mahbot 0.7.3

//! Audio-subsystem utilities: PCM math and resampling, deterministic
//! test-signal generation for the voice tests, and the streaming SHA-256
//! verifier the audio model downloads share.
//!
//! These live here (not in `crate::util`) because the whole audio subsystem
//! is macOS-only.

use anyhow::{Context as _, Result};
use rand::RngExt;
#[cfg(any(test, feature = "voice-tests"))]
use rand::SeedableRng;
use sha2::{Digest, Sha256};
use std::fs::File;
use std::io::Read;
use std::path::Path;

use crate::util::hex_string;

// ── PCM resampling ─────────────────────────────────────────────────────

/// Resample PCM audio from one sample rate to another using linear
/// interpolation with a 3-tap binomial anti-aliasing filter for downsampling.
///
/// This is the canonical implementation. All other resample call sites
/// delegate to this one, except `audio::local_transcriber`, which uses
/// `qwen_asr::audio::resample` directly.
///
/// When `from_rate > to_rate` (downsampling), a simple binomial low-pass filter
/// is applied to attenuate frequencies above the new Nyquist before decimation.
/// Without this filter, linear interpolation introduces aliasing — high-frequency
/// content above `to_rate / 2` folds back into the audible range as noise.
///
/// The 3-tap binomial `[0.25, 0.5, 0.25]` gives reasonable stopband attenuation
/// (~6 dB at 0.25 normalised) for speech audio. For 48 kHz → 16 kHz this
/// attenuates content above ~8 kHz.
///
/// # Aliasing trade-off
///
/// Linear interpolation introduces aliasing when downsampling even with the
/// pre-filter — the filter only provides ~6 dB stopband attenuation. This is
/// acceptable for speech processing and wake word training data augmentation.
/// If aliasing artifacts prove problematic, a sinc-based resampler can be
/// substituted here — all call sites except `local_transcriber` (which
/// resamples via `qwen_asr` itself) benefit automatically.
#[must_use]
#[expect(
    clippy::cast_possible_truncation,
    clippy::cast_sign_loss,
    clippy::cast_precision_loss
)]
pub(crate) fn resample_audio(samples: &[f32], from_rate: u32, to_rate: u32) -> Vec<f32> {
    if from_rate == to_rate {
        return samples.to_vec();
    }
    let ratio = f64::from(to_rate) / f64::from(from_rate);
    let output_len = (samples.len() as f64 * ratio).ceil() as usize;

    // Anti-aliasing filter for downsampling
    let filtered: Vec<f32> = if from_rate > to_rate && samples.len() >= 3 {
        let mut out = Vec::with_capacity(samples.len());
        out.push(samples[0] * 0.75 + samples[1] * 0.25);
        for i in 1..samples.len() - 1 {
            out.push(samples[i - 1] * 0.25 + samples[i] * 0.5 + samples[i + 1] * 0.25);
        }
        out.push(samples[samples.len() - 2] * 0.25 + samples[samples.len() - 1] * 0.75);
        out
    } else {
        samples.to_vec()
    };

    let mut output = Vec::with_capacity(output_len);
    for i in 0..output_len {
        let src_pos = i as f64 / ratio;
        let src_idx = src_pos as usize;
        let frac = src_pos - src_idx as f64;
        if src_idx + 1 < filtered.len() {
            output.push(
                (f64::from(filtered[src_idx]) * (1.0 - frac)
                    + f64::from(filtered[src_idx + 1]) * frac) as f32,
            );
        } else if src_idx < filtered.len() {
            output.push(filtered[src_idx]);
        } else {
            output.push(0.0);
        }
    }
    output
}

// ── Shared model-integrity verifier ────────────────────────────────────

/// Verify a file's SHA256 hash matches the expected hex string.
///
/// Shared model-integrity verifier extracted from the three near-identical
/// private streaming copies in `audio::tts`, `audio::local_transcriber`, and
/// `audio::voice` (whose empty-`expected` skip semantics and error wording had
/// drifted).  Model-download integrity is security-adjacent, so the copies are
/// consolidated here.
///
/// * If `expected` is empty, verification is skipped (returns `Ok`) — the
///   canonical "no hash configured" semantics that won the reconciliation.
/// * Uses streaming SHA256 via [`Sha256::update`] to avoid loading the entire
///   file into memory (model files can be multiple GB).
pub(crate) fn verify_sha256(path: &Path, expected: &str) -> Result<()> {
    if expected.is_empty() {
        return Ok(()); // no hash configured — skip verification
    }

    let mut hasher = Sha256::new();
    let mut file = File::open(path)
        .with_context(|| format!("Failed to open {} for SHA256 verification", path.display()))?;
    let mut buf = vec![0u8; 65536]; // 64 KB heap buffer
    loop {
        let n = file.read(&mut buf)?;
        if n == 0 {
            break;
        }
        hasher.update(&buf[..n]);
    }
    let actual = hex_string(&hasher.finalize());
    if actual != expected {
        anyhow::bail!(
            "SHA256 mismatch for {}: expected {expected}, got {actual}",
            path.display()
        );
    }
    Ok(())
}

// Tests consolidated from the three former private copies (tts,
// local_transcriber, voice).  The file-not-found error path is covered with a
// NON-empty expected hash because the shared empty-hash skip returns Ok before
// opening the file (sanctioned reconciliation of the transcriber's original
// `verify_sha256(path, "").is_err()` assertion).

