trustformers-models 0.1.1

use trustformers_core::{errors::Result, tensor::Tensor};

/// Output structure for cross-attention operations
#[derive(Debug, Clone)]
pub struct CrossAttentionOutput {
    /// Attention output tensor
    pub output: Tensor,
    /// Attention weights (optional)
    pub attention_weights: Option<Tensor>,
    /// Attention statistics (optional)
    pub attention_stats: Option<AttentionStats>,
}

/// Statistics about attention patterns
#[derive(Debug, Clone)]
pub struct AttentionStats {
    /// Average attention entropy
    pub entropy: f32,
    /// Maximum attention weight
    pub max_weight: f32,
    /// Minimum attention weight
    pub min_weight: f32,
    /// Attention sparsity ratio
    pub sparsity: f32,
    /// Head-wise statistics
    pub head_stats: Vec<HeadStats>,
}

/// Per-head attention statistics
#[derive(Debug, Clone)]
pub struct HeadStats {
    /// Head index
    pub head_idx: usize,
    /// Head entropy
    pub entropy: f32,
    /// Head sparsity
    pub sparsity: f32,
    /// Most attended positions
    pub top_positions: Vec<usize>,
}

/// Create attention mask for cross-attention
pub fn create_attention_mask(
    query_len: usize,
    key_len: usize,
    mask_type: MaskType,
) -> Result<Tensor> {
    match mask_type {
        MaskType::None => {
            // No masking - all positions visible
            Ok(Tensor::zeros(&[query_len, key_len])?)
        },
        MaskType::Causal => {
            // Causal mask - can only attend to previous positions
            let mut mask = vec![vec![0.0f32; key_len]; query_len];
            for (i, row) in mask.iter_mut().enumerate() {
                for j in (i + 1)..key_len.min(query_len) {
                    row[j] = f32::NEG_INFINITY;
                }
            }
            let flattened: Vec<f32> = mask.into_iter().flatten().collect();
            Ok(Tensor::from_vec(flattened, &[query_len, key_len])?)
        },
        MaskType::Local(window_size) => {
            // Local attention mask - only attend to nearby positions
            let mut mask = vec![vec![f32::NEG_INFINITY; key_len]; query_len];
            for (i, row) in mask.iter_mut().enumerate() {
                let start = i.saturating_sub(window_size / 2);
                let end = (i + window_size / 2 + 1).min(key_len);
                for j in start..end {
                    row[j] = 0.0;
                }
            }
            let flattened: Vec<f32> = mask.into_iter().flatten().collect();
            Ok(Tensor::from_vec(flattened, &[query_len, key_len])?)
        },
        MaskType::Custom(custom_mask) => Ok(custom_mask),
    }
}

/// Types of attention masks
#[derive(Debug, Clone)]
pub enum MaskType {
    /// No masking
    None,
    /// Causal masking for autoregressive models
    Causal,
    /// Local window masking
    Local(usize),
    /// Custom mask tensor
    Custom(Tensor),
}

/// Create sparse attention mask
pub fn create_sparse_mask(
    query_len: usize,
    key_len: usize,
    sparsity_ratio: f32,
    pattern: SparsePattern,
) -> Result<Tensor> {
    match pattern {
        SparsePattern::Random => create_random_sparse_mask(query_len, key_len, sparsity_ratio),
        SparsePattern::Block(block_size) => {
            create_block_sparse_mask(query_len, key_len, block_size)
        },
        SparsePattern::Strided(stride) => create_strided_sparse_mask(query_len, key_len, stride),
        SparsePattern::TopK(k) => create_topk_sparse_mask(query_len, key_len, k),
    }
}

/// Sparse attention patterns
#[derive(Debug, Clone)]
pub enum SparsePattern {
    /// Random sparse connections
    Random,
    /// Block-based sparse connections
    Block(usize),
    /// Strided sparse connections
    Strided(usize),
    /// Top-k sparse connections
    TopK(usize),
}

fn create_random_sparse_mask(
    query_len: usize,
    key_len: usize,
    sparsity_ratio: f32,
) -> Result<Tensor> {
    let mut mask = vec![vec![f32::NEG_INFINITY; key_len]; query_len];
    let keep_ratio = 1.0 - sparsity_ratio;

    for (i, row) in mask.iter_mut().enumerate() {
        for (j, val) in row.iter_mut().enumerate() {
            // Simplified random selection - in practice would use proper RNG
            if (i + j) % 10 < (keep_ratio * 10.0) as usize {
                *val = 0.0;
            }
        }
    }

    let flattened: Vec<f32> = mask.into_iter().flatten().collect();
    Tensor::from_vec(flattened, &[query_len, key_len])
}

fn create_block_sparse_mask(query_len: usize, key_len: usize, block_size: usize) -> Result<Tensor> {
    let mut mask = vec![vec![f32::NEG_INFINITY; key_len]; query_len];

    for (i, row) in mask.iter_mut().enumerate() {
        for (j, val) in row.iter_mut().enumerate() {
            let qi = i / block_size;
            let kj = j / block_size;

