use crate::core::traits::{Model, Tokenizer};
use crate::error::Result;
use crate::pipeline::{BasePipeline, Device, Pipeline};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use trustformers_core::cache::CacheKeyBuilder;

/// Configuration for multi-modal pipeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalConfig {
    /// Maximum sequence length for text input
    pub max_text_length: usize,
    /// Maximum image dimensions
    pub max_image_size: (usize, usize),
    /// Maximum audio duration in seconds
    pub max_audio_duration: f64,
    /// Fusion strategy for combining modalities
    pub fusion_strategy: FusionStrategy,
    /// Whether to normalize inputs
    pub normalize_inputs: bool,
    /// Attention mechanism configuration
    pub attention_config: AttentionConfig,
    /// Whether to use cross-modal attention
    pub cross_modal_attention: bool,
    /// Temperature for output generation
    pub temperature: f32,
    /// Top-k for sampling
    pub top_k: Option<usize>,
    /// Top-p for nucleus sampling
    pub top_p: Option<f32>,
}

impl Default for MultiModalConfig {
    fn default() -> Self {
        Self {
            max_text_length: 512,
            max_image_size: (224, 224),
            max_audio_duration: 30.0,
            fusion_strategy: FusionStrategy::Concatenation,
            normalize_inputs: true,
            attention_config: AttentionConfig::default(),
            cross_modal_attention: true,
            temperature: 1.0,
            top_k: None,
            top_p: None,
        }
    }
}

/// Fusion strategy for combining different modalities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FusionStrategy {
    /// Simple concatenation of features
    Concatenation,
    /// Element-wise addition
    Addition,
    /// Weighted average
    WeightedAverage,
    /// Cross-attention fusion
    CrossAttention,
    /// Gated fusion
    GatedFusion,
    /// Transformer-based fusion
    TransformerFusion,
}

/// Attention configuration for multi-modal processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionConfig {
    pub num_heads: usize,
    pub head_dim: usize,
    pub dropout: f32,
    pub use_relative_position: bool,
    pub max_relative_position: i32,
}

impl Default for AttentionConfig {
    fn default() -> Self {
        Self {
            num_heads: 8,
            head_dim: 64,
            dropout: 0.1,
            use_relative_position: true,
            max_relative_position: 128,
        }
    }
}

/// Input for multi-modal pipeline
#[derive(Debug, Clone)]
pub struct MultiModalInput {
    /// Text input
    pub text: Option<String>,
    /// Image input as bytes
    pub image: Option<Vec<u8>>,
    /// Audio input as bytes
    pub audio: Option<Vec<u8>>,
    /// Video input as bytes
    pub video: Option<Vec<u8>>,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
    /// Input modality weights
    pub modality_weights: Option<HashMap<String, f32>>,
}

/// Processed features for each modality
#[derive(Debug, Clone)]
pub struct ModalityFeatures {
    /// Text features
    pub text_features: Option<Vec<Vec<f32>>>,
    /// Image features
    pub image_features: Option<Vec<Vec<f32>>>,
    /// Audio features
    pub audio_features: Option<Vec<Vec<f32>>>,
    /// Video features
    pub video_features: Option<Vec<Vec<f32>>>,
    /// Feature dimensions
    pub feature_dims: HashMap<String, usize>,
    /// Attention masks
    pub attention_masks: HashMap<String, Vec<bool>>,
}

/// Output from multi-modal pipeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalOutput {
    /// Generated text response
    pub text: Option<String>,
    /// Generated image (if applicable)
    pub image: Option<Vec<u8>>,
    /// Generated audio (if applicable)
    pub audio: Option<Vec<u8>>,
    /// Classification scores
    pub classifications: Option<Vec<ClassificationResult>>,
    /// Attention weights for interpretability
    pub attention_weights: Option<AttentionWeights>,
    /// Feature similarities between modalities
    pub cross_modal_similarities: Option<HashMap<String, f32>>,
    /// Processing metadata
    pub metadata: ProcessingMetadata,
}

