oxirs_vec/

joint_embedding_spaces.rs

//! Joint Embedding Spaces for Cross-Modal Vector Search
//!
//! This module implements advanced joint embedding spaces that enable:
//! - CLIP-style text-image alignment
//! - Cross-modal attention mechanisms
//! - Contrastive learning for alignment
//! - Multi-modal fusion strategies
//! - Domain adaptation and transfer learning

use crate::{
    cross_modal_embeddings::{
        AudioData, ImageData, Modality, ModalityData, MultiModalContent, VideoData,
    },
    Vector,
};
use anyhow::{anyhow, Result};
use parking_lot::RwLock;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;

/// Configuration for joint embedding space
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct JointEmbeddingConfig {
    /// Dimension of the joint embedding space
    pub joint_dim: usize,
    /// Temperature parameter for contrastive learning
    pub temperature: f32,
    /// Learning rate for alignment optimization
    pub learning_rate: f32,
    /// Margin for triplet loss
    pub margin: f32,
    /// Enable contrastive learning
    pub contrastive_learning: bool,
    /// Enable triplet loss
    pub triplet_loss: bool,
    /// Enable hard negative mining
    pub hard_negative_mining: bool,
    /// Batch size for training
    pub batch_size: usize,
    /// Number of negative samples per positive
    pub negative_samples: usize,
    /// Enable curriculum learning
    pub curriculum_learning: bool,
    /// Weight decay for regularization
    pub weight_decay: f32,
    /// Gradient clipping threshold
    pub gradient_clip: f32,
    /// Enable domain adaptation
    pub domain_adaptation: bool,
    /// Cross-modal alignment strength
    pub alignment_strength: f32,
    /// Enable self-supervised learning
    pub self_supervised: bool,
}

impl Default for JointEmbeddingConfig {
    fn default() -> Self {
        Self {
            joint_dim: 512,
            temperature: 0.07,
            learning_rate: 1e-4,
            margin: 0.2,
            contrastive_learning: true,
            triplet_loss: false,
            hard_negative_mining: true,
            batch_size: 256,
            negative_samples: 5,
            curriculum_learning: false,
            weight_decay: 1e-4,
            gradient_clip: 1.0,
            domain_adaptation: true,
            alignment_strength: 1.0,
            self_supervised: false,
        }
    }
}

/// Type alias for contrastive pairs result
type ContrastivePairs = (
    Vec<(Modality, Vector, Modality, Vector)>,
    Vec<(Modality, Vector, Modality, Vector)>,
);

/// Joint embedding space for cross-modal alignment
pub struct JointEmbeddingSpace {
    config: JointEmbeddingConfig,
    text_projector: LinearProjector,
    image_projector: LinearProjector,
    audio_projector: LinearProjector,
    video_projector: LinearProjector,
    attention_mechanism: CrossModalAttention,
    alignment_cache: Arc<RwLock<HashMap<String, AlignmentPair>>>,
    training_stats: Arc<RwLock<TrainingStatistics>>,
    temperature_scheduler: TemperatureScheduler,
    domain_adapter: DomainAdapter,
}

/// Linear projector for transforming embeddings to joint space
#[derive(Debug, Clone)]
pub struct LinearProjector {
    weights: Vec<Vec<f32>>,
    bias: Vec<f32>,
    input_dim: usize,
    output_dim: usize,
    dropout_rate: f32,
    activation: ActivationFunction,
}

/// Activation functions for projectors
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ActivationFunction {
    ReLU,
    GELU,
    Tanh,
    Sigmoid,
    Swish,
    Mish,
    LeakyReLU(f32),
}

/// Cross-modal attention mechanism for joint spaces
#[derive(Debug, Clone)]
pub struct CrossModalAttention {
    query_projector: LinearProjector,
    key_projector: LinearProjector,
    value_projector: LinearProjector,
    output_projector: LinearProjector,
    num_heads: usize,
    head_dim: usize,
    dropout_rate: f32,
    scale: f32,
    enable_relative_pos: bool,
}

/// Alignment pair for caching and training
#[derive(Debug, Clone)]
pub struct AlignmentPair {
    modality1: Modality,
    modality2: Modality,
    embedding1: Vector,
    embedding2: Vector,
    similarity: f32,
    confidence: f32,
    timestamp: std::time::SystemTime,
}

/// Training statistics for monitoring
#[derive(Debug, Clone, Default)]
pub struct TrainingStatistics {
    total_samples: u64,
    positive_pairs: u64,
    negative_pairs: u64,
    average_loss: f32,
    average_similarity: f32,
    convergence_rate: f32,
    alignment_accuracy: f32,
    cross_modal_retrieval_acc: HashMap<(Modality, Modality), f32>,
    training_epochs: u32,
    last_improvement: u32,
}

/// Temperature scheduler for contrastive learning
#[derive(Debug, Clone)]
pub struct TemperatureScheduler {
    initial_temperature: f32,
    final_temperature: f32,
    decay_steps: usize,
    current_step: usize,
    schedule_type: ScheduleType,
}

#[derive(Debug, Clone, Copy)]
pub enum ScheduleType {
    Linear,
    Exponential,
    Cosine,
    Warmup,
}

/// Domain adaptation module for cross-domain alignment
#[derive(Debug, Clone)]
pub struct DomainAdapter {
    source_stats: DomainStatistics,
    target_stats: DomainStatistics,
    adaptation_weights: Vec<f32>,
    domain_classifier: Option<DomainClassifier>,
    adaptation_strength: f32,
}

#[derive(Debug, Clone, Default)]
pub struct DomainStatistics {
    mean: Vec<f32>,
    variance: Vec<f32>,
    sample_count: usize,
    feature_statistics: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct DomainClassifier {
    weights: Vec<Vec<f32>>,
    bias: Vec<f32>,
    accuracy: f32,
}

/// CLIP-style contrastive learning implementation
pub struct CLIPAligner {
    joint_space: JointEmbeddingSpace,
    optimizer: ContrastiveOptimizer,
    data_augmentation: DataAugmentation,
    curriculum: CurriculumLearning,
}

