//! ML-Based ComputeBrick Tuner

//!

//! Implements learned cost models for kernel selection and throughput prediction.

//! See: `docs/specifications/ml-tuner-bricks.md`

//!

//! # Architecture

//!

//! ```text

//! BrickProfiler → FeatureExtractor → TunerModel → Recommendations

//! ```

//!

//! # Scientific Foundations

//!

//! - Chen et al. (2018). "TVM: An Automated End-to-End Optimizing Compiler." OSDI '18.

//! - Williams et al. (2009). "Roofline: An Insightful Visual Performance Model." CACM.

//! - Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine."

//!

//! # Example

//!

//! ```rust,ignore

//! use trueno::tuner::{BrickTuner, TunerFeatures};

//!

//! let features = TunerFeatures::builder()

//!     .model_params_b(1.5)

//!     .hidden_dim(1536)

//!     .batch_size(4)

//!     .quant_type(QuantType::Q4K)

//!     .build();

//!

//! let tuner = BrickTuner::load_or_default();

//! let recommendation = tuner.recommend(&features);

//! println!("Predicted: {} tok/s", recommendation.throughput.predicted_tps);

//! ```

// Submodules

mod brick_tuner;

mod data_collector;

pub mod error;

mod evolution;

mod features;

pub(crate) mod helpers;

mod models;

pub mod pretrained;

mod types;

// Re-export all public types

pub use brick_tuner::{BrickTuner, ExperimentSuggestion, TunerRecommendation};

pub use data_collector::{

    ConceptDriftStatus, TrainingSample, TrainingStats, TunerDataCollector, UserFeedback,

};

pub use error::TunerError;

pub use evolution::{CalibrationResult, KernelArm, KernelBandit, OnlineLearner};

pub use features::{FeatureExtractor, RunConfig, TunerFeatures, TunerFeaturesBuilder};

pub use models::{

    BottleneckClassifier, BottleneckPrediction, KernelClassifier, KernelRecommendation,

    ThroughputPrediction, ThroughputRegressor,

};

pub use types::{BottleneckClass, KernelType, QuantType};

// Re-export helpers for tests (crate-internal)

#[cfg(test)]

pub(crate) use helpers::{chrono_lite_now, pad_right};

// ============================================================================

// BrickProfiler Integration

// ============================================================================

use crate::brick::BrickProfiler;

impl BrickProfiler {

    /// Get ML-based tuning recommendations.

    ///

    /// Extracts features from current profile and returns recommendations.

    pub fn get_tuner_recommendations(&self, config: &RunConfig) -> Option<TunerRecommendation> {

        if !self.is_enabled() {

            return None;

        }

        // Create feature extractor

        let extractor = FeatureExtractor::new();

        // Extract features

        let features = extractor.extract(self, config);

        // Get recommendation from global tuner

        let tuner = BrickTuner::new();

        Some(tuner.recommend(&features))

    }

    /// Print tuner recommendations to console.

    pub fn print_tuner_recommendations(&self, config: &RunConfig) {

        if let Some(rec) = self.get_tuner_recommendations(config) {

            let tuner = BrickTuner::new();

            tuner.print_recommendation(&rec);

        } else {

            println!("Tuner recommendations not available (profiler disabled)");

        }

    }

    /// Get tokens per second from profiler.

    pub fn tokens_per_sec(&self) -> Option<f32> {

        let total_ns = self.total_ns();

        let total_tokens = self.total_tokens();

        if total_ns == 0 || total_tokens == 0 {

            return None;

        }

        Some(total_tokens as f32 * 1e9 / total_ns as f32)

    }

}

// ============================================================================

// Tests

// ============================================================================

#[cfg(test)]

mod tests;