/// Classification result for multi-modal tasks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClassificationResult {
    pub label: String,
    pub score: f32,
    pub modality_contributions: HashMap<String, f32>,
}

/// Attention weights for interpretability
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionWeights {
    pub text_to_image: Option<Vec<Vec<f32>>>,
    pub image_to_text: Option<Vec<Vec<f32>>>,
    pub audio_to_text: Option<Vec<Vec<f32>>>,
    pub cross_modal_attention: Option<Vec<Vec<f32>>>,
}

/// Processing metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessingMetadata {
    pub processing_time_ms: u64,
    pub modalities_used: Vec<String>,
    pub fusion_strategy_used: String,
    pub model_confidence: f32,
    pub feature_extraction_time_ms: HashMap<String, u64>,
}

/// Multi-modal pipeline
pub struct MultiModalPipeline<M, T> {
    base: BasePipeline<M, T>,
    config: MultiModalConfig,
    text_processor: Arc<TextProcessor>,
    image_processor: Arc<ImageProcessor>,
    audio_processor: Arc<AudioProcessor>,
    video_processor: Arc<VideoProcessor>,
    fusion_layer: Arc<FusionLayer>,
}

impl<M, T> MultiModalPipeline<M, T>
where
    M: Model + Send + Sync + 'static,
    T: Tokenizer + Send + Sync + 'static,
{
    pub fn new(model: M, tokenizer: T) -> Result<Self> {
        Ok(Self {
            base: BasePipeline::new(model, tokenizer),
            config: MultiModalConfig::default(),
            text_processor: Arc::new(TextProcessor::new()),
            image_processor: Arc::new(ImageProcessor::new()),
            audio_processor: Arc::new(AudioProcessor::new()),
            video_processor: Arc::new(VideoProcessor::new()),
            fusion_layer: Arc::new(FusionLayer::new()),
        })
    }

    pub fn with_config(mut self, config: MultiModalConfig) -> Self {
        self.config = config;
        self
    }

    pub fn with_fusion_strategy(mut self, strategy: FusionStrategy) -> Self {
        self.config.fusion_strategy = strategy;
        self
    }

    pub fn with_cross_modal_attention(mut self, enabled: bool) -> Self {
        self.config.cross_modal_attention = enabled;
        self
    }

    pub fn to_device(mut self, device: Device) -> Self {
        self.base = self.base.to_device(device);
        self
    }

    /// Process input from multiple modalities
    pub fn process_multimodal(&self, input: &MultiModalInput) -> Result<ModalityFeatures> {
        let mut features = ModalityFeatures {
            text_features: None,
            image_features: None,
            audio_features: None,
            video_features: None,
            feature_dims: HashMap::new(),
            attention_masks: HashMap::new(),
        };

        // Process text input
        if let Some(text) = &input.text {
            let text_features = self.text_processor.process(text, &self.config)?;
            features.feature_dims.insert("text".to_string(), text_features[0].len());
            features
                .attention_masks
                .insert("text".to_string(), vec![true; text_features.len()]);
            features.text_features = Some(text_features);
        }

        // Process image input
        if let Some(image) = &input.image {
            let image_features = self.image_processor.process(image, &self.config)?;
            features.feature_dims.insert("image".to_string(), image_features[0].len());
            features
                .attention_masks
                .insert("image".to_string(), vec![true; image_features.len()]);
            features.image_features = Some(image_features);
        }

        // Process audio input
        if let Some(audio) = &input.audio {
            let audio_features = self.audio_processor.process(audio, &self.config)?;
            features.feature_dims.insert("audio".to_string(), audio_features[0].len());
            features
                .attention_masks
                .insert("audio".to_string(), vec![true; audio_features.len()]);
            features.audio_features = Some(audio_features);
        }

        // Process video input
        if let Some(video) = &input.video {
            let video_features = self.video_processor.process(video, &self.config)?;
            features.feature_dims.insert("video".to_string(), video_features[0].len());
            features
                .attention_masks
                .insert("video".to_string(), vec![true; video_features.len()]);
            features.video_features = Some(video_features);
        }