/// Contrastive optimizer for alignment training
#[derive(Debug, Clone)]
pub struct ContrastiveOptimizer {
    learning_rate: f32,
    momentum: f32,
    weight_decay: f32,
    gradient_history: HashMap<String, Vec<f32>>,
    adaptive_lr: bool,
    lr_schedule: LearningRateSchedule,
}

#[derive(Debug, Clone, Copy)]
pub enum LearningRateSchedule {
    Constant,
    StepDecay { step_size: usize, gamma: f32 },
    ExponentialDecay { gamma: f32 },
    CosineAnnealing { min_lr: f32, max_epochs: usize },
}

/// Data augmentation for improved generalization
#[derive(Debug, Clone)]
pub struct DataAugmentation {
    text_augmentations: Vec<TextAugmentation>,
    image_augmentations: Vec<ImageAugmentation>,
    audio_augmentations: Vec<AudioAugmentation>,
    cross_modal_mixup: bool,
    augmentation_probability: f32,
}

#[derive(Debug, Clone)]
pub enum TextAugmentation {
    RandomWordDropout(f32),
    Paraphrasing,
    BackTranslation,
    SynonymReplacement(f32),
    ContextualAugmentation,
}

#[derive(Debug, Clone)]
pub enum ImageAugmentation {
    RandomCrop {
        size: (u32, u32),
    },
    RandomFlip {
        horizontal: bool,
        vertical: bool,
    },
    ColorJitter {
        brightness: f32,
        contrast: f32,
        saturation: f32,
    },
    RandomRotation {
        max_angle: f32,
    },
    GaussianBlur {
        sigma: f32,
    },
}

#[derive(Debug, Clone)]
pub enum AudioAugmentation {
    TimeStretch { factor: f32 },
    PitchShift { semitones: f32 },
    AddNoise { snr_db: f32 },
    FrequencyMasking { max_freq_mask: f32 },
    TimeMasking { max_time_mask: f32 },
}

/// Curriculum learning for progressive training
#[derive(Debug, Clone)]
pub struct CurriculumLearning {
    enabled: bool,
    current_difficulty: f32,
    difficulty_schedule: DifficultySchedule,
    pacing_function: PacingFunction,
    competence_threshold: f32,
}

#[derive(Debug, Clone)]
pub enum DifficultySchedule {
    Linear { start: f32, end: f32, epochs: usize },
    Exponential { base: f32, scale: f32 },
    Adaptive { improvement_threshold: f32 },
}

#[derive(Debug, Clone)]
pub enum PacingFunction {
    Root,
    Linear,
    Logarithmic,
    Polynomial(f32),
}

impl LinearProjector {
    pub fn new(
        input_dim: usize,
        output_dim: usize,
        dropout_rate: f32,
        activation: ActivationFunction,
    ) -> Self {
        // Xavier/Glorot initialization
        let limit = (6.0 / (input_dim + output_dim) as f32).sqrt();
        let mut weights = Vec::with_capacity(output_dim);

        for _ in 0..output_dim {
            let mut row = Vec::with_capacity(input_dim);
            for _ in 0..input_dim {
                // Simple deterministic initialization based on indices
                let weight = ((row.len() as f32 * 0.01) % 2.0 - 1.0) * limit;
                row.push(weight);
            }
            weights.push(row);
        }

        let bias = vec![0.0; output_dim];

        Self {
            weights,
            bias,
            input_dim,
            output_dim,
            dropout_rate,
            activation,
        }
    }

    pub fn forward(&self, input: &Vector) -> Result<Vector> {
        if input.dimensions != self.input_dim {
            return Err(anyhow!(
                "Input dimension mismatch: expected {}, got {}",
                self.input_dim,
                input.dimensions
            ));
        }

        let input_values = input.as_f32();
        let mut output = vec![0.0; self.output_dim];

        // Matrix multiplication: output = input * weights^T + bias
        for (i, output_val) in output.iter_mut().enumerate().take(self.output_dim) {
            let mut sum = self.bias[i];
            for (j, &input_val) in input_values.iter().enumerate().take(self.input_dim) {
                sum += input_val * self.weights[i][j];
            }
            *output_val = sum;
        }

        // Apply activation function
        for value in &mut output {
            *value = self.apply_activation(*value);
        }

        // Apply dropout during training (simplified - always apply for consistency)
        if self.dropout_rate > 0.0 {
            for (i, value) in output.iter_mut().enumerate() {
                // Deterministic dropout based on index for reproducibility
                if (i as f32 * 0.12345) % 1.0 < self.dropout_rate {
                    *value = 0.0;
                } else {
                    *value /= 1.0 - self.dropout_rate; // Scale to maintain expected value
                }
            }
        }

        Ok(Vector::new(output))
    }

    fn apply_activation(&self, x: f32) -> f32 {
        match self.activation {
            ActivationFunction::ReLU => x.max(0.0),
            ActivationFunction::GELU => {
                // Approximate GELU: x * Φ(x) ≈ 0.5 * x * (1 + tanh(√(2/π) * (x + 0.044715 * x³)))
                let sqrt_2_pi = (2.0 / std::f32::consts::PI).sqrt();
                let inner = sqrt_2_pi * (x + 0.044715 * x.powi(3));
                0.5 * x * (1.0 + inner.tanh())
            }
            ActivationFunction::Tanh => x.tanh(),
            ActivationFunction::Sigmoid => 1.0 / (1.0 + (-x).exp()),
            ActivationFunction::Swish => x * (1.0 / (1.0 + (-x).exp())), // x * sigmoid(x)
            ActivationFunction::Mish => x * (1.0 + x.exp()).ln().tanh(),
            ActivationFunction::LeakyReLU(alpha) => {
                if x > 0.0 {
                    x
                } else {
                    alpha * x
                }
            }
        }
    }

    pub fn update_weights(&mut self, gradients: &[Vec<f32>], learning_rate: f32) {
        for i in 0..self.output_dim {
            for j in 0..self.input_dim {
                if i < gradients.len() && j < gradients[i].len() {
                    self.weights[i][j] -= learning_rate * gradients[i][j];
                }
            }
        }
    }
}

impl CrossModalAttention {
    pub fn new(
        input_dim: usize,
        num_heads: usize,
        dropout_rate: f32,
        enable_relative_pos: bool,
    ) -> Self {
        let head_dim = input_dim / num_heads;
        let scale = 1.0 / (head_dim as f32).sqrt();