            // Allow attention within blocks and to adjacent blocks
            if qi == kj || qi.abs_diff(kj) <= 1 {
                *val = 0.0;
            }
        }
    }

    let flattened: Vec<f32> = mask.into_iter().flatten().collect();
    Tensor::from_vec(flattened, &[query_len, key_len])
}

fn create_strided_sparse_mask(query_len: usize, key_len: usize, stride: usize) -> Result<Tensor> {
    let mut mask = vec![vec![f32::NEG_INFINITY; key_len]; query_len];

    for (i, row) in mask.iter_mut().enumerate() {
        for (j, val) in row.iter_mut().enumerate() {
            // Allow attention to positions at regular intervals
            if j % stride == i % stride {
                *val = 0.0;
            }
        }
    }

    let flattened: Vec<f32> = mask.into_iter().flatten().collect();
    Tensor::from_vec(flattened, &[query_len, key_len])
}

fn create_topk_sparse_mask(query_len: usize, key_len: usize, k: usize) -> Result<Tensor> {
    let mut mask = vec![vec![f32::NEG_INFINITY; key_len]; query_len];

    for (i, row) in mask.iter_mut().enumerate() {
        // Allow attention to k nearest positions
        let start = i.saturating_sub(k / 2);
        let end = (i + k / 2 + 1).min(key_len);

        for j in start..end {
            row[j] = 0.0;
        }
    }

    let flattened: Vec<f32> = mask.into_iter().flatten().collect();
    Tensor::from_vec(flattened, &[query_len, key_len])
}

/// Create hierarchical attention mask
pub fn create_hierarchical_mask(
    query_len: usize,
    key_len: usize,
    num_levels: usize,
    pooling_factor: usize,
) -> Result<Vec<Tensor>> {
    let mut masks = Vec::new();

    for level in 0..num_levels {
        let level_pooling = pooling_factor.pow(level as u32);
        let level_query_len = query_len.div_ceil(level_pooling);
        let level_key_len = key_len.div_ceil(level_pooling);

        let mask = create_attention_mask(level_query_len, level_key_len, MaskType::None)?;
        masks.push(mask);
    }

    Ok(masks)
}

/// Compute attention statistics
pub fn compute_attention_stats(
    attention_weights: &Tensor,
    num_heads: usize,
) -> Result<AttentionStats> {
    let shape = attention_weights.shape();
    let _batch_size = shape[0];
    let _seq_len = shape[2];

    // Compute overall statistics
    let entropy = compute_entropy(attention_weights)?;
    let (min_weight, max_weight) = compute_min_max(attention_weights)?;
    let sparsity = compute_sparsity(attention_weights, 1e-6)?;

    // Compute per-head statistics
    let mut head_stats = Vec::new();
    for head in 0..num_heads {
        // Select the specific head weights from the attention tensor
        // Shape: [batch_size, num_heads, seq_len, seq_len] -> [batch_size, seq_len, seq_len]
        let head_weights = attention_weights.select(1, head as i64)?;
        let head_entropy = compute_entropy(&head_weights)?;
        let head_sparsity = compute_sparsity(&head_weights, 1e-6)?;
        let top_positions = compute_top_positions(&head_weights, 5)?;

        head_stats.push(HeadStats {
            head_idx: head,
            entropy: head_entropy,
            sparsity: head_sparsity,
            top_positions,
        });
    }

    Ok(AttentionStats {
        entropy,
        max_weight,
        min_weight,
        sparsity,
        head_stats,
    })
}

fn compute_entropy(_tensor: &Tensor) -> Result<f32> {
    // Simplified entropy computation
    // In practice, this would use proper entropy calculation
    Ok(0.5) // Placeholder
}

fn compute_min_max(_tensor: &Tensor) -> Result<(f32, f32)> {
    // Simplified min/max computation
    Ok((0.0, 1.0)) // Placeholder
}

fn compute_sparsity(_tensor: &Tensor, _threshold: f32) -> Result<f32> {
    // Simplified sparsity computation
    Ok(0.1) // Placeholder
}

fn compute_top_positions(_tensor: &Tensor, _k: usize) -> Result<Vec<usize>> {
    // Simplified top-k computation
    Ok(vec![0, 1, 2, 3, 4]) // Placeholder
}

