mlmf 0.2.0

//! Multi-Modal Processor Implementation
//!
//! This module provides concrete implementations of multi-modal processing
//! capabilities, including cross-modal attention and fusion strategies.

use crate::error::{Error as MlmfError, Result};
use crate::multimodal::*;
use candle_core::{DType, Device, Module, Tensor};
use candle_nn::{Linear, VarBuilder, VarMap};
use std::collections::HashMap;
use std::sync::Arc;

/// Basic multi-modal processor implementation
pub struct BasicMultiModalProcessor {
    config: MultiModalConfig,
    device: Device,
    dtype: DType,

    // Modality-specific encoders (simplified - would be actual models in production)
    _text_encoder_placeholder: bool,
    _image_encoder_placeholder: bool,
    _audio_encoder_placeholder: bool,
    _video_encoder_placeholder: bool,

    // Cross-modal attention layers
    cross_attention_layers: HashMap<(Modality, Modality), CrossModalAttention>,

    // Fusion layers
    fusion_layer: Option<FusionLayer>,

    // Statistics
    stats: MultiModalStats,
}

impl BasicMultiModalProcessor {
    /// Create a new basic multi-modal processor
    pub fn new(config: MultiModalConfig, device: Device, dtype: DType) -> Result<Self> {
        let mut processor = Self {
            config: config.clone(),
            device: device.clone(),
            dtype,
            _text_encoder_placeholder: false,
            _image_encoder_placeholder: false,
            _audio_encoder_placeholder: false,
            _video_encoder_placeholder: false,
            cross_attention_layers: HashMap::new(),
            fusion_layer: None,
            stats: MultiModalStats {
                processing_times: HashMap::new(),
                memory_usage: HashMap::new(),
                attention_stats: HashMap::new(),
                total_time_ms: 0.0,
                samples_processed: 0,
            },
        };

        // Initialize cross-modal attention layers
        processor.initialize_attention_layers()?;

        // Initialize fusion layer
        processor.initialize_fusion_layer()?;

        Ok(processor)
    }

    fn initialize_attention_layers(&mut self) -> Result<()> {
        let modalities: Vec<Modality> = self.config.modalities.keys().cloned().collect();

        for &query_mod in &modalities {
            for &key_mod in &modalities {
                if query_mod != key_mod {
                    let query_config = &self.config.modalities[&query_mod];
                    let key_config = &self.config.modalities[&key_mod];

                    let attention_layer = CrossModalAttention::new(
                        query_config.embedding_dim,
                        key_config.embedding_dim,
                        &self.config.cross_modal_attention,
                        &self.device,
                        self.dtype,
                    )?;

                    self.cross_attention_layers
                        .insert((query_mod, key_mod), attention_layer);
                }
            }
        }

        Ok(())
    }

    fn initialize_fusion_layer(&mut self) -> Result<()> {
        let total_dim: usize = self
            .config
            .modalities
            .values()
            .map(|config| config.embedding_dim)
            .sum();

        self.fusion_layer = Some(FusionLayer::new(
            total_dim,
            &self.config.fusion_strategy,
            &self.device,
            self.dtype,
        )?);

        Ok(())
    }
}

impl MultiModalProcessor for BasicMultiModalProcessor {
    fn process(&self, input: MultiModalInput) -> Result<MultiModalOutput> {
        let start_time = std::time::Instant::now();

        // Step 1: Process each modality
        let mut modality_embeddings = HashMap::new();
        let mut processing_times = HashMap::new();

        for (modality, modality_input) in &input.modality_inputs {
            let modality_start = std::time::Instant::now();

            let preprocessed = self.preprocess_modality(*modality, modality_input.tensor())?;
            let embeddings = self.encode_modality(*modality, &preprocessed)?;

            modality_embeddings.insert(*modality, embeddings);
            processing_times.insert(*modality, modality_start.elapsed().as_secs_f64() * 1000.0);
        }

        // Step 2: Apply cross-modal attention
        let mut attention_weights = HashMap::new();
        let mut attended_embeddings = modality_embeddings.clone();

        for ((query_mod, key_mod), attention_layer) in &self.cross_attention_layers {
            if let (Some(query_emb), Some(key_emb)) = (
                modality_embeddings.get(query_mod),
                modality_embeddings.get(key_mod),
            ) {
                let (attended, weights) = attention_layer.forward(query_emb, key_emb, key_emb)?;
                attended_embeddings.insert(*query_mod, attended);
                attention_weights.insert((*query_mod, *key_mod), weights);
            }
        }