#[cfg(test)]
mod verify_sha256_tests {
    use super::verify_sha256;

    fn sha256_hex(data: &[u8]) -> String {
        use sha2::{Digest, Sha256};
        let mut hasher = Sha256::new();
        hasher.update(data);
        crate::util::hex_string(&hasher.finalize())
    }

    #[test]
    fn verify_sha256_cases() {
        // Matching and mismatching hashes over a written file.
        let dir = tempfile::tempdir().unwrap();
        let path = dir.path().join("test.bin");
        std::fs::write(&path, b"test data").unwrap();
        let hash = sha256_hex(b"test data");
        assert!(
            verify_sha256(&path, &hash).is_ok(),
            "matching hash should pass"
        );
        assert!(
            verify_sha256(&path, &sha256_hex(b"other data")).is_err(),
            "mismatching hash should fail"
        );

        // Empty expected hash → Ok without opening the file (skip semantics).
        let dir = tempfile::tempdir().unwrap();
        let missing = dir.path().join("nonexistent.bin");
        assert!(
            verify_sha256(&missing, "").is_ok(),
            "empty hash should skip verification"
        );
        // The not-found error path requires a non-empty expected hash.
        assert!(
            verify_sha256(&missing, &sha256_hex(b"anything")).is_err(),
            "missing file with non-empty hash should fail"
        );
    }
}

// ── Audio utility functions (canonical implementations) ────────────────

/// Generate pink noise (1/f spectrum) using the Voss-McCartney algorithm.
///
/// Uses a seeded RNG for reproducibility.  The high-pass delta variant
/// naturally removes DC bias.  Output is normalized to unit RMS.
///
/// # Voss-McCartney variants
///
/// There are several common variants of Voss-McCartney pink noise:
///
/// * **High-pass delta** (this implementation): stores previous value per
///   octave, emits the difference (new - prev).  Removes DC bias naturally.
/// * **Direct-sum**: sums all octave values directly.  May accumulate DC bias.
///
/// The canonical implementation uses 16 octaves (~3 dB/octave rolloff down
/// to 0.03 Hz at 16 kHz) and the high-pass delta variant for DC-free output.
///
/// Used by the voice-pipeline benchmark (`voice-tests`) and default-build unit tests.
#[cfg(any(test, feature = "voice-tests"))]
pub(crate) fn generate_pink_noise(len: usize, mut rng: impl rand::Rng) -> Vec<f32> {
    const NUM_OCTAVES: usize = 16;
    let mut values = [0.0f32; NUM_OCTAVES];
    let mut outputs = [0.0f32; NUM_OCTAVES];
    let mut sample_count = 0u64;
    let mut noise = Vec::with_capacity(len);

    for _ in 0..len {
        sample_count += 1;
        let mut sum = 0.0;
        for octave in 0..NUM_OCTAVES {
            // Update this octave's generator at intervals of 2^octave samples.
            if sample_count.is_multiple_of(1u64 << octave) {
                values[octave] = rng.random::<f32>() * 2.0 - 1.0;
            }
            // High-pass delta: emit difference instead of direct value.
            let new_val = values[octave];
            let delta = new_val - outputs[octave];
            outputs[octave] = new_val;
            sum += delta;
        }
        noise.push(sum);
    }

    // Normalize to unit RMS
    let rms = compute_rms(&noise).max(1e-10);
    for s in &mut noise {
        *s /= rms;
    }

    noise
}

/// Pink-noise alias of [`add_noise_color`] — kept for the recipe's variant-4
/// call sites; the SNR-scaling/clamp arithmetic lives in one place.
/// Used by the voice-pipeline bench (`voice-tests`) and default-build unit tests.
#[cfg(any(test, feature = "voice-tests"))]
pub(crate) fn add_noise(pcm: &[f32], snr_db: f32, seed: u64) -> Vec<f32> {
    add_noise_color(pcm, snr_db, NoiseColor::Pink, seed)
}

/// Apply a fixed gain to PCM audio.
///
/// DETERMINISTIC — no RNG involved. The gain is `10^(gain_db / 20)`.
/// Negative values attenuate, positive values amplify.
/// Used by the voice-pipeline bench (`voice-tests`) and default-build unit tests.
#[cfg(any(test, feature = "voice-tests"))]
pub(crate) fn apply_gain(pcm: &[f32], gain_db: f32) -> Vec<f32> {
    let amp = 10.0_f32.powf(gain_db / 20.0);
    pcm.iter().map(|&s| s * amp).collect()
}