/// Apply attention dropout
pub fn apply_attention_dropout(
    attention_weights: Tensor,
    dropout_rate: f32,
    training: bool,
) -> Result<Tensor> {
    if training && dropout_rate > 0.0 {
        attention_weights.dropout(dropout_rate)
    } else {
        Ok(attention_weights)
    }
}

/// Compute scaled dot-product attention
pub fn scaled_dot_product_attention(
    query: &Tensor,
    key: &Tensor,
    value: &Tensor,
    mask: Option<&Tensor>,
    scale: f32,
    dropout_rate: f32,
    training: bool,
) -> Result<CrossAttentionOutput> {
    // Compute attention scores
    let key_shape = key.shape();
    let dim0 = key_shape.len().saturating_sub(2);
    let dim1 = key_shape.len().saturating_sub(1);
    let scores = query.matmul(&key.transpose(dim0, dim1)?)?;
    let scores = scores.mul_scalar(scale)?;

    // Apply mask if provided
    let scores = if let Some(mask) = mask { scores.add(mask)? } else { scores };

    // Apply softmax
    let attention_weights = scores.softmax(-1)?;

    // Apply dropout
    let attention_weights = apply_attention_dropout(attention_weights, dropout_rate, training)?;

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

    Ok(CrossAttentionOutput {
        output,
        attention_weights: Some(attention_weights),
        attention_stats: None,
    })
}

/// Reshape tensor for multi-head attention
pub fn reshape_for_multihead(tensor: Tensor, num_heads: usize, head_dim: usize) -> Result<Tensor> {
    let shape = tensor.shape();
    let batch_size = shape[0];
    let seq_len = shape[1];

    tensor.reshape(&[batch_size, seq_len, num_heads, head_dim])?.transpose(1, 2)
}

/// Reshape tensor back from multi-head attention
pub fn reshape_from_multihead(tensor: Tensor, hidden_size: usize) -> Result<Tensor> {
    let shape = tensor.shape();
    let batch_size = shape[0];
    let seq_len = shape[2];

    tensor.transpose(1, 2)?.reshape(&[batch_size, seq_len, hidden_size])
}

/// Pool tensor for hierarchical attention
pub fn pool_tensor(tensor: Tensor, pooling_factor: usize, method: PoolingMethod) -> Result<Tensor> {
    match method {
        PoolingMethod::Average => average_pool_1d(tensor, pooling_factor),
        PoolingMethod::Max => max_pool_1d(tensor, pooling_factor),
        PoolingMethod::Learnable => {
            // Placeholder for learnable pooling
            average_pool_1d(tensor, pooling_factor)
        },
    }
}

/// Pooling methods for hierarchical attention
#[derive(Debug, Clone)]
pub enum PoolingMethod {
    /// Average pooling
    Average,
    /// Max pooling
    Max,
    /// Learnable pooling
    Learnable,
}

fn average_pool_1d(tensor: Tensor, _pooling_factor: usize) -> Result<Tensor> {
    // Simplified average pooling
    // In practice, this would use proper pooling operations
    Ok(tensor)
}

fn max_pool_1d(tensor: Tensor, _pooling_factor: usize) -> Result<Tensor> {
    // Simplified max pooling
    // In practice, this would use proper pooling operations
    Ok(tensor)
}

/// Interpolate tensor for hierarchical attention
pub fn interpolate_tensor(
    tensor: Tensor,
    target_length: usize,
    method: InterpolationMethod,
) -> Result<Tensor> {
    match method {
        InterpolationMethod::Linear => linear_interpolate(tensor, target_length),
        InterpolationMethod::Nearest => nearest_interpolate(tensor, target_length),
    }
}

/// Interpolation methods
#[derive(Debug, Clone)]
pub enum InterpolationMethod {
    /// Linear interpolation
    Linear,
    /// Nearest neighbor interpolation
    Nearest,
}

fn linear_interpolate(tensor: Tensor, _target_length: usize) -> Result<Tensor> {
    // Simplified linear interpolation
    Ok(tensor)
}

fn nearest_interpolate(tensor: Tensor, _target_length: usize) -> Result<Tensor> {
    // Simplified nearest neighbor interpolation
    Ok(tensor)
}