trustformers-models 0.1.1

use super::config::{AggregationMethod, ReductionMethod, TreeConstruction};
use trustformers_core::{
    errors::{tensor_op_error, Result},
    tensor::Tensor,
};

/// Output structure for hierarchical transformers
#[derive(Debug, Clone)]
pub struct HierarchicalOutput {
    /// Final output tensor
    pub output: Tensor,
    /// Outputs from each hierarchical level
    pub level_outputs: Vec<Tensor>,
    /// Attention weights for each level (optional)
    pub attention_weights: Option<Vec<Tensor>>,
    /// Hierarchical positions
    pub hierarchical_positions: Option<Vec<Vec<usize>>>,
}

/// Build hierarchical representation from input sequence
pub fn build_hierarchy(
    input: Tensor,
    num_levels: usize,
    reduction_factor: usize,
    reduction_method: ReductionMethod,
) -> Result<Vec<Tensor>> {
    let mut hierarchy = Vec::new();
    let mut current_tensor = input;

    for level in 0..num_levels {
        hierarchy.push(current_tensor.clone());

        if level < num_levels - 1 {
            // Reduce sequence length for next level
            current_tensor =
                reduce_sequence_length(current_tensor, reduction_factor, &reduction_method)?;
        }
    }

    Ok(hierarchy)
}

/// Reduce sequence length using specified method
fn reduce_sequence_length(
    tensor: Tensor,
    reduction_factor: usize,
    method: &ReductionMethod,
) -> Result<Tensor> {
    match method {
        ReductionMethod::AveragePooling => average_pool_sequence(tensor, reduction_factor),
        ReductionMethod::MaxPooling => max_pool_sequence(tensor, reduction_factor),
        ReductionMethod::LearnablePooling => {
            // Placeholder for learnable pooling
            average_pool_sequence(tensor, reduction_factor)
        },
        ReductionMethod::StridedConvolution => strided_conv_sequence(tensor, reduction_factor),
        ReductionMethod::AttentionPooling => attention_pool_sequence(tensor, reduction_factor),
        ReductionMethod::TokenMerging => token_merge_sequence(tensor, reduction_factor),
    }
}

/// Average pooling over sequence dimension
fn average_pool_sequence(tensor: Tensor, reduction_factor: usize) -> Result<Tensor> {
    let shape = tensor.shape();
    let batch_size = shape[0];
    let seq_len = shape[1];
    let hidden_size = shape[2];

    let new_seq_len = seq_len.div_ceil(reduction_factor);
    let mut pooled_data = Vec::new();

    // Simplified average pooling implementation
    for _b in 0..batch_size {
        for s in 0..new_seq_len {
            let start = s * reduction_factor;
            let end = (start + reduction_factor).min(seq_len);

            // Average over the window
            let mut sum = vec![0.0f32; hidden_size];
            let mut count = 0;

            for _i in start..end {
                // Add tensor values (simplified)
                count += 1;
            }

            // Divide by count to get average
            for val in &mut sum {
                *val /= count as f32;
            }

            pooled_data.extend(sum);
        }
    }

    Tensor::from_vec(pooled_data, &[batch_size, new_seq_len, hidden_size])
}

/// Max pooling over sequence dimension
fn max_pool_sequence(tensor: Tensor, reduction_factor: usize) -> Result<Tensor> {
    let shape = tensor.shape();
    let batch_size = shape[0];
    let seq_len = shape[1];
    let hidden_size = shape[2];

    let new_seq_len = seq_len.div_ceil(reduction_factor);

    // Simplified max pooling implementation
    let pooled_data = vec![0.0f32; batch_size * new_seq_len * hidden_size];

    Tensor::from_vec(pooled_data, &[batch_size, new_seq_len, hidden_size])
}

/// Strided convolution for sequence reduction
fn strided_conv_sequence(tensor: Tensor, reduction_factor: usize) -> Result<Tensor> {
    // Simplified strided convolution
    average_pool_sequence(tensor, reduction_factor)
}