        Self {
            query_projector: LinearProjector::new(
                input_dim,
                input_dim,
                dropout_rate,
                ActivationFunction::ReLU,
            ),
            key_projector: LinearProjector::new(
                input_dim,
                input_dim,
                dropout_rate,
                ActivationFunction::ReLU,
            ),
            value_projector: LinearProjector::new(
                input_dim,
                input_dim,
                dropout_rate,
                ActivationFunction::ReLU,
            ),
            output_projector: LinearProjector::new(
                input_dim,
                input_dim,
                dropout_rate,
                ActivationFunction::ReLU,
            ),
            num_heads,
            head_dim,
            dropout_rate,
            scale,
            enable_relative_pos,
        }
    }

    pub fn cross_attention(
        &self,
        query_modality: &Vector,
        key_modality: &Vector,
        value_modality: &Vector,
    ) -> Result<Vector> {
        // Project to query, key, value spaces
        let query = self.query_projector.forward(query_modality)?;
        let key = self.key_projector.forward(key_modality)?;
        let value = self.value_projector.forward(value_modality)?;

        // Multi-head attention computation
        let attended = self.multi_head_attention(&query, &key, &value)?;

        // Output projection
        self.output_projector.forward(&attended)
    }

    fn multi_head_attention(&self, query: &Vector, key: &Vector, value: &Vector) -> Result<Vector> {
        let query_vals = query.as_f32();
        let key_vals = key.as_f32();
        let value_vals = value.as_f32();

        if query_vals.len() != key_vals.len() || key_vals.len() != value_vals.len() {
            return Err(anyhow!("Dimension mismatch in attention"));
        }

        let _seq_len = query_vals.len() / self.head_dim;
        let mut output = vec![0.0; query_vals.len()];

        // Process each attention head
        for head in 0..self.num_heads {
            let head_start = head * self.head_dim;
            let head_end = head_start + self.head_dim;

            // Extract head-specific query, key, value
            let head_query = &query_vals[head_start..head_end];
            let head_key = &key_vals[head_start..head_end];
            let head_value = &value_vals[head_start..head_end];

            // Compute attention scores
            let attention_score = self.compute_attention_score(head_query, head_key);

            // Apply attention to values
            for i in 0..self.head_dim {
                output[head_start + i] = head_value[i] * attention_score;
            }
        }

        // Apply relative positional encoding if enabled
        if self.enable_relative_pos {
            self.apply_relative_position_encoding(&mut output)?;
        }

        Ok(Vector::new(output))
    }

    fn compute_attention_score(&self, query: &[f32], key: &[f32]) -> f32 {
        let dot_product: f32 = query.iter().zip(key.iter()).map(|(q, k)| q * k).sum();
        let scaled_score = dot_product * self.scale;

        // Apply softmax (simplified single-head version)
        scaled_score.tanh() // Approximate attention weight
    }

    fn apply_relative_position_encoding(&self, output: &mut [f32]) -> Result<()> {
        // Simplified relative positional encoding
        let output_len = output.len();
        for (i, value) in output.iter_mut().enumerate() {
            let pos_encoding = (i as f32 / output_len as f32).sin();
            *value += 0.1 * pos_encoding; // Small positional bias
        }
        Ok(())
    }
}

impl TemperatureScheduler {
    pub fn new(
        initial_temperature: f32,
        final_temperature: f32,
        decay_steps: usize,
        schedule_type: ScheduleType,
    ) -> Self {
        Self {
            initial_temperature,
            final_temperature,
            decay_steps,
            current_step: 0,
            schedule_type,
        }
    }

    pub fn get_current_temperature(&self) -> f32 {
        if self.current_step >= self.decay_steps {
            return self.final_temperature;
        }

        let progress = self.current_step as f32 / self.decay_steps as f32;

        match self.schedule_type {
            ScheduleType::Linear => {
                self.initial_temperature
                    + (self.final_temperature - self.initial_temperature) * progress
            }
            ScheduleType::Exponential => {
                self.initial_temperature
                    * (self.final_temperature / self.initial_temperature).powf(progress)
            }
            ScheduleType::Cosine => {
                let cosine_progress = 0.5 * (1.0 + (std::f32::consts::PI * progress).cos());
                self.final_temperature
                    + (self.initial_temperature - self.final_temperature) * cosine_progress
            }
            ScheduleType::Warmup => {
                if progress < 0.1 {
                    // Warmup phase
                    self.initial_temperature * (progress / 0.1)
                } else {
                    // Decay phase
                    let decay_progress = (progress - 0.1) / 0.9;
                    self.initial_temperature
                        + (self.final_temperature - self.initial_temperature) * decay_progress
                }
            }
        }
    }

    pub fn step(&mut self) {
        self.current_step += 1;
    }
}

impl DomainAdapter {
    pub fn new(adaptation_strength: f32) -> Self {
        Self {
            source_stats: DomainStatistics::default(),
            target_stats: DomainStatistics::default(),
            adaptation_weights: Vec::new(),
            domain_classifier: None,
            adaptation_strength,
        }
    }

    pub fn adapt_embedding(&self, embedding: &Vector, is_source_domain: bool) -> Result<Vector> {
        let input_values = embedding.as_f32();
        let mut adapted_values = input_values.clone();

        if self.adaptation_weights.len() != input_values.len() {
            return Ok(embedding.clone()); // No adaptation available
        }

        // Apply domain adaptation
        let stats = if is_source_domain {
            &self.source_stats
        } else {
            &self.target_stats
        };

        for (i, adapted_value) in adapted_values.iter_mut().enumerate() {
            if i < stats.mean.len() && i < stats.variance.len() {
                // Normalize using domain statistics
                let normalized =
                    (*adapted_value - stats.mean[i]) / (stats.variance[i].sqrt() + 1e-8);

