//! Model optimization features

use crate::error::Result;
use crate::ml_framework::MLModel;

/// Model optimization techniques
#[derive(Debug, Clone)]
pub enum OptimizationTechnique {
    /// Remove unnecessary operations
    Pruning { sparsity: f32 },
    /// Fuse operations
    OperatorFusion,
    /// Constant folding
    ConstantFolding,
    /// Graph optimization
    GraphOptimization,
    /// Knowledge distillation
    Distillation,
}

/// Model optimizer
pub struct ModelOptimizer {
    techniques: Vec<OptimizationTechnique>,
}

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

impl ModelOptimizer {
    pub fn new() -> Self {
        Self {
            techniques: Vec::new(),
        }
    }

    pub fn add_technique(mut self, technique: OptimizationTechnique) -> Self {
        self.techniques.push(technique);
        self
    }

    /// Optimize model
    pub fn optimize(&self, model: &MLModel) -> Result<MLModel> {
        let mut optimized = model.clone();

        for technique in &self.techniques {
            match technique {
                OptimizationTechnique::Pruning { sparsity } => {
                    optimized = self.apply_pruning(optimized, *sparsity)?;
                }
                OptimizationTechnique::OperatorFusion => {
                    // Implement operator fusion
                }
                _ => {}
            }
        }

        Ok(optimized)
    }

    fn apply_pruning(&self, mut model: MLModel, sparsity: f32) -> Result<MLModel> {
        for (_, tensor) in model.weights.iter_mut() {
            let data = tensor.data.as_slice_mut().expect("Operation failed");
            let threshold = self.compute_pruning_threshold(data, sparsity);

            for val in data.iter_mut() {
                if val.abs() < threshold {
                    *val = 0.0;
                }
            }
        }

        Ok(model)
    }

    fn compute_pruning_threshold(&self, data: &[f32], sparsity: f32) -> f32 {
        let mut sorted: Vec<f32> = data.iter().map(|x| x.abs()).collect();
        sorted.sort_by(|a, b| a.partial_cmp(b).expect("Operation failed"));
        let idx = (sorted.len() as f32 * sparsity) as usize;
        sorted.get(idx).copied().unwrap_or(0.0)
    }
}