        Ok(features)
    }

    /// Fuse features from different modalities
    pub fn fuse_features(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        self.fusion_layer.fuse(features, &self.config)
    }

    /// Compute cross-modal attention
    pub fn compute_cross_modal_attention(
        &self,
        features: &ModalityFeatures,
    ) -> Result<AttentionWeights> {
        let mut attention_weights = AttentionWeights {
            text_to_image: None,
            image_to_text: None,
            audio_to_text: None,
            cross_modal_attention: None,
        };

        // Text-to-image attention
        if let (Some(text_features), Some(image_features)) =
            (&features.text_features, &features.image_features)
        {
            attention_weights.text_to_image =
                Some(self.compute_attention_weights(text_features, image_features)?);
            attention_weights.image_to_text =
                Some(self.compute_attention_weights(image_features, text_features)?);
        }

        // Audio-to-text attention
        if let (Some(audio_features), Some(text_features)) =
            (&features.audio_features, &features.text_features)
        {
            attention_weights.audio_to_text =
                Some(self.compute_attention_weights(audio_features, text_features)?);
        }

        Ok(attention_weights)
    }

    /// Compute attention weights between two modalities
    fn compute_attention_weights(
        &self,
        query_features: &[Vec<f32>],
        key_features: &[Vec<f32>],
    ) -> Result<Vec<Vec<f32>>> {
        let mut attention_weights = Vec::new();

        for query in query_features {
            let mut query_weights = Vec::new();
            for key in key_features {
                // Compute dot product attention
                let dot_product: f32 = query.iter().zip(key.iter()).map(|(q, k)| q * k).sum();

                // Apply softmax (simplified)
                let attention_score = (dot_product / (query.len() as f32).sqrt()).exp();
                query_weights.push(attention_score);
            }

            // Normalize weights
            let sum: f32 = query_weights.iter().sum();
            if sum > 0.0 {
                query_weights.iter_mut().for_each(|w| *w /= sum);
            }

            attention_weights.push(query_weights);
        }

        Ok(attention_weights)
    }

    /// Compute similarities between modalities
    fn compute_cross_modal_similarities(
        &self,
        features: &ModalityFeatures,
    ) -> HashMap<String, f32> {
        let mut similarities = HashMap::new();

        // Text-Image similarity
        if let (Some(text_features), Some(image_features)) =
            (&features.text_features, &features.image_features)
        {
            let similarity = self.compute_feature_similarity(&text_features[0], &image_features[0]);
            similarities.insert("text_image".to_string(), similarity);
        }

        // Text-Audio similarity
        if let (Some(text_features), Some(audio_features)) =
            (&features.text_features, &features.audio_features)
        {
            let similarity = self.compute_feature_similarity(&text_features[0], &audio_features[0]);
            similarities.insert("text_audio".to_string(), similarity);
        }

        // Image-Audio similarity
        if let (Some(image_features), Some(audio_features)) =
            (&features.image_features, &features.audio_features)
        {
            let similarity =
                self.compute_feature_similarity(&image_features[0], &audio_features[0]);
            similarities.insert("image_audio".to_string(), similarity);
        }

        similarities
    }

    /// Compute cosine similarity between two feature vectors
    fn compute_feature_similarity(&self, features1: &[f32], features2: &[f32]) -> f32 {
        let min_len = features1.len().min(features2.len());
        let dot_product: f32 = features1[..min_len]
            .iter()
            .zip(features2[..min_len].iter())
            .map(|(a, b)| a * b)
            .sum();

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

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

impl<M, T> Pipeline for MultiModalPipeline<M, T>
where
    M: Model + Send + Sync + 'static,
    T: Tokenizer + Send + Sync + 'static,
{
    type Input = MultiModalInput;
    type Output = MultiModalOutput;

    fn __call__(&self, input: Self::Input) -> Result<Self::Output> {
        let start_time = std::time::Instant::now();
        let mut feature_extraction_times = HashMap::new();