                // Apply adaptation weights
                *adapted_value = normalized * self.adaptation_weights[i] * self.adaptation_strength
                    + *adapted_value * (1.0 - self.adaptation_strength);
            }
        }

        Ok(Vector::new(adapted_values))
    }

    pub fn update_domain_statistics(&mut self, embeddings: &[Vector], is_source_domain: bool) {
        let stats = if is_source_domain {
            &mut self.source_stats
        } else {
            &mut self.target_stats
        };

        if embeddings.is_empty() {
            return;
        }

        let dim = embeddings[0].dimensions;
        if stats.mean.len() != dim {
            stats.mean = vec![0.0; dim];
            stats.variance = vec![0.0; dim];
            stats.sample_count = 0;
        }

        // Update running statistics
        for embedding in embeddings {
            let values = embedding.as_f32();
            for (i, &value) in values.iter().enumerate().take(dim) {
                let delta = value - stats.mean[i];
                stats.sample_count += 1;
                stats.mean[i] += delta / stats.sample_count as f32;
                let delta2 = value - stats.mean[i];
                stats.variance[i] += delta * delta2;
            }
        }

        // Finalize variance calculation
        if stats.sample_count > 1 {
            for variance in &mut stats.variance {
                *variance /= (stats.sample_count - 1) as f32;
            }
        }

        // Update adaptation weights based on domain discrepancy
        self.update_adaptation_weights();
    }

    fn update_adaptation_weights(&mut self) {
        let dim = self.source_stats.mean.len();
        if dim == 0 || dim != self.target_stats.mean.len() {
            return;
        }

        self.adaptation_weights = vec![1.0; dim];

        for i in 0..dim {
            // Compute domain discrepancy as statistical distance
            let mean_diff = (self.source_stats.mean[i] - self.target_stats.mean[i]).abs();
            let var_ratio = (self.source_stats.variance[i]
                / (self.target_stats.variance[i] + 1e-8))
                .ln()
                .abs();

            // Weight adaptation based on discrepancy
            let discrepancy = mean_diff + 0.5 * var_ratio;
            self.adaptation_weights[i] = 1.0 / (1.0 + discrepancy);
        }
    }
}

impl JointEmbeddingSpace {
    pub fn new(config: JointEmbeddingConfig) -> Self {
        let text_projector = LinearProjector::new(
            768, // BERT-style embedding dimension
            config.joint_dim,
            0.1,
            ActivationFunction::GELU,
        );

        let image_projector = LinearProjector::new(
            2048, // ResNet/Vision Transformer dimension
            config.joint_dim,
            0.1,
            ActivationFunction::GELU,
        );

        let audio_projector = LinearProjector::new(
            1024, // Audio embedding dimension
            config.joint_dim,
            0.1,
            ActivationFunction::GELU,
        );

        let video_projector = LinearProjector::new(
            1536, // Video embedding dimension
            config.joint_dim,
            0.1,
            ActivationFunction::GELU,
        );

        let attention_mechanism = CrossModalAttention::new(config.joint_dim, 8, 0.1, true);

        let temperature_scheduler = TemperatureScheduler::new(
            config.temperature * 2.0,
            config.temperature,
            1000,
            ScheduleType::Cosine,
        );

        let domain_adapter = DomainAdapter::new(config.alignment_strength);

        Self {
            config,
            text_projector,
            image_projector,
            audio_projector,
            video_projector,
            attention_mechanism,
            alignment_cache: Arc::new(RwLock::new(HashMap::new())),
            training_stats: Arc::new(RwLock::new(TrainingStatistics::default())),
            temperature_scheduler,
            domain_adapter,
        }
    }

    /// Project modality-specific embedding to joint space
    pub fn project_to_joint_space(&self, modality: Modality, embedding: &Vector) -> Result<Vector> {
        let projected = match modality {
            Modality::Text => self.text_projector.forward(embedding)?,
            Modality::Image => self.image_projector.forward(embedding)?,
            Modality::Audio => self.audio_projector.forward(embedding)?,
            Modality::Video => self.video_projector.forward(embedding)?,
            _ => {
                // For other modalities, use text projector as fallback
                self.text_projector.forward(embedding)?
            }
        };

        // Apply L2 normalization for cosine similarity computation
        Ok(projected.normalized())
    }

    /// Compute cross-modal similarity in joint space
    pub fn cross_modal_similarity(
        &self,
        modality1: Modality,
        embedding1: &Vector,
        modality2: Modality,
        embedding2: &Vector,
    ) -> Result<f32> {
        let joint_emb1 = self.project_to_joint_space(modality1, embedding1)?;
        let joint_emb2 = self.project_to_joint_space(modality2, embedding2)?;

        // Apply cross-modal attention if different modalities
        if modality1 != modality2 {
            let attended_emb1 =
                self.attention_mechanism
                    .cross_attention(&joint_emb1, &joint_emb2, &joint_emb2)?;
            let attended_emb2 =
                self.attention_mechanism
                    .cross_attention(&joint_emb2, &joint_emb1, &joint_emb1)?;

            attended_emb1.cosine_similarity(&attended_emb2)
        } else {
            joint_emb1.cosine_similarity(&joint_emb2)
        }
    }

    /// Contrastive learning alignment training
    pub fn contrastive_align(
        &mut self,
        positive_pairs: &[(Modality, Vector, Modality, Vector)],
        negative_pairs: &[(Modality, Vector, Modality, Vector)],
    ) -> Result<f32> {
        let mut total_loss = 0.0;
        let temperature = self.temperature_scheduler.get_current_temperature();

        // Process positive pairs
        for (mod1, emb1, mod2, emb2) in positive_pairs {
            let similarity = self.cross_modal_similarity(*mod1, emb1, *mod2, emb2)?;
            let positive_score = similarity / temperature;

            // Contrastive loss for positive pairs (should be high similarity)
            let positive_loss = -positive_score.ln_1p(); // -log(1 + exp(score))
            total_loss += positive_loss;

            // Cache successful alignments
            self.cache_alignment(*mod1, emb1.clone(), *mod2, emb2.clone(), similarity);
        }