        // Step 3: Fuse modalities
        let fused_embeddings = if let Some(fusion_layer) = &self.fusion_layer {
            let embeddings_vec: Vec<&Tensor> = attended_embeddings.values().collect();
            fusion_layer.fuse(&embeddings_vec)?
        } else {
            // Simple concatenation fallback
            self.simple_concatenation(&attended_embeddings)?
        };

        let total_time = start_time.elapsed().as_secs_f64() * 1000.0;

        Ok(MultiModalOutput {
            fused_embeddings,
            modality_embeddings: attended_embeddings,
            attention_weights,
            metadata: HashMap::new(),
        })
    }

    fn supported_modalities(&self) -> Vec<Modality> {
        self.config.modalities.keys().cloned().collect()
    }

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

    fn preprocess_modality(&self, modality: Modality, input: &Tensor) -> Result<Tensor> {
        let config = self.config.modalities.get(&modality).ok_or_else(|| {
            MlmfError::invalid_config(format!("No configuration for modality: {:?}", modality))
        })?;

        match &config.preprocessing {
            PreprocessingConfig::Text {
                max_length,
                padding,
                truncation,
                ..
            } => self.preprocess_text(input, *max_length, *padding, *truncation),
            PreprocessingConfig::Image {
                resize,
                normalize,
                patch_size,
            } => self.preprocess_image(input, *resize, *normalize, *patch_size),
            PreprocessingConfig::Audio {
                sample_rate,
                frame_length,
                hop_length,
                n_mels,
            } => self.preprocess_audio(input, *sample_rate, *frame_length, *hop_length, *n_mels),
            PreprocessingConfig::Video {
                frame_rate,
                frame_size,
                temporal_window,
            } => self.preprocess_video(input, *frame_rate, *frame_size, *temporal_window),
            PreprocessingConfig::Custom(_) => {
                // For now, return input as-is for custom preprocessing
                Ok(input.clone())
            }
        }
    }

    fn cross_modal_attention(
        &self,
        query_modality: Modality,
        key_modality: Modality,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
    ) -> Result<(Tensor, Tensor)> {
        if let Some(attention_layer) = self
            .cross_attention_layers
            .get(&(query_modality, key_modality))
        {
            attention_layer.forward(query, key, value)
        } else {
            Err(MlmfError::invalid_config(format!(
                "No cross-modal attention layer for {:?} -> {:?}",
                query_modality, key_modality
            )))
        }
    }
}

impl BasicMultiModalProcessor {
    fn encode_modality(&self, modality: Modality, input: &Tensor) -> Result<Tensor> {
        match modality {
            Modality::Text => {
                // Simple linear projection fallback
                self.simple_projection(input, 768)
            }
            Modality::Image => self.simple_projection(input, 2048),
            Modality::Audio => self.simple_projection(input, 512),
            Modality::Video => self.simple_projection(input, 1024),
            Modality::Custom(_) => {
                // For custom modalities, use identity or simple projection
                Ok(input.clone())
            }
        }
    }

    fn preprocess_text(
        &self,
        input: &Tensor,
        max_length: usize,
        padding: bool,
        truncation: bool,
    ) -> Result<Tensor> {
        let shape = input.shape();
        if shape.dims().len() < 2 {
            return Err(MlmfError::invalid_format(
                "Text input must have at least 2 dimensions".to_string(),
            ));
        }

        let seq_len = shape.dims()[1];

        if truncation && seq_len > max_length {
            // Truncate to max_length
            let indices = Tensor::arange(0, max_length as i64, &self.device)?;
            Ok(input.index_select(&indices, 1)?)
        } else if padding && seq_len < max_length {
            // Pad to max_length
            let batch_size = shape.dims()[0];
            let pad_length = max_length - seq_len;
            let padding_tensor =
                Tensor::zeros((batch_size, pad_length), input.dtype(), &self.device)?;
            Ok(Tensor::cat(&[input, &padding_tensor], 1)?)
        } else {
            Ok(input.clone())
        }
    }

    fn preprocess_image(
        &self,
        input: &Tensor,
        _resize: Option<(u32, u32)>,
        normalize: bool,
        _patch_size: u32,
    ) -> Result<Tensor> {
        let mut processed = input.clone();

