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//! `oxicuda-seq` — Sequence Models & Structured Prediction for OxiCUDA.
//!
//! # Architecture
//!
//! ```text
//! oxicuda-seq
//! ├── hmm/ — Hidden Markov Models (discrete/Gaussian), forward-backward, Viterbi, Baum-Welch
//! ├── crf/ — Linear-chain CRF: forward-backward in score space, L-BFGS training, Viterbi decoding
//! ├── memm/ — Maximum-Entropy Markov Models
//! ├── ssvm/ — Structured SVM (linear-chain) with cutting-plane optimisation
//! ├── structured/ — Sinkhorn CRF: entropy-regularised optimal-transport normalisation
//! ├── beam/ — Generic beam search with length normalisation and diversity penalty
//! ├── ctc/ — CTC loss (forward-backward) + greedy / prefix-beam decoding
//! ├── decoders/ — Stochastic decoders: top-k, nucleus (top-p), typical sampling
//! ├── alignment/ — Needleman-Wunsch, Smith-Waterman, Gotoh affine-gap, Hirschberg
//! ├── grid_crf/ — Pairwise 2D CRF + mean-field inference
//! ├── kalman/ — Linear/EKF Kalman filter, RTS smoother, EM parameter learning
//! ├── mrf/ — General MRF + Ising, Gibbs sampler, loopy belief propagation
//! ├── perceptron/ — Structured + averaged perceptron sequence taggers (Collins 2002)
//! ├── matching/ — Aho–Corasick multi-pattern matching automaton
//! ├── string/ — Manacher longest-palindrome, suffix automaton (DAWG),
//! │ Z-algorithm, suffix array + LCP (SA-IS/Kasai), BWT/FM-index
//! ├── tagging/ — BIO/BIOES conversion + validation, span-based F1 (seqeval)
//! └── metrics/ — Token/sequence accuracy, edit distance, BLEU, perplexity, log-loss
//! ```
//!
//! NOTE on lints: numerical kernels in this crate use straight integer-index
//! loops (`for i in 0..n`) because every body indexes multiple parallel arrays.
//! Rewriting them in iterator form obscures the math and forces awkward `zip`
//! chains, so `clippy::needless_range_loop` is allowed crate-wide.
pub use ;
pub use ;