        // Process negative pairs
        for (mod1, emb1, mod2, emb2) in negative_pairs {
            let similarity = self.cross_modal_similarity(*mod1, emb1, *mod2, emb2)?;
            let negative_score = similarity / temperature;

            // Contrastive loss for negative pairs (should be low similarity)
            let negative_loss = (negative_score + self.config.margin).max(0.0);
            total_loss += negative_loss;
        }

        // Update training statistics
        self.update_training_stats(positive_pairs.len(), negative_pairs.len(), total_loss);

        // Step temperature scheduler
        self.temperature_scheduler.step();

        Ok(total_loss / (positive_pairs.len() + negative_pairs.len()) as f32)
    }

    /// Find cross-modal nearest neighbors in joint space
    pub fn cross_modal_search(
        &self,
        query_modality: Modality,
        query_embedding: &Vector,
        candidate_modality: Modality,
        candidate_embeddings: &[Vector],
        top_k: usize,
    ) -> Result<Vec<(usize, f32)>> {
        let query_joint = self.project_to_joint_space(query_modality, query_embedding)?;
        let mut similarities = Vec::new();

        for (idx, candidate) in candidate_embeddings.iter().enumerate() {
            let candidate_joint = self.project_to_joint_space(candidate_modality, candidate)?;

            // Apply cross-modal attention if different modalities
            let similarity = if query_modality != candidate_modality {
                let attended_query = self.attention_mechanism.cross_attention(
                    &query_joint,
                    &candidate_joint,
                    &candidate_joint,
                )?;
                attended_query.cosine_similarity(&candidate_joint)?
            } else {
                query_joint.cosine_similarity(&candidate_joint)?
            };

            similarities.push((idx, similarity));
        }

        // Sort by similarity (descending) and take top-k
        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        similarities.truncate(top_k);

        Ok(similarities)
    }

    /// Zero-shot cross-modal retrieval
    pub fn zero_shot_retrieval(
        &self,
        query_modality: Modality,
        query_embedding: &Vector,
        target_modality: Modality,
        target_embeddings: &[Vector],
        top_k: usize,
    ) -> Result<Vec<(usize, f32)>> {
        // Project query to joint space
        let _query_joint = self.project_to_joint_space(query_modality, query_embedding)?;

        // Search across target modality
        self.cross_modal_search(
            query_modality,
            query_embedding,
            target_modality,
            target_embeddings,
            top_k,
        )
    }

    /// Multi-modal fusion in joint space
    pub fn multi_modal_fusion(&self, modalities: &[(Modality, Vector)]) -> Result<Vector> {
        if modalities.is_empty() {
            return Err(anyhow!("No modalities provided for fusion"));
        }

        let mut joint_embeddings = Vec::new();
        for (modality, embedding) in modalities {
            let joint_emb = self.project_to_joint_space(*modality, embedding)?;
            joint_embeddings.push(joint_emb);
        }

        // Apply cross-modal attention between all pairs
        let mut attended_embeddings = Vec::new();
        for i in 0..joint_embeddings.len() {
            let mut attended = joint_embeddings[i].clone();

            for j in 0..joint_embeddings.len() {
                if i != j {
                    let cross_attended = self.attention_mechanism.cross_attention(
                        &joint_embeddings[i],
                        &joint_embeddings[j],
                        &joint_embeddings[j],
                    )?;

                    // Weighted combination
                    let weight = 1.0 / joint_embeddings.len() as f32;
                    attended = attended.add(&cross_attended.scale(weight))?;
                }
            }

            attended_embeddings.push(attended);
        }

        // Average fusion
        if attended_embeddings.len() == 1 {
            Ok(attended_embeddings[0].clone())
        } else {
            let mut fused = attended_embeddings[0].clone();
            for embedding in attended_embeddings.iter().skip(1) {
                fused = fused.add(embedding)?;
            }
            Ok(fused.scale(1.0 / attended_embeddings.len() as f32))
        }
    }

    fn cache_alignment(
        &self,
        mod1: Modality,
        emb1: Vector,
        mod2: Modality,
        emb2: Vector,
        similarity: f32,
    ) {
        let alignment = AlignmentPair {
            modality1: mod1,
            modality2: mod2,
            embedding1: emb1,
            embedding2: emb2,
            similarity,
            confidence: similarity.abs(), // Use absolute similarity as confidence
            timestamp: std::time::SystemTime::now(),
        };

        let cache_key = format!("{mod1:?}_{mod2:?}_{similarity}");
        let mut cache = self.alignment_cache.write();
        cache.insert(cache_key, alignment);

        // Limit cache size
        if cache.len() > 10000 {
            // Remove oldest entries
            let mut entries: Vec<_> = cache.iter().collect();
            entries.sort_by_key(|(_, v)| v.timestamp);
            let oldest_key = entries[0].0.clone();
            cache.remove(&oldest_key);
        }
    }

    fn update_training_stats(&self, positive_count: usize, negative_count: usize, loss: f32) {
        let mut stats = self.training_stats.write();
        stats.total_samples += (positive_count + negative_count) as u64;
        stats.positive_pairs += positive_count as u64;
        stats.negative_pairs += negative_count as u64;

        // Update running average loss
        let total_samples = stats.total_samples as f32;
        stats.average_loss = (stats.average_loss * (total_samples - 1.0) + loss) / total_samples;
    }

    /// Get training statistics
    pub fn get_training_stats(&self) -> TrainingStatistics {
        self.training_stats.read().clone()
    }

    /// Get alignment cache statistics
    pub fn get_cache_stats(&self) -> (usize, f32) {
        let cache = self.alignment_cache.read();
        let cache_size = cache.len();
        let avg_similarity = if cache.is_empty() {
            0.0
        } else {
            cache.values().map(|a| a.similarity).sum::<f32>() / cache_size as f32
        };
        (cache_size, avg_similarity)
    }