        // For now, we'll assume input is already in the correct format
        // In a real implementation, you would add resizing and normalization here

        if normalize {
            // Simple normalization: (x - 0.5) / 0.5
            processed = ((processed - 0.5)? / 0.5)?;
        }

        Ok(processed)
    }

    fn preprocess_audio(
        &self,
        input: &Tensor,
        _sample_rate: u32,
        _frame_length: usize,
        _hop_length: usize,
        _n_mels: usize,
    ) -> Result<Tensor> {
        // For now, return input as-is
        // In a real implementation, you would add STFT, mel-spectrogram conversion, etc.
        Ok(input.clone())
    }

    fn preprocess_video(
        &self,
        input: &Tensor,
        _frame_rate: f32,
        _frame_size: (u32, u32),
        _temporal_window: usize,
    ) -> Result<Tensor> {
        // For now, return input as-is
        // In a real implementation, you would add frame sampling, resizing, etc.
        Ok(input.clone())
    }

    fn simple_projection(&self, input: &Tensor, output_dim: usize) -> Result<Tensor> {
        let input_shape = input.shape();
        let input_dim = input_shape.dims()[input_shape.dims().len() - 1];

        // Create a simple linear projection
        let weights = Tensor::randn(0f32, 1f32, (input_dim, output_dim), &self.device)?;
        Ok(input.matmul(&weights)?)
    }

    fn simple_concatenation(&self, embeddings: &HashMap<Modality, Tensor>) -> Result<Tensor> {
        let tensors: Vec<&Tensor> = embeddings.values().collect();
        if tensors.is_empty() {
            return Err(MlmfError::invalid_format(
                "No embeddings to concatenate".to_string(),
            ));
        }

        // Concatenate along the last dimension
        let last_dim = tensors[0].shape().dims().len() - 1;
        Ok(Tensor::cat(&tensors, last_dim)?)
    }
}

/// Cross-modal attention layer
pub struct CrossModalAttention {
    query_proj: Linear,
    key_proj: Linear,
    value_proj: Linear,
    output_proj: Linear,
    num_heads: usize,
    head_dim: usize,
    temperature: f32,
}

impl CrossModalAttention {
    pub fn new(
        query_dim: usize,
        key_dim: usize,
        config: &CrossModalAttentionConfig,
        device: &Device,
        dtype: DType,
    ) -> Result<Self> {
        let head_dim = query_dim / config.num_heads;
        let attention_dim = config.num_heads * head_dim;

        let varmap = VarMap::new();
        let vb = VarBuilder::from_varmap(&varmap, dtype, device);

        let query_proj = Linear::new(
            Tensor::randn(0f32, 1f32, (query_dim, attention_dim), device)?,
            None,
        );
        let key_proj = Linear::new(
            Tensor::randn(0f32, 1f32, (key_dim, attention_dim), device)?,
            None,
        );
        let value_proj = Linear::new(
            Tensor::randn(0f32, 1f32, (key_dim, attention_dim), device)?,
            None,
        );
        let output_proj = Linear::new(
            Tensor::randn(0f32, 1f32, (attention_dim, query_dim), device)?,
            None,
        );

        Ok(Self {
            query_proj,
            key_proj,
            value_proj,
            output_proj,
            num_heads: config.num_heads,
            head_dim,
            temperature: config.temperature,
        })
    }

    pub fn forward(
        &self,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
    ) -> Result<(Tensor, Tensor)> {
        let batch_size = query.shape().dims()[0];
        let seq_len_q = query.shape().dims()[1];
        let seq_len_k = key.shape().dims()[1];

        // Project inputs
        let q = self.query_proj.forward(query)?;
        let k = self.key_proj.forward(key)?;
        let v = self.value_proj.forward(value)?;

        // Reshape for multi-head attention
        let q = q
            .reshape((batch_size, seq_len_q, self.num_heads, self.head_dim))?
            .transpose(1, 2)?; // [batch, heads, seq_q, head_dim]
        let k = k
            .reshape((batch_size, seq_len_k, self.num_heads, self.head_dim))?
            .transpose(1, 2)?; // [batch, heads, seq_k, head_dim]
        let v = v
            .reshape((batch_size, seq_len_k, self.num_heads, self.head_dim))?
            .transpose(1, 2)?; // [batch, heads, seq_k, head_dim]