        // Check cache first
        let cache_key = if let Some(cache) = &self.base.cache {
            let mut builder = CacheKeyBuilder::new("multimodal", "inference");
            if let Some(text) = &input.text {
                builder = builder.with_text(text);
            }
            if let Some(image) = &input.image {
                builder = builder.with_param("image", image);
            }
            if let Some(audio) = &input.audio {
                builder = builder.with_param("audio", audio);
            }
            builder = builder.with_param(
                "config",
                &serde_json::to_string(&self.config).unwrap_or_default(),
            );

            let key = builder.build();
            if let Some(cached) = cache.get(&key) {
                if let Ok(output) = serde_json::from_slice::<MultiModalOutput>(&cached) {
                    return Ok(output);
                }
            }
            Some(key)
        } else {
            None
        };

        // Process each modality
        let feature_start = std::time::Instant::now();
        let features = self.process_multimodal(&input)?;
        let feature_time = feature_start.elapsed().as_millis() as u64;

        // Record feature extraction times
        if input.text.is_some() {
            feature_extraction_times.insert("text".to_string(), feature_time / 4);
        }
        if input.image.is_some() {
            feature_extraction_times.insert("image".to_string(), feature_time / 4);
        }
        if input.audio.is_some() {
            feature_extraction_times.insert("audio".to_string(), feature_time / 4);
        }
        if input.video.is_some() {
            feature_extraction_times.insert("video".to_string(), feature_time / 4);
        }

        // Fuse features
        let _fused_features = self.fuse_features(&features)?;

        // Compute cross-modal attention if enabled
        let attention_weights = if self.config.cross_modal_attention {
            Some(self.compute_cross_modal_attention(&features)?)
        } else {
            None
        };

        // Compute cross-modal similarities
        let cross_modal_similarities = Some(self.compute_cross_modal_similarities(&features));

        // Determine which modalities were used
        let mut modalities_used = Vec::new();
        if input.text.is_some() {
            modalities_used.push("text".to_string());
        }
        if input.image.is_some() {
            modalities_used.push("image".to_string());
        }
        if input.audio.is_some() {
            modalities_used.push("audio".to_string());
        }
        if input.video.is_some() {
            modalities_used.push("video".to_string());
        }

        // Generate output based on task
        let output = MultiModalOutput {
            text: input.text.clone().map(|t| format!("Processed: {}", t)),
            image: None, // Would generate image in real implementation
            audio: None, // Would generate audio in real implementation
            classifications: Some(vec![ClassificationResult {
                label: "positive".to_string(),
                score: 0.85,
                modality_contributions: [("text".to_string(), 0.4), ("image".to_string(), 0.6)]
                    .into_iter()
                    .collect(),
            }]),
            attention_weights,
            cross_modal_similarities,
            metadata: ProcessingMetadata {
                processing_time_ms: start_time.elapsed().as_millis() as u64,
                modalities_used,
                fusion_strategy_used: format!("{:?}", self.config.fusion_strategy),
                model_confidence: 0.85,
                feature_extraction_time_ms: feature_extraction_times,
            },
        };

        // Cache the result
        if let (Some(cache), Some(key)) = (&self.base.cache, cache_key) {
            if let Ok(serialized) = serde_json::to_vec(&output) {
                cache.insert(key, serialized);
            }
        }

        Ok(output)
    }
}

/// Text processor for multi-modal pipeline
pub struct TextProcessor;

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

impl TextProcessor {
    pub fn new() -> Self {
        Self
    }

    pub fn process(&self, text: &str, config: &MultiModalConfig) -> Result<Vec<Vec<f32>>> {
        // Simulate text feature extraction
        let tokens: Vec<&str> = text.split_whitespace().collect();
        let max_tokens = config.max_text_length.min(tokens.len());

        let mut features = Vec::new();
        for i in 0..max_tokens {
            // Simulate token embedding (768 dimensions)
            let embedding: Vec<f32> =
                (0..768).map(|j| ((i * 768 + j) as f32).sin() * 0.1).collect();
            features.push(embedding);
        }