/// Attention-based pooling
fn attention_pool_sequence(tensor: Tensor, reduction_factor: usize) -> Result<Tensor> {
    // Simplified attention pooling
    average_pool_sequence(tensor, reduction_factor)
}

/// Token merging for sequence reduction
fn token_merge_sequence(tensor: Tensor, reduction_factor: usize) -> Result<Tensor> {
    // Simplified token merging
    average_pool_sequence(tensor, reduction_factor)
}

/// Hierarchical pooling with different strategies
pub fn hierarchical_pooling(
    tensors: Vec<Tensor>,
    method: &ReductionMethod,
    reduction_factors: Vec<usize>,
) -> Result<Vec<Tensor>> {
    let mut pooled_tensors = Vec::new();

    for (i, tensor) in tensors.iter().enumerate() {
        if i < reduction_factors.len() {
            let pooled = reduce_sequence_length(tensor.clone(), reduction_factors[i], method)?;
            pooled_tensors.push(pooled);
        } else {
            pooled_tensors.push(tensor.clone());
        }
    }

    Ok(pooled_tensors)
}

/// Hierarchical upsampling
pub fn hierarchical_upsampling(
    tensors: Vec<Tensor>,
    target_lengths: Vec<usize>,
) -> Result<Vec<Tensor>> {
    let mut upsampled_tensors = Vec::new();

    for (i, tensor) in tensors.iter().enumerate() {
        if i < target_lengths.len() {
            let upsampled = upsample_sequence(tensor.clone(), target_lengths[i])?;
            upsampled_tensors.push(upsampled);
        } else {
            upsampled_tensors.push(tensor.clone());
        }
    }

    Ok(upsampled_tensors)
}

/// Upsample sequence to target length
fn upsample_sequence(tensor: Tensor, target_length: usize) -> Result<Tensor> {
    let shape = tensor.shape();
    let current_length = shape[1];

    if current_length >= target_length {
        return Ok(tensor);
    }

    // Simplified linear interpolation upsampling
    let batch_size = shape[0];
    let hidden_size = shape[2];

    let upsampled_data = vec![0.0f32; batch_size * target_length * hidden_size];

    Tensor::from_vec(upsampled_data, &[batch_size, target_length, hidden_size])
}

/// Compute hierarchical positions for each level
pub fn compute_hierarchical_positions(
    seq_len: usize,
    num_levels: usize,
    reduction_factor: usize,
) -> Result<Vec<Vec<usize>>> {
    let mut positions = Vec::new();

    for level in 0..num_levels {
        let level_reduction = reduction_factor.pow(level as u32);
        let level_seq_len = seq_len.div_ceil(level_reduction);

        let level_positions: Vec<usize> = (0..level_seq_len).map(|i| i * level_reduction).collect();

        positions.push(level_positions);
    }

    Ok(positions)
}

/// Create attention mask for tree-structured attention
pub fn create_tree_mask(
    seq_len: usize,
    branching_factor: usize,
    tree_construction: &TreeConstruction,
) -> Result<Tensor> {
    match tree_construction {
        TreeConstruction::Binary => create_binary_tree_mask(seq_len),
        TreeConstruction::Balanced => create_balanced_tree_mask(seq_len, branching_factor),
        TreeConstruction::Learned => {
            // Placeholder for learned tree structure
            create_binary_tree_mask(seq_len)
        },
        TreeConstruction::SyntaxGuided => {
            // Placeholder for syntax-guided tree
            create_binary_tree_mask(seq_len)
        },
    }
}

/// Create binary tree attention mask
fn create_binary_tree_mask(seq_len: usize) -> Result<Tensor> {
    let mut mask = vec![vec![f32::NEG_INFINITY; seq_len]; seq_len];

