//! Speculative Decoding for Inference Acceleration
//!
//! This module implements speculative decoding to improve inference throughput by using
//! a smaller, faster "draft" model to generate candidate tokens that are then verified
//! by the target model. This can provide significant speedups for autoregressive generation.
//!
//! Key features:
//! - Multiple draft model types (small models, pruned models, cached predictions)
//! - Configurable acceptance thresholds and draft lengths
//! - Performance tracking and adaptive optimization
//! - Safe error handling throughout the pipeline
//!
//! # Layering
//!
//! This module is the *orchestration* layer: strategy traits
//! ([`MedusaDraftModel`], [`SelfSpeculativeModel`]), configs, candidate
//! trees, and the [`AdvancedSpeculativeDecoder`] that combines them. The
//! concrete numeric implementations live at the crate root:
//! [`crate::medusa`] (real multi-head FFN prediction over hidden states,
//! arXiv:2401.10774) and [`crate::lookahead`] (n-gram lookahead decoding).
//! The configs here (`MedusaConfig`, `LookaheadConfig`) parameterize the
//! orchestration strategies; they are distinct from the implementation
//! configs (`crate::medusa::MedusaConfig` is re-exported from the crate
//! root as `MedusaHeadsConfig` to disambiguate).

mod advanced;
mod config;
mod decoder;
mod eagle;
pub mod eagle3;
mod error;
mod lookahead;
mod medusa;
mod metrics;
mod self_spec;
pub mod streaming;
mod traits;
mod tree;
mod types;

pub use advanced::*;
pub use config::*;
pub use decoder::*;
pub use eagle::*;
pub use eagle3::{DynamicDraftTree, Eagle3Config, Eagle3Decoder};
pub use error::*;
pub use lookahead::*;
pub use medusa::*;
pub use metrics::*;
pub use self_spec::*;
pub use streaming::{
    SpeculativeStreamConfig, SpeculativeStreamer, StreamStats, StreamedToken, collect_stream,
};
pub use traits::*;
pub use tree::*;
pub use types::*;

#[cfg(test)]
mod tests;