    /// Evaluate cross-modal retrieval performance
    pub fn evaluate_retrieval(
        &self,
        test_pairs: &[(Modality, Vector, Modality, Vector)],
        distractors: &[(Modality, Vector)],
        k_values: &[usize],
    ) -> Result<HashMap<usize, f32>> {
        let mut recall_at_k = HashMap::new();

        for &k in k_values {
            let mut total_recall = 0.0;

            for (query_mod, query_emb, target_mod, target_emb) in test_pairs {
                // Create candidate set with target + distractors
                let mut candidates = vec![target_emb.clone()];
                for (distractor_mod, distractor_emb) in distractors {
                    if *distractor_mod == *target_mod {
                        candidates.push(distractor_emb.clone());
                    }
                }

                // Perform search
                let results =
                    self.cross_modal_search(*query_mod, query_emb, *target_mod, &candidates, k)?;

                // Check if target is in top-k (target is always at index 0)
                let found_target = results.iter().any(|(idx, _)| *idx == 0);
                if found_target {
                    total_recall += 1.0;
                }
            }

            recall_at_k.insert(k, total_recall / test_pairs.len() as f32);
        }

        Ok(recall_at_k)
    }
}

impl CLIPAligner {
    pub fn new(config: JointEmbeddingConfig) -> Self {
        let joint_space = JointEmbeddingSpace::new(config.clone());
        let optimizer = ContrastiveOptimizer::new(config.learning_rate, 0.9, config.weight_decay);
        let data_augmentation = DataAugmentation::default();
        let curriculum = CurriculumLearning::new();

        Self {
            joint_space,
            optimizer,
            data_augmentation,
            curriculum,
        }
    }

    /// Train CLIP-style alignment with contrastive learning
    pub fn train_alignment(
        &mut self,
        training_data: &[(MultiModalContent, MultiModalContent)],
        epochs: usize,
    ) -> Result<Vec<f32>> {
        let mut epoch_losses = Vec::new();

        for epoch in 0..epochs {
            let mut epoch_loss = 0.0;
            let mut batch_count = 0;

            // Create batches from training data
            for batch in training_data.chunks(self.joint_space.config.batch_size) {
                let (positive_pairs, negative_pairs) = self.create_contrastive_pairs(batch)?;

                // Apply data augmentation
                let augmented_positive = self.augment_pairs(&positive_pairs)?;
                let augmented_negative = self.augment_pairs(&negative_pairs)?;

                // Compute contrastive loss
                let batch_loss = self
                    .joint_space
                    .contrastive_align(&augmented_positive, &augmented_negative)?;

                epoch_loss += batch_loss;
                batch_count += 1;

                // Update curriculum difficulty
                if self.curriculum.enabled {
                    self.curriculum.update_difficulty(batch_loss);
                }
            }

            let avg_epoch_loss = epoch_loss / batch_count as f32;
            epoch_losses.push(avg_epoch_loss);

            // Update learning rate schedule
            self.optimizer.step_schedule();

            tracing::info!(
                "Epoch {}/{}: Average Loss = {:.4}, Temperature = {:.4}",
                epoch + 1,
                epochs,
                avg_epoch_loss,
                self.joint_space
                    .temperature_scheduler
                    .get_current_temperature()
            );
        }

        Ok(epoch_losses)
    }

    fn create_contrastive_pairs(
        &self,
        batch: &[(MultiModalContent, MultiModalContent)],
    ) -> Result<ContrastivePairs> {
        let mut positive_pairs = Vec::new();
        let mut negative_pairs = Vec::new();

        // Create positive pairs from matched content
        for (content1, content2) in batch {
            for (mod1, data1) in &content1.modalities {
                for (mod2, data2) in &content2.modalities {
                    if let (Ok(emb1), Ok(emb2)) = (
                        self.extract_embedding(*mod1, data1),
                        self.extract_embedding(*mod2, data2),
                    ) {
                        positive_pairs.push((*mod1, emb1, *mod2, emb2));
                    }
                }
            }
        }

        // Create negative pairs by mismatching content
        let batch_size = batch.len();
        for i in 0..batch_size {
            for j in 0..batch_size {
                if i != j {
                    let (content1, _) = &batch[i];
                    let (_, content2) = &batch[j];

                    for (mod1, data1) in &content1.modalities {
                        for (mod2, data2) in &content2.modalities {
                            if let (Ok(emb1), Ok(emb2)) = (
                                self.extract_embedding(*mod1, data1),
                                self.extract_embedding(*mod2, data2),
                            ) {
                                negative_pairs.push((*mod1, emb1, *mod2, emb2));
                            }
                        }
                    }
                }
            }
        }

        // Limit negative pairs to avoid imbalance
        let max_negatives = positive_pairs.len() * self.joint_space.config.negative_samples;
        negative_pairs.truncate(max_negatives);

        Ok((positive_pairs, negative_pairs))
    }

    fn extract_embedding(&self, modality: Modality, data: &ModalityData) -> Result<Vector> {
        // Extract embeddings from modality data
        match (modality, data) {
            (Modality::Text, ModalityData::Text(text)) => {
                // Simple text embedding (in practice, use BERT/transformer)
                let words: Vec<&str> = text.split_whitespace().collect();
                let embedding = self.create_text_embedding(&words);
                Ok(embedding)
            }
            (Modality::Image, ModalityData::Image(image)) => {
                // Simple image embedding (in practice, use CNN/Vision Transformer)
                let embedding = self.create_image_embedding(image);
                Ok(embedding)
            }
            (Modality::Audio, ModalityData::Audio(audio)) => {
                // Simple audio embedding (in practice, use audio transformers)
                let embedding = self.create_audio_embedding(audio);
                Ok(embedding)
            }
            (Modality::Video, ModalityData::Video(video)) => {
                // Simple video embedding (in practice, use video transformers)
                let embedding = self.create_video_embedding(video);
                Ok(embedding)
            }
            (Modality::Numeric, ModalityData::Numeric(values)) => Ok(Vector::new(values.clone())),
            _ => Err(anyhow!("Modality-data type mismatch")),
        }
    }

    fn create_text_embedding(&self, words: &[&str]) -> Vector {
        // Simplified text embedding using word hashing
        let mut embedding = vec![0.0; 768]; // BERT-style dimension

        for (i, word) in words.iter().enumerate().take(100) {
            let hash = self.simple_hash(word) as usize;
            let idx = hash % embedding.len();
            embedding[idx] += 1.0 / (i + 1) as f32; // Position-weighted
        }

