//! # Training Controller
//! 
//! Orchestrates the end-to-end distributed training loop, coordinating
//! data ingestion, forward/backward passes, gradient synchronization,
//! and periodic checkpointing for the MoE-13 frontier model.

use crate::pipelines::data_pipeline::DataPipeline;
use crate::training::distributed::DistributedOrchestrator;
use crate::training::checkpoint::CheckpointManager;
use anyhow::Result;
use std::path::Path;

pub struct TrainingController {
    pub orchestrator: DistributedOrchestrator,
    pub pipeline: DataPipeline,
    pub checkpoint_mgr: CheckpointManager,
}

impl TrainingController {
    pub fn new(orch: DistributedOrchestrator, pipe: DataPipeline, cm: CheckpointManager) -> Self {
        Self { orchestrator: orch, pipeline: pipe, checkpoint_mgr: cm }
    }

    /// Primary training loop for the ternary MoE-13 frontier model.
    pub async fn run_epoch(&self, epoch_id: usize, shard_path: &Path) -> Result<()> {
        // 1. Data Ingestion
        let data = self.pipeline.ingest_stream(shard_path).await?;
        
        // 2. Mock Forward Pass & Gradient Computation
        // In a full implementation, we integrate the Sparse Ternary Matmul kernels here.
        
        // 3. Distributed Sync
        // self.orchestrator.all_reduce_ternary(weights).await?;
        
        // 4. Periodic Checkpointing
        if epoch_id % 10 == 0 {
            // self.checkpoint_mgr.save_checkpoint(&format!("epoch_{}", epoch_id), weights).await?;
        }
        
        Ok(())
    }
}
