01); // Should sum to 1 (softmax)
        }
    }

    #[test]
    fn test_transformer_processing() {
        let evaluator = NeuralEvaluator::new();
        let features = vec![0.1; 100];

        let result = evaluator.apply_transformer_processing(&features);
        assert!(result.is_ok());

        let (processed_features, attention_weights) = result.unwrap();

        assert!(!processed_features.is_empty());
        assert!(!attention_weights.is_empty());
    }

    #[test]
    fn test_quality_prediction() {
        let evaluator = NeuralEvaluator::new();
        let features = vec![0.5; 10];

        let score = evaluator.predict_quality_score(&features);
        assert!(score.is_ok());

        let result = score.unwrap();
        assert!(result >= 0.0 && result <= 1.0);
    }

    #[test]
    fn test_multi_scale_analysis() {
        let evaluator = NeuralEvaluator::new();
        let audio = AudioBuffer::mono(vec![0.1; 16000], 16000);

        let scores = evaluator.compute_multi_scale_scores(&audio);
        assert!(scores.is_ok());

        let result = scores.unwrap();
        assert!(!result.is_empty());
        assert!(result.iter().all(|&score| score >= 0.0 && score <= 1.0));
    }

    #[test]
    fn test_feature_importance() {
        let evaluator = NeuralEvaluator::new();
        let features = vec![0.1; 100];

        let importance = evaluator.analyze_feature_importance(&features);
        assert!(importance.is_ok());

        let result = importance.unwrap();
        assert!(!result.is_empty());
        assert!(result.values().all(|&val| val >= 0.0));
    }

    #[test]
    fn test_self_supervised_evaluation() {
        let evaluator = NeuralEvaluator::new();
        let audio = AudioBuffer::mono(vec![0.1; 16000], 16000);

        let score = evaluator.evaluate_self_supervised(&audio);
        assert!(score.is_ok());

        let result = score.unwrap();
        assert!(result >= 0.0 && result <= 1.0);
    }

    #[test]
    fn test_adversarial_robustness() {
        let evaluator = NeuralEvaluator::new();
        let audio = AudioBuffer::mono(vec![0.1; 16000], 16000);

        let robustness = evaluator.evaluate_adversarial_robustness(&audio);
        assert!(robustness.is_ok());

        let result = robustness.unwrap();
        assert!(result >= 0.0 && result <= 1.0);
    }

    #[test]
    fn test_neural_comparison() {
        let evaluator = NeuralEvaluator::new();
        let reference = AudioBuffer::mono(vec![0.1; 16000], 16000);
        let generated = AudioBuffer::mono(vec![0.1; 16000], 16000);

        let similarity = evaluator.compare_neural_quality(&reference, &generated);
        assert!(similarity.is_ok());

        let result = similarity.unwrap();
        assert!(result >= 0.0 && result <= 1.0);
        assert!(result > 0.8); // Should be high for identical signals
    }

    #[test]
    fn test_feature_similarity() {
        let evaluator = NeuralEvaluator::new();
        let features1 = vec![1.0, 2.0, 3.0];
        let features2 = vec![2.0, 4.0, 6.0];

        let similarity = evaluator.compute_feature_similarity(&features1, &features2);
        assert!((similarity - 1.0).abs() < 0.001); // Should be 1.0 for perfectly correlated
    }

    #[test]
    fn test_activation_functions() {
        let evaluator = NeuralEvaluator::new();

        assert!(evaluator.apply_activation(-1.0) < 0.0); // GELU allows negative outputs
        assert!(evaluator.apply_activation(1.0) > 0.0);
        assert!(evaluator.apply_activation(0.0) >= 0.0);
    }

    #[test]
    fn test_hz_to_mel_conversion() {
        let mel_1000 = NeuralEvaluator::hz_to_mel(1000.0);
        let mel_2000 = NeuralEvaluator::hz_to_mel(2000.0);

        assert!(mel_1000 < mel_2000); // Higher frequency should have higher mel value
        assert!(mel_1000 > 0.0);
    }

    #[test]
    fn test_empty_audio_handling() {
        let evaluator = NeuralEvaluator::new();
        let empty_audio = AudioBuffer::mono(vec![], 16000);

        let result = evaluator.evaluate_neural_quality(&empty_audio);
        assert!(result.is_err()); // Should fail gracefully
    }

    #[test]
    fn test_perceptual_alignment() {
        let evaluator = NeuralEvaluator::new();
        let features = vec![0.5; 10]; // Well-distributed features

        let alignment = evaluator.compute_perceptual_alignment(&features);
        assert!(alignment.is_ok());

        let result = alignment.unwrap();
        assert!(result >= 0.0 && result <= 1.0);
    }
}