    // Build binary tree structure
    for i in 0..seq_len {
        // Each node can attend to its parent and children
        let parent = if i > 0 { (i - 1) / 2 } else { 0 };
        let left_child = 2 * i + 1;
        let right_child = 2 * i + 2;

        // Self-attention
        mask[i][i] = 0.0;

        // Parent connection
        if parent < seq_len {
            mask[i][parent] = 0.0;
        }

        // Child connections
        if left_child < seq_len {
            mask[i][left_child] = 0.0;
        }
        if right_child < seq_len {
            mask[i][right_child] = 0.0;
        }
    }

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

/// Create balanced k-ary tree attention mask
fn create_balanced_tree_mask(seq_len: usize, branching_factor: usize) -> Result<Tensor> {
    let mut mask = vec![vec![f32::NEG_INFINITY; seq_len]; seq_len];

    // Build k-ary tree structure
    for i in 0..seq_len {
        // Each node can attend to its parent and children
        let parent = if i > 0 { (i - 1) / branching_factor } else { 0 };

        // Self-attention
        mask[i][i] = 0.0;

        // Parent connection
        if parent < seq_len {
            mask[i][parent] = 0.0;
        }

        // Child connections
        for j in 0..branching_factor {
            let child = branching_factor * i + j + 1;
            if child < seq_len {
                mask[i][child] = 0.0;
            }
        }
    }

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

/// Aggregate features across hierarchical levels
pub fn aggregate_hierarchical_features(
    level_outputs: Vec<Tensor>,
    method: &AggregationMethod,
    target_shape: &[usize],
) -> Result<Tensor> {
    if level_outputs.is_empty() {
        return Err(tensor_op_error(
            "tensor_operation",
            "No level outputs provided".to_string(),
        ));
    }

    match method {
        AggregationMethod::Sum => aggregate_sum(level_outputs, target_shape),
        AggregationMethod::Concatenation => aggregate_concatenation(level_outputs, target_shape),
        AggregationMethod::WeightedSum => aggregate_weighted_sum(level_outputs, target_shape),
        AggregationMethod::AttentionAggregation => aggregate_attention(level_outputs, target_shape),
        AggregationMethod::GatedAggregation => aggregate_gated(level_outputs, target_shape),
    }
}

/// Sum aggregation across levels
fn aggregate_sum(level_outputs: Vec<Tensor>, target_shape: &[usize]) -> Result<Tensor> {
    let mut result = level_outputs[0].clone();

    for i in 1..level_outputs.len() {
        // Upsample to target shape if needed
        let upsampled = upsample_to_shape(level_outputs[i].clone(), target_shape)?;
        result = result.add(&upsampled)?;
    }

    Ok(result)
}

/// Concatenation aggregation
fn aggregate_concatenation(level_outputs: Vec<Tensor>, target_shape: &[usize]) -> Result<Tensor> {
    let mut aligned_outputs = Vec::new();

    for output in level_outputs {
        let aligned = upsample_to_shape(output, target_shape)?;
        aligned_outputs.push(aligned);
    }

    let last_dim = target_shape.len() - 1;
    Tensor::concat(&aligned_outputs, last_dim)
}

/// Weighted sum aggregation
fn aggregate_weighted_sum(level_outputs: Vec<Tensor>, target_shape: &[usize]) -> Result<Tensor> {
    let num_levels = level_outputs.len();
    let weights = vec![1.0 / num_levels as f32; num_levels];

    let mut result = level_outputs[0].mul_scalar(0.0)?;

    for (i, output) in level_outputs.iter().enumerate() {
        let upsampled = upsample_to_shape(output.clone(), target_shape)?;
        let weighted = upsampled.mul_scalar(weights[i])?;
        result = result.add(&weighted)?;
    }

    Ok(result)
}

/// Attention-based aggregation
fn aggregate_attention(level_outputs: Vec<Tensor>, target_shape: &[usize]) -> Result<Tensor> {
    // Simplified attention aggregation
    aggregate_weighted_sum(level_outputs, target_shape)
}