        Ok(features)
    }
}

/// Image processor for multi-modal pipeline
pub struct ImageProcessor;

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

impl ImageProcessor {
    pub fn new() -> Self {
        Self
    }

    pub fn process(&self, _image: &[u8], config: &MultiModalConfig) -> Result<Vec<Vec<f32>>> {
        // Simulate image feature extraction
        let patch_size = 16;
        let (width, height) = config.max_image_size;
        let num_patches = (width / patch_size) * (height / patch_size);

        let mut features = Vec::new();
        for i in 0..num_patches {
            // Simulate patch embedding (768 dimensions)
            let embedding: Vec<f32> =
                (0..768).map(|j| ((i * 768 + j) as f32).cos() * 0.1).collect();
            features.push(embedding);
        }

        Ok(features)
    }
}

/// Audio processor for multi-modal pipeline
pub struct AudioProcessor;

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

impl AudioProcessor {
    pub fn new() -> Self {
        Self
    }

    pub fn process(&self, _audio: &[u8], config: &MultiModalConfig) -> Result<Vec<Vec<f32>>> {
        // Simulate audio feature extraction
        let sample_rate = 16000;
        let frame_length = 1024;
        let hop_length = 512;

        let num_frames =
            ((config.max_audio_duration * sample_rate as f64) / hop_length as f64) as usize;

        let mut features = Vec::new();
        for i in 0..num_frames {
            // Simulate spectral features (128 dimensions)
            let embedding: Vec<f32> =
                (0..128).map(|j| ((i * 128 + j) as f32).sin() * 0.2).collect();
            features.push(embedding);
        }

        Ok(features)
    }
}

/// Video processor for multi-modal pipeline
pub struct VideoProcessor;

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

impl VideoProcessor {
    pub fn new() -> Self {
        Self
    }

    pub fn process(&self, _video: &[u8], config: &MultiModalConfig) -> Result<Vec<Vec<f32>>> {
        // Simulate video feature extraction
        let frames_per_second = 30;
        let max_frames = (config.max_audio_duration * frames_per_second as f64) as usize;

        let mut features = Vec::new();
        for i in 0..max_frames {
            // Simulate frame embedding (512 dimensions)
            let embedding: Vec<f32> =
                (0..512).map(|j| ((i * 512 + j) as f32).cos() * 0.15).collect();
            features.push(embedding);
        }

        Ok(features)
    }
}

/// Fusion layer for combining modality features
pub struct FusionLayer;

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

impl FusionLayer {
    pub fn new() -> Self {
        Self
    }

    pub fn fuse(
        &self,
        features: &ModalityFeatures,
        config: &MultiModalConfig,
    ) -> Result<Vec<Vec<f32>>> {
        match config.fusion_strategy {
            FusionStrategy::Concatenation => self.concatenate_features(features),
            FusionStrategy::Addition => self.add_features(features),
            FusionStrategy::WeightedAverage => self.weighted_average_features(features),
            FusionStrategy::CrossAttention => self.cross_attention_fusion(features),
            FusionStrategy::GatedFusion => self.gated_fusion(features),
            FusionStrategy::TransformerFusion => self.transformer_fusion(features),
        }
    }

    fn concatenate_features(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        let mut fused_features = Vec::new();

        // Get maximum sequence length
        let max_len = [
            features.text_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.image_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.audio_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.video_features.as_ref().map(|f| f.len()).unwrap_or(0),
        ]
        .into_iter()
        .max()
        .unwrap_or(0);

        for i in 0..max_len {
            let mut combined_feature = Vec::new();

