structured-zstd 0.2.1

use super::*;
use alloc::format;
use alloc::string::String;

/// Every rank past either end of the suffix array reads the noise band, and
/// the band is the same bytes on every run. A corpus whose text repeats the
/// band therefore compared equal at every rank past the end, and the
/// neighbour walks never stopped: training hung on such a corpus. The walks
/// stop at the two noise slots the reference allocates.
#[test]
fn neighbour_walks_stop_at_the_noise_slots() {
    // The corpus is the band itself, and its one suffix is the only rank in
    // the array: every walk leaves the array on its first step.
    let band = noise_band();
    let corpus = Corpus::new(&band);
    let suffixes = Suffixes::new(vec![0], band.len());
    let mut done = vec![false; band.len() + 16];
    let solution = analyze_position(&mut done, &suffixes, 0, &corpus, MIN_RATIO);
    // One suffix cannot repeat `MIN_RATIO` times.
    assert_eq!(solution.length, 0);
}

/// The same hang in the second walk, the one that measures the kept segment's
/// neighbourhood: four copies of the band repeat `MIN_RATIO` times, so the
/// analysis gets past the repetition check and walks the ranks again, reaching
/// the upper noise slot with the band still matching.
#[test]
fn the_measuring_walk_stops_at_the_noise_slot() {
    let band = noise_band();
    let corpus_bytes: Vec<u8> = band
        .iter()
        .copied()
        .cycle()
        .take(4 * NOISE_LENGTH)
        .collect();
    let corpus = Corpus::new(&corpus_bytes);
    // In suffix order: each copy's suffix is a prefix of the one before it.
    let suffixes = Suffixes::new(vec![96, 64, 32, 0], corpus_bytes.len());
    let mut done = vec![false; corpus_bytes.len() + 16];
    let solution = analyze_position(&mut done, &suffixes, 0, &corpus, MIN_RATIO);
    assert!(
        solution.length as usize >= MIN_MATCH_LENGTH,
        "four copies repeat often enough to be kept"
    );
}

/// `count` log lines of a few shapes, each line a sample.
fn log_samples(count: u32) -> (Vec<u8>, Vec<usize>) {
    const SHAPES: [&str; 4] = [
        "ts={i} level=INFO msg=\"flush memtable\" tenant=demo table=orders region=eu-west\n",
        "ts={i} level=WARN msg=\"slow compaction\" tenant=demo table=users region=us-east\n",
        "ts={i} level=INFO msg=\"rotate segment\" tenant=acme table=orders region=eu-west\n",
        "ts={i} level=ERROR msg=\"write stalled\" tenant=acme table=events region=ap-south\n",
    ];
    let mut samples = Vec::new();
    let mut sizes = Vec::new();
    for i in 0..count {
        let line = SHAPES[(i % 4) as usize].replace("{i}", &format!("{:08}", i * 7919));
        sizes.push(line.len());
        samples.extend_from_slice(line.as_bytes());
    }
    (samples, sizes)
}

/// The trainer keeps what the corpus repeats: every segment it returns is a
/// run of the corpus that occurs at least `MIN_RATIO` times, and the content
/// stays within the size asked for.
#[test]
fn the_content_is_made_of_repeated_corpus_runs() {
    let (samples, sizes) = log_samples(400);
    let content = train_legacy_raw(&samples, &sizes, 4096, 0).unwrap();
    assert!(content.len() >= CONTENT_SIZE_MIN && content.len() <= 4096);
    let text = String::from_utf8_lossy(&content);
    for field in [
        "tenant=demo table=orders",
        "msg=\"flush memtable\"",
        "region=eu-west",
    ] {
        assert!(
            text.contains(field),
            "{field} is repeated in every fourth sample"
        );
    }
    // A timestamp is unique per sample, so no whole one survives.
    assert!(!text.contains(&format!("ts={:08}", 3 * 7919)));
}

/// A size below the reference's minimum, a corpus under 512 bytes, and a corpus
/// with nothing repeated are refused, as `ZDICT_trainFromBuffer_legacy` refuses
/// them.
#[test]
fn too_little_to_train_on_is_refused() {
    let (samples, sizes) = log_samples(400);
    assert_eq!(
        train_legacy_raw(&samples, &sizes, DICT_SIZE_MIN - 1, 0),
        Err(TooSmall::Dictionary)
    );
    let (small, small_sizes) = log_samples(5);
    assert!(small.len() < MIN_SAMPLES_SIZE);
    assert_eq!(
        train_legacy_raw(&small, &small_sizes, 4096, 0),
        Err(TooSmall::Corpus)
    );
    let mut state = 0x9E37_79B9u32;
    let noise: Vec<u8> = (0..8192)
        .map(|_| {
            state ^= state << 13;
            state ^= state >> 17;
            state ^= state << 5;
            state as u8
        })
        .collect();
    assert_eq!(
        train_legacy_raw(&noise, &[noise.len()], 4096, 0),
        Err(TooSmall::Content)
    );
}

/// Selectivity decides how often a segment must repeat: `samples >> s`, never
/// below four. With many samples a low selectivity demands more repetitions
/// than a rare shape has, so it keeps less than a high one.
#[test]
fn selectivity_sets_how_often_a_segment_must_repeat() {
    let (mut samples, mut sizes) = log_samples(4096);
    // A shape present in 40 samples: kept when 40 repetitions suffice.
    for i in 0..40 {
        let line = format!("rare shape {i:04} backup completed for volume vol-archive-7\n");
        sizes.push(line.len());
        samples.extend_from_slice(line.as_bytes());
    }
    let strict = train_legacy_raw(&samples, &sizes, 64 * 1024, 6).unwrap();
    let loose = train_legacy_raw(&samples, &sizes, 64 * 1024, 9).unwrap();
    let has_rare = |content: &[u8]| String::from_utf8_lossy(content).contains("vol-archive-7");
    // 4136 >> 6 = 64 repetitions needed: the rare shape has 40.
    assert!(!has_rare(&strict));
    // 4136 >> 9 = 8 repetitions needed.
    assert!(has_rare(&loose));
}