        // Normalize
        let norm: f32 = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
        if norm > 0.0 {
            for value in &mut embedding {
                *value /= norm;
            }
        }

        Vector::new(embedding)
    }

    fn create_image_embedding(&self, image: &ImageData) -> Vector {
        // Simplified image embedding using basic features
        let mut embedding = vec![0.0; 2048]; // ResNet-style dimension

        // Color histogram features
        let color_features = self.extract_color_features(image);
        for (i, &feature) in color_features.iter().enumerate().take(256) {
            if i < embedding.len() {
                embedding[i] = feature;
            }
        }

        // Texture features (simplified)
        let texture_features = self.extract_texture_features(image);
        for (i, &feature) in texture_features.iter().enumerate().take(256) {
            if i + 256 < embedding.len() {
                embedding[i + 256] = feature;
            }
        }

        Vector::new(embedding)
    }

    fn create_audio_embedding(&self, audio: &AudioData) -> Vector {
        // Simplified audio embedding using spectral features
        let mut embedding = vec![0.0; 1024]; // Audio transformer dimension

        // MFCC-style features
        if let Some(ref features) = audio.features {
            for (i, &feature) in features.iter().enumerate().take(embedding.len()) {
                embedding[i] = feature;
            }
        } else {
            // Extract from raw samples
            let spectral_features = self.extract_spectral_features(audio);
            for (i, &feature) in spectral_features.iter().enumerate().take(embedding.len()) {
                embedding[i] = feature;
            }
        }

        Vector::new(embedding)
    }

    fn create_video_embedding(&self, video: &VideoData) -> Vector {
        // Simplified video embedding combining visual and temporal features
        let mut embedding = vec![0.0; 1536]; // Video transformer dimension

        // Average frame features
        if !video.frames.is_empty() {
            let frame_embedding = self.create_image_embedding(&video.frames[0]);
            let frame_values = frame_embedding.as_f32();
            for (i, &value) in frame_values.iter().enumerate().take(1024) {
                if i < embedding.len() {
                    embedding[i] = value;
                }
            }
        }

        // Audio features if available
        if let Some(ref audio) = video.audio {
            let audio_embedding = self.create_audio_embedding(audio);
            let audio_values = audio_embedding.as_f32();
            for (i, &value) in audio_values.iter().enumerate().take(512) {
                if i + 1024 < embedding.len() {
                    embedding[i + 1024] = value;
                }
            }
        }

        Vector::new(embedding)
    }

    fn simple_hash(&self, text: &str) -> u64 {
        let mut hash = 5381u64;
        for byte in text.bytes() {
            hash = hash.wrapping_mul(33).wrapping_add(byte as u64);
        }
        hash
    }

    fn extract_color_features(&self, image: &ImageData) -> Vec<f32> {
        // Simplified color histogram
        let mut histogram = vec![0.0; 256];

        match image.format {
            crate::cross_modal_embeddings::ImageFormat::RGB => {
                for chunk in image.data.chunks(3) {
                    if chunk.len() == 3 {
                        let intensity = (chunk[0] as f32 + chunk[1] as f32 + chunk[2] as f32) / 3.0;
                        let bin = (intensity as usize).min(255);
                        histogram[bin] += 1.0;
                    }
                }
            }
            _ => {
                // Simplified handling for other formats
                for &pixel in &image.data {
                    let bin = (pixel as usize).min(255);
                    histogram[bin] += 1.0;
                }
            }
        }

        // Normalize histogram
        let total: f32 = histogram.iter().sum();
        if total > 0.0 {
            for value in &mut histogram {
                *value /= total;
            }
        }

        histogram
    }

    fn extract_texture_features(&self, image: &ImageData) -> Vec<f32> {
        // Simplified texture features using local binary patterns
        let mut features = vec![0.0; 256];

        let width = image.width as usize;
        let height = image.height as usize;

        if width > 2 && height > 2 {
            for y in 1..height - 1 {
                for x in 1..width - 1 {
                    let center_idx = y * width + x;
                    if center_idx < image.data.len() {
                        let center = image.data[center_idx];
                        let mut pattern = 0u8;

                        // Check 8 neighbors
                        let neighbors = [
                            (-1, -1),
                            (0, -1),
                            (1, -1),
                            (-1, 0),
                            (1, 0),
                            (-1, 1),
                            (0, 1),
                            (1, 1),
                        ];

                        for (bit, (dx, dy)) in neighbors.iter().enumerate() {
                            let nx = (x as i32 + dx) as usize;
                            let ny = (y as i32 + dy) as usize;
                            let neighbor_idx = ny * width + nx;

                            if neighbor_idx < image.data.len() && image.data[neighbor_idx] > center
                            {
                                pattern |= 1 << bit;
                            }
                        }

                        features[pattern as usize] += 1.0;
                    }
                }
            }
        }

        // Normalize
        let total: f32 = features.iter().sum();
        if total > 0.0 {
            for value in &mut features {
                *value /= total;
            }
        }

        features
    }

    fn extract_spectral_features(&self, audio: &AudioData) -> Vec<f32> {
        // Simplified spectral features using basic FFT-like transform
        let mut features = vec![0.0; 128];

        if !audio.samples.is_empty() {
            // Simple frequency domain representation
            let chunk_size = audio.samples.len() / features.len();

            for (i, feature) in features.iter_mut().enumerate() {
                let start = i * chunk_size;
                let end = ((i + 1) * chunk_size).min(audio.samples.len());

                if start < end {
                    let chunk = &audio.samples[start..end];

                    // Compute energy in this frequency band
                    let energy: f32 = chunk.iter().map(|x| x * x).sum();
                    *feature = energy.sqrt() / (chunk.len() as f32).sqrt();
                }
            }
        }

        features
    }

    fn augment_pairs(
        &self,
        pairs: &[(Modality, Vector, Modality, Vector)],
    ) -> Result<Vec<(Modality, Vector, Modality, Vector)>> {
        // Simple augmentation by adding small noise
        let mut augmented = Vec::new();

        for (mod1, emb1, mod2, emb2) in pairs {
            let aug_emb1 = self.add_noise(emb1, 0.01)?;
            let aug_emb2 = self.add_noise(emb2, 0.01)?;
            augmented.push((*mod1, aug_emb1, *mod2, aug_emb2));
        }

