//! # Ternarization Pipeline
//! 
//! Orchestrates the transformation of high-capacity MoE models into 
//! ternary-native states using Straight-Through Estimation (STE).

use anyhow::Result;
use crate::core::ternary_mapper::TernaryMapper;

pub struct TernarizationPipeline {
    pub mapper: TernaryMapper,
}

impl TernarizationPipeline {
    pub fn new(threshold: f32) -> Self {
        Self {
            mapper: TernaryMapper::new(threshold),
        }
    }

    /// Straight-Through Estimator (STE) for ternary thresholding.
    /// During the forward pass, this acts as the hard ternary map {-1, 0, 1}.
    /// During the backward pass, we pass the gradient through unchanged (identity).
    ///
    /// # Mathematical Formulation
    /// Forward: y = sign(x) if |x| > threshold else 0
    /// Backward: ∂L/∂x ≈ ∂L/∂y
    pub fn forward_ste(x: f32, threshold: f32) -> i8 {
        if x > threshold {
            1
        } else if x < -threshold {
            -1
        } else {
            0
        }
    }

    /// Computes the backward pass gradient approximation for the STE.
    /// In actual training, this would be registered in the Autograd graph.
    pub fn backward_ste(grad_output: f32, x: f32, threshold: f32) -> f32 {
        if x.abs() <= threshold {
            grad_output
        } else {
            0.0 // Suppress gradient outside the threshold boundary if necessary
        }
    }

    pub fn run_mock_ternarization() {
        let weights = vec![0.9, -0.8, 0.1, 0.0, -0.05, 0.7];
        let threshold = 0.5;
        let mapper = TernaryMapper::new(threshold);
        
        let (ternary, _alpha) = mapper.ternarize(&weights, threshold);
        let non_zero = ternary.iter().filter(|&&w| w != 0).count();
        let sparsity = (weights.len() - non_zero) as f32 / weights.len() as f32;

        println!("\n[ALBERT::TERNARY-STE]");
        println!("Weights: {:?}", weights);
        println!("Mapped: {:?}", ternary);
        println!("Sparsity: {:.0}%", sparsity * 100.0);
        println!();
    }

    pub async fn run_forge(&self, model_path: &str) -> Result<()> {
        log::info!("Initiating Ternarization Forge for model at {}", model_path);
        self.load_pretrained_weights(model_path)?;
        self.apply_structural_quantization()?;
        self.validate_coherence()?;
        Ok(())
    }

    fn load_pretrained_weights(&self, _path: &str) -> Result<()> { Ok(()) }
    fn apply_structural_quantization(&self) -> Result<()> { Ok(()) }
    fn validate_coherence(&self) -> Result<()> { Ok(()) }
}

/// Dataset streamer for ternary training.
pub trait DatasetStreamer {
    fn get_next_batch(&self, batch_size: usize) -> (Vec<f32>, Vec<f32>);
}

pub struct MockStreamer {
    pub input_size: usize,
}

impl DatasetStreamer for MockStreamer {
    fn get_next_batch(&self, batch_size: usize) -> (Vec<f32>, Vec<f32>) {
        let input: Vec<f32> = (0..batch_size * self.input_size)
            .map(|_| rand::random::<f32>() * 2.0 - 1.0)
            .collect();
        // Target: simple parity task (sum > 0)
        let target: Vec<f32> = input.chunks(self.input_size)
            .map(|c| if c.iter().sum::<f32>() > 0.0 { 1.0 } else { -1.0 })
            .collect();
        (input, target)
    }
}