        // Compute attention scores
        let scores = q.matmul(&k.transpose(2, 3)?)?; // [batch, heads, seq_q, seq_k]
        let scale_factor = (self.head_dim as f64).sqrt() as f32;
        let scale_tensor = Tensor::full(scale_factor, scores.shape(), scores.device())?;
        let scaled_scores = scores.div(&scale_tensor)?;
        let attention_weights = candle_nn::ops::softmax(&scaled_scores, 3)?;

        // Apply attention to values
        let attended = attention_weights.matmul(&v)?; // [batch, heads, seq_q, head_dim]

        // Reshape back
        let attended = attended.transpose(1, 2)?.reshape((
            batch_size,
            seq_len_q,
            self.num_heads * self.head_dim,
        ))?;

        // Apply output projection
        let output = self.output_proj.forward(&attended)?;

        // Average attention weights across heads for interpretability
        let avg_attention = attention_weights.mean(1)?;

        Ok((output, avg_attention))
    }
}

/// Fusion layer for combining multiple modalities
pub struct FusionLayer {
    strategy: FusionStrategy,
    projection: Option<Linear>,
    attention_weights: Option<Linear>,
}

impl FusionLayer {
    pub fn new(
        input_dim: usize,
        strategy: &FusionStrategy,
        device: &Device,
        dtype: DType,
    ) -> Result<Self> {
        let (projection, attention_weights) = match strategy {
            FusionStrategy::EarlyFusion => (None, None),
            FusionStrategy::LateFusion => {
                let proj = Linear::new(
                    Tensor::randn(0f32, 1f32, (input_dim, input_dim), device)?,
                    Some(Tensor::zeros(input_dim, dtype, device)?),
                );
                (Some(proj), None)
            }
            FusionStrategy::AttentionFusion { attention_dim } => {
                let proj = Linear::new(
                    Tensor::randn(0f32, 1f32, (input_dim, *attention_dim), device)?,
                    None,
                );
                let attn = Linear::new(
                    Tensor::randn(0f32, 1f32, (*attention_dim, 1), device)?,
                    None,
                );
                (Some(proj), Some(attn))
            }
            _ => (None, None),
        };

        Ok(Self {
            strategy: strategy.clone(),
            projection,
            attention_weights,
        })
    }

    pub fn fuse(&self, embeddings: &[&Tensor]) -> Result<Tensor> {
        if embeddings.is_empty() {
            return Err(MlmfError::invalid_format(
                "No embeddings to fuse".to_string(),
            ));
        }

        match &self.strategy {
            FusionStrategy::EarlyFusion => {
                // Simple concatenation
                let last_dim = embeddings[0].shape().dims().len() - 1;
                Ok(Tensor::cat(embeddings, last_dim)?)
            }
            FusionStrategy::LateFusion => {
                // Weighted sum with learned projection
                let stacked = Tensor::stack(embeddings, 0)?;
                let mean_embedding = stacked.mean(0)?;

                if let Some(proj) = &self.projection {
                    Ok(proj.forward(&mean_embedding)?)
                } else {
                    Ok(mean_embedding)
                }
            }
            FusionStrategy::AttentionFusion { .. } => {
                // Attention-weighted fusion
                if let (Some(proj), Some(attn_weights)) =
                    (&self.projection, &self.attention_weights)
                {
                    let stacked = Tensor::stack(embeddings, 1)?; // [batch, num_modalities, dim]
                    let projected = proj.forward(&stacked)?;
                    let attention_scores = attn_weights.forward(&projected)?;
                    let attention_weights = candle_nn::ops::softmax(&attention_scores, 1)?;

                    // Weighted sum
                    Ok((stacked * attention_weights)?.sum(1)?)
                } else {
                    // Fallback to simple mean
                    let stacked = Tensor::stack(embeddings, 0)?;
                    Ok(stacked.mean(0)?)
                }
            }
            _ => {
                // Default to concatenation
                let last_dim = embeddings[0].shape().dims().len() - 1;
                Ok(Tensor::cat(embeddings, last_dim)?)
            }
        }
    }
}

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

    #[test]
    fn test_basic_processor_creation() -> Result<()> {
        let config = MultiModalConfig::default();
        let device = Device::Cpu;
        let processor = BasicMultiModalProcessor::new(config, device, DType::F32)?;

        assert_eq!(processor.supported_modalities().len(), 2);
        assert!(processor.supported_modalities().contains(&Modality::Text));
        assert!(processor.supported_modalities().contains(&Modality::Image));

        Ok(())
    }
}