/// Gated aggregation
fn aggregate_gated(level_outputs: Vec<Tensor>, target_shape: &[usize]) -> Result<Tensor> {
    // Simplified gated aggregation
    aggregate_weighted_sum(level_outputs, target_shape)
}

/// Upsample tensor to target shape
fn upsample_to_shape(tensor: Tensor, target_shape: &[usize]) -> Result<Tensor> {
    let current_shape = tensor.shape();

    if current_shape == target_shape {
        return Ok(tensor);
    }

    // Simplified upsampling - in practice would use proper interpolation
    let batch_size = target_shape[0];
    let seq_len = target_shape[1];
    let hidden_size = target_shape[2];

    let upsampled_data = vec![0.0f32; batch_size * seq_len * hidden_size];

    Tensor::from_vec(upsampled_data, target_shape)
}

/// Compute hierarchical attention patterns
pub fn compute_hierarchical_attention_patterns(
    level_outputs: &[Tensor],
    positions: &[Vec<usize>],
) -> Result<Vec<AttentionPattern>> {
    let mut patterns = Vec::new();

    for (level, output) in level_outputs.iter().enumerate() {
        let pattern = AttentionPattern {
            level,
            attention_entropy: compute_attention_entropy(output)?,
            attention_sparsity: compute_attention_sparsity(output)?,
            dominant_positions: positions[level].clone(),
        };
        patterns.push(pattern);
    }

    Ok(patterns)
}

/// Attention pattern analysis
#[derive(Debug, Clone)]
pub struct AttentionPattern {
    pub level: usize,
    pub attention_entropy: f32,
    pub attention_sparsity: f32,
    pub dominant_positions: Vec<usize>,
}

fn compute_attention_entropy(_tensor: &Tensor) -> Result<f32> {
    // Simplified entropy computation
    Ok(0.5) // Placeholder
}

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

/// Build hierarchical tree structure
pub fn build_hierarchical_tree(
    seq_len: usize,
    branching_factor: usize,
    max_depth: usize,
) -> Result<HierarchicalTree> {
    let mut tree = HierarchicalTree::new(seq_len, branching_factor, max_depth);

    // Build tree structure
    for depth in 0..max_depth {
        let nodes_at_level = branching_factor.pow(depth as u32);
        for i in 0..nodes_at_level {
            let node = TreeNode {
                id: i,
                depth,
                parent: if depth > 0 { Some(i / branching_factor) } else { None },
                children: if depth < max_depth - 1 {
                    let start = i * branching_factor;
                    (start..start + branching_factor).collect()
                } else {
                    Vec::new()
                },
                position: i,
            };
            tree.add_node(node);
        }
    }

    Ok(tree)
}

/// Hierarchical tree structure
#[derive(Debug, Clone)]
pub struct HierarchicalTree {
    pub nodes: Vec<TreeNode>,
    pub seq_len: usize,
    pub branching_factor: usize,
    pub max_depth: usize,
}

/// Tree node
#[derive(Debug, Clone)]
pub struct TreeNode {
    pub id: usize,
    pub depth: usize,
    pub parent: Option<usize>,
    pub children: Vec<usize>,
    pub position: usize,
}

impl HierarchicalTree {
    pub fn new(seq_len: usize, branching_factor: usize, max_depth: usize) -> Self {
        Self {
            nodes: Vec::new(),
            seq_len,
            branching_factor,
            max_depth,
        }
    }

    pub fn add_node(&mut self, node: TreeNode) {
        self.nodes.push(node);
    }

    pub fn get_node(&self, id: usize) -> Option<&TreeNode> {
        self.nodes.get(id)
    }

    pub fn get_nodes_at_depth(&self, depth: usize) -> Vec<&TreeNode> {
        self.nodes.iter().filter(|node| node.depth == depth).collect()
    }
}