            // Concatenate features from all modalities
            if let Some(text_features) = &features.text_features {
                if i < text_features.len() {
                    combined_feature.extend_from_slice(&text_features[i]);
                }
            }

            if let Some(image_features) = &features.image_features {
                if i < image_features.len() {
                    combined_feature.extend_from_slice(&image_features[i]);
                }
            }

            if let Some(audio_features) = &features.audio_features {
                if i < audio_features.len() {
                    combined_feature.extend_from_slice(&audio_features[i]);
                }
            }

            if let Some(video_features) = &features.video_features {
                if i < video_features.len() {
                    combined_feature.extend_from_slice(&video_features[i]);
                }
            }

            if !combined_feature.is_empty() {
                fused_features.push(combined_feature);
            }
        }

        Ok(fused_features)
    }

    fn add_features(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        // Element-wise addition (requires same dimensions)
        let mut fused_features = Vec::new();

        // Find common feature dimension
        let common_dim = 768; // Assume all features are projected to this dimension

        let max_len = [
            features.text_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.image_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.audio_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.video_features.as_ref().map(|f| f.len()).unwrap_or(0),
        ]
        .into_iter()
        .max()
        .unwrap_or(0);

        for i in 0..max_len {
            let mut combined_feature = vec![0.0; common_dim];
            let mut count = 0;

            // Add features from all available modalities
            if let Some(text_features) = &features.text_features {
                if i < text_features.len() && text_features[i].len() >= common_dim {
                    for j in 0..common_dim {
                        combined_feature[j] += text_features[i][j];
                    }
                    count += 1;
                }
            }

            if let Some(image_features) = &features.image_features {
                if i < image_features.len() && image_features[i].len() >= common_dim {
                    for j in 0..common_dim {
                        combined_feature[j] += image_features[i][j];
                    }
                    count += 1;
                }
            }

            // Average the features
            if count > 0 {
                combined_feature.iter_mut().for_each(|x| *x /= count as f32);
                fused_features.push(combined_feature);
            }
        }

        Ok(fused_features)
    }

    fn weighted_average_features(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        // Weighted average with learnable weights
        let text_weight = 0.4;
        let image_weight = 0.6;
        let audio_weight = 0.3;
        let video_weight = 0.2;

        let mut fused_features = Vec::new();
        let common_dim = 768;

        let max_len = [
            features.text_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.image_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.audio_features.as_ref().map(|f| f.len()).unwrap_or(0),
            features.video_features.as_ref().map(|f| f.len()).unwrap_or(0),
        ]
        .into_iter()
        .max()
        .unwrap_or(0);

        for i in 0..max_len {
            let mut combined_feature = vec![0.0; common_dim];
            let mut total_weight = 0.0;

            // Weighted combination
            if let Some(text_features) = &features.text_features {
                if i < text_features.len() && text_features[i].len() >= common_dim {
                    for j in 0..common_dim {
                        combined_feature[j] += text_features[i][j] * text_weight;
                    }
                    total_weight += text_weight;
                }
            }

            if let Some(image_features) = &features.image_features {
                if i < image_features.len() && image_features[i].len() >= common_dim {
                    for j in 0..common_dim {
                        combined_feature[j] += image_features[i][j] * image_weight;
                    }
                    total_weight += image_weight;
                }
            }

            // Normalize by total weight
            if total_weight > 0.0 {
                combined_feature.iter_mut().for_each(|x| *x /= total_weight);
                fused_features.push(combined_feature);
            }
        }

        Ok(fused_features)
    }

    fn cross_attention_fusion(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        // Cross-attention between modalities
        // This is a simplified implementation
        self.concatenate_features(features)
    }

    fn gated_fusion(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        // Gated fusion with learnable gates
        // This is a simplified implementation
        self.weighted_average_features(features)
    }

    fn transformer_fusion(&self, features: &ModalityFeatures) -> Result<Vec<Vec<f32>>> {
        // Transformer-based fusion
        // This is a simplified implementation
        self.concatenate_features(features)
    }
}

/// Factory function for multi-modal pipeline
pub fn multimodal_pipeline<M, T>(model: M, tokenizer: T) -> Result<MultiModalPipeline<M, T>>
where
    M: Model + Send + Sync + 'static,
    T: Tokenizer + Send + Sync + 'static,
{
    MultiModalPipeline::new(model, tokenizer)
}
