//! Attention mechanisms for translation models

use crate::Result;
use rtx_tensor::Tensor;
use serde::{Deserialize, Serialize};

/// Attention mechanism types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AttentionType {
    /// Additive (Bahdanau) attention
    Additive,
    /// Multiplicative (Luong) attention
    Multiplicative,
    /// Scaled dot-product attention
    ScaledDotProduct,
    /// Multi-head attention
    MultiHead { num_heads: usize },
}

/// Attention configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionConfig {
    pub attention_type: AttentionType,
    pub hidden_size: usize,
    pub dropout_rate: f32,
    pub temperature: f32,
}

impl Default for AttentionConfig {
    fn default() -> Self {
        Self {
            attention_type: AttentionType::ScaledDotProduct,
            hidden_size: 512,
            dropout_rate: 0.1,
            temperature: 1.0,
        }
    }
}

/// Attention layer for sequence-to-sequence models
pub struct AttentionLayer {
    config: AttentionConfig,
}

impl AttentionLayer {
    pub fn new(config: AttentionConfig) -> Self {
        Self { config }
    }

    /// Apply attention mechanism
    pub fn forward(
        &self,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        mask: Option<&Tensor>,
    ) -> Result<(Tensor, Tensor)> {
        match &self.config.attention_type {
            AttentionType::Additive => self.additive_attention(query, key, value, mask),
            AttentionType::Multiplicative => self.multiplicative_attention(query, key, value, mask),
            AttentionType::ScaledDotProduct => {
                self.scaled_dot_product_attention(query, key, value, mask)
            }
            AttentionType::MultiHead { num_heads } => {
                self.multi_head_attention(query, key, value, mask, *num_heads)
            }
        }
    }

    fn scaled_dot_product_attention(
        &self,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        _mask: Option<&Tensor>,
    ) -> Result<(Tensor, Tensor)> {
        // Compute attention scores: Q * K^T / sqrt(d_k)
        let key_shape = key.shape().dims();
        let d_k = key_shape[key_shape.len() - 1] as f32;
        let scores = (query.matmul(&key.transpose(-2, -1)?)? / d_k.sqrt())?;

        // Apply mask if provided
        // Note: Stubbed - where_cond not available in rtx-tensor
        let masked_scores = scores;

        // Apply softmax to get attention weights
        let attention_weights = masked_scores.softmax(-1)?;

        // Apply attention to values
        let context = attention_weights.matmul(value)?;

        Ok((context, attention_weights))
    }

    fn additive_attention(
        &self,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        _mask: Option<&Tensor>,
    ) -> Result<(Tensor, Tensor)> {
        // Mock implementation - in practice would use learned parameters
        let scores = query.matmul(&key.transpose(-2, -1)?)?;
        let attention_weights = scores.softmax(-1)?;
        let context = attention_weights.matmul(value)?;
        Ok((context, attention_weights))
    }

    fn multiplicative_attention(
        &self,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        _mask: Option<&Tensor>,
    ) -> Result<(Tensor, Tensor)> {
        // Mock implementation
        let scores = query.matmul(&key.transpose(-2, -1)?)?;
        let attention_weights = scores.softmax(-1)?;
        let context = attention_weights.matmul(value)?;
        Ok((context, attention_weights))
    }

    fn multi_head_attention(
        &self,
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        mask: Option<&Tensor>,
        _num_heads: usize,
    ) -> Result<(Tensor, Tensor)> {
        // For simplicity, delegate to scaled dot-product
        self.scaled_dot_product_attention(query, key, value, mask)
    }
}

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

    #[test]
    fn test_attention_layer() -> Result<()> {
        let config = AttentionConfig::default();
        let layer = AttentionLayer::new(config);

        let seq_len = 10;
        let hidden_size = 64;
        let device = Device::cuda(0).unwrap_or(Device::default());
        let query = Tensor::randn(&[1, seq_len, hidden_size], &device)?;
        let key = Tensor::randn(&[1, seq_len, hidden_size], &device)?;
        let value = Tensor::randn(&[1, seq_len, hidden_size], &device)?;

        let (context, weights) = layer.forward(&query, &key, &value, None)?;

        assert_eq!(context.shape(), &[1, seq_len, hidden_size]);
        assert_eq!(weights.shape(), &[1, seq_len, seq_len]);

        Ok(())
    }
}