        Ok(augmented)
    }

    fn add_noise(&self, embedding: &Vector, noise_std: f32) -> Result<Vector> {
        let values = embedding.as_f32();
        let mut noisy_values = Vec::with_capacity(values.len());

        for (i, &value) in values.iter().enumerate() {
            // Deterministic noise based on index for reproducibility
            let noise = ((i as f32 * 0.1234).sin() * noise_std).clamp(-0.1, 0.1);
            noisy_values.push(value + noise);
        }

        Ok(Vector::new(noisy_values))
    }
}

impl ContrastiveOptimizer {
    pub fn new(learning_rate: f32, momentum: f32, weight_decay: f32) -> Self {
        Self {
            learning_rate,
            momentum,
            weight_decay,
            gradient_history: HashMap::new(),
            adaptive_lr: true,
            lr_schedule: LearningRateSchedule::CosineAnnealing {
                min_lr: learning_rate * 0.01,
                max_epochs: 100,
            },
        }
    }

    pub fn step_schedule(&mut self) {
        // Update learning rate based on schedule
        match self.lr_schedule {
            LearningRateSchedule::StepDecay {
                step_size: _,
                gamma,
            } => {
                // Implement step decay
                self.learning_rate *= gamma;
            }
            LearningRateSchedule::ExponentialDecay { gamma } => {
                self.learning_rate *= gamma;
            }
            LearningRateSchedule::CosineAnnealing {
                min_lr,
                max_epochs: _,
            } => {
                // Simplified cosine annealing
                let progress = 0.01; // Would track actual progress
                let lr_range = self.learning_rate - min_lr;
                self.learning_rate =
                    min_lr + lr_range * (1.0 + (std::f32::consts::PI * progress).cos()) / 2.0;
            }
            LearningRateSchedule::Constant => {
                // No change
            }
        }
    }
}

impl Default for DataAugmentation {
    fn default() -> Self {
        Self {
            text_augmentations: vec![
                TextAugmentation::RandomWordDropout(0.1),
                TextAugmentation::SynonymReplacement(0.1),
            ],
            image_augmentations: vec![
                ImageAugmentation::RandomFlip {
                    horizontal: true,
                    vertical: false,
                },
                ImageAugmentation::ColorJitter {
                    brightness: 0.2,
                    contrast: 0.2,
                    saturation: 0.2,
                },
            ],
            audio_augmentations: vec![
                AudioAugmentation::AddNoise { snr_db: 20.0 },
                AudioAugmentation::TimeStretch { factor: 1.1 },
            ],
            cross_modal_mixup: false,
            augmentation_probability: 0.5,
        }
    }
}

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

impl CurriculumLearning {
    pub fn new() -> Self {
        Self {
            enabled: false,
            current_difficulty: 0.0,
            difficulty_schedule: DifficultySchedule::Linear {
                start: 0.0,
                end: 1.0,
                epochs: 50,
            },
            pacing_function: PacingFunction::Root,
            competence_threshold: 0.8,
        }
    }

    pub fn update_difficulty(&mut self, loss: f32) {
        if self.enabled {
            // Adjust difficulty based on loss
            if loss < self.competence_threshold {
                self.current_difficulty = (self.current_difficulty + 0.01).min(1.0);
            } else {
                self.current_difficulty = (self.current_difficulty - 0.005).max(0.0);
            }
        }
    }
}

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

    #[test]
    fn test_joint_embedding_space() {
        let config = JointEmbeddingConfig::default();
        let joint_space = JointEmbeddingSpace::new(config);

        let text_embedding = Vector::new(vec![0.1; 768]);
        let image_embedding = Vector::new(vec![0.2; 2048]);

        let joint_text = joint_space
            .project_to_joint_space(Modality::Text, &text_embedding)
            .unwrap();
        let joint_image = joint_space
            .project_to_joint_space(Modality::Image, &image_embedding)
            .unwrap();

        assert_eq!(joint_text.dimensions, 512);
        assert_eq!(joint_image.dimensions, 512);

        let similarity = joint_space
            .cross_modal_similarity(
                Modality::Text,
                &text_embedding,
                Modality::Image,
                &image_embedding,
            )
            .unwrap();

        assert!((-1.0..=1.0).contains(&similarity));
    }

    #[test]
    fn test_cross_modal_attention() {
        let attention = CrossModalAttention::new(128, 4, 0.1, true);

        let query = Vector::new(vec![0.1; 128]);
        let key = Vector::new(vec![0.2; 128]);
        let value = Vector::new(vec![0.3; 128]);

        let result = attention.cross_attention(&query, &key, &value).unwrap();
        assert_eq!(result.dimensions, 128);
    }

    #[test]
    fn test_contrastive_learning() {
        let config = JointEmbeddingConfig::default();
        let mut joint_space = JointEmbeddingSpace::new(config);

        let positive_pairs = vec![(
            Modality::Text,
            Vector::new(vec![0.1; 768]),
            Modality::Image,
            Vector::new(vec![0.1; 2048]),
        )];

        let negative_pairs = vec![(
            Modality::Text,
            Vector::new(vec![0.1; 768]),
            Modality::Image,
            Vector::new(vec![-0.1; 2048]),
        )];

        let loss = joint_space
            .contrastive_align(&positive_pairs, &negative_pairs)
            .unwrap();

        assert!(loss >= 0.0);
    }

    #[test]
    fn test_clip_aligner() {
        let config = JointEmbeddingConfig::default();
        let aligner = CLIPAligner::new(config);

        let text_words = vec!["hello", "world"];
        let text_embedding = aligner.create_text_embedding(&text_words);
        assert_eq!(text_embedding.dimensions, 768);

        let (cache_size, _) = aligner.joint_space.get_cache_stats();
        assert_eq!(cache_size, 0); // Empty initially
    }
}