/// Noise colors supported by the noise-mixing helpers (voice-tests bench and
/// default-build unit tests).
#[cfg(any(test, feature = "voice-tests"))]
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub(crate) enum NoiseColor {
    Pink,
    // Constructed only by the voice-tests bench augmentation; the default
    // test build exercises Pink only.
    #[cfg_attr(not(feature = "voice-tests"), allow(dead_code))]
    Brown,
}

/// Add color noise to PCM audio at the given SNR (deterministic, seeded).
///
/// Mirrors [`add_noise`]'s arithmetic (unit-RMS noise scaled to the SNR
/// target, clamped mix) with the color selector.  `Brown` is a leaky
/// integration of white noise (DC-free-ish, low-frequency dominant).
/// Used by the voice-pipeline bench (`voice-tests`) and default-build unit tests.
#[cfg(any(test, feature = "voice-tests"))]
pub(crate) fn add_noise_color(pcm: &[f32], snr_db: f32, color: NoiseColor, seed: u64) -> Vec<f32> {
    let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
    let signal_rms = compute_rms(pcm).max(1e-10);
    let noise: Vec<f32> = match color {
        NoiseColor::Pink => generate_pink_noise(pcm.len(), &mut rng),
        NoiseColor::Brown => {
            let mut acc = 0.0f32;
            (0..pcm.len())
                .map(|_| {
                    acc = 0.999 * acc + (rng.random::<f32>() * 2.0 - 1.0);
                    acc
                })
                .collect()
        }
    };
    let noise_rms_target = signal_rms * 10.0_f32.powf(-snr_db / 20.0);
    let noise_rms_current = compute_rms(&noise).max(1e-10);
    let scale = noise_rms_target / noise_rms_current;
    pcm.iter()
        .zip(noise.iter())
        .map(|(&s, &n)| (s + n * scale).clamp(-1.0, 1.0))
        .collect()
}

/// Compute the RMS (root mean square) of audio samples.
///
/// Returns `0.0` for empty input.  Ungated so production hot
/// paths (AGC, noise generation, utterance quality) share one implementation
/// instead of hand-rolling `sum(x²)/n → sqrt`.  Callers that need a
/// divide-by-zero floor for degenerate all-zero input apply `.max(1e-10)` at
/// the call site — it is deliberately NOT part of this function.
#[expect(clippy::cast_precision_loss)]
pub(crate) fn compute_rms(samples: &[f32]) -> f32 {
    if samples.is_empty() {
        return 0.0;
    }
    let sum_sq: f32 = samples.iter().map(|&s| s * s).sum();
    (sum_sq / samples.len() as f32).sqrt()
}

/// Compute both Box-Muller branches from two uniform draws in `(0, 1]`.
///
/// `z1 = sqrt(-2 ln u1) cos(2π u2)`, `z2 = sqrt(-2 ln u1) sin(2π u2)`.
/// Shared math for the bench's EPSILON-clamp pair sampler below.
#[must_use]
#[inline]
pub(crate) fn gaussian_pair_from_uniforms(u1: f32, u2: f32) -> (f32, f32) {
    let r = (-2.0 * u1.ln()).sqrt();
    let theta = 2.0 * core::f32::consts::PI * u2;
    (r * theta.cos(), r * theta.sin())
}

/// Draw a standard-normal pair via Box-Muller with EPSILON-clamped draws
/// (2 draws per 2 samples).  Preserves the bench's draw sequence and
/// degenerate-input semantics (`u1`/`u2` floored at [`f32::EPSILON`] rather
/// than re-rolled).  Used by the `voice-tests` bench and the seeded-sequence
/// equivalence test.
#[cfg_attr(not(any(feature = "voice-tests", test)), allow(dead_code))]
pub(crate) fn sample_gaussian_pair_clamped(rng: &mut impl rand::Rng) -> (f32, f32) {
    let u1: f32 = rng.random::<f32>().max(f32::EPSILON);
    let u2: f32 = rng.random::<f32>().max(f32::EPSILON);
    gaussian_pair_from_uniforms(u1, u2)
}

/// Apply speed perturbation by resampling.
///
/// Changes both speed and pitch (time-domain resampling). For wake word
/// training data diversity, this is acceptable and does not require
/// pitch-preserving time-stretching.
///
/// # Arguments
///
/// * `samples` — Audio PCM f32 at `sample_rate`.
/// * `sample_rate` — Original sample rate in Hz.
/// * `factor` — Speed factor: >1.0 = faster (fewer samples), <1.0 = slower
///   (more samples). Typical range: 0.8-1.2 (±20%).
///
/// # Returns
///
/// Speed-adjusted audio at the original `sample_rate`.
/// Used by the voice-pipeline bench (`voice-tests`) and default-build unit tests.
#[must_use]
#[cfg(any(test, feature = "voice-tests"))]
#[expect(
    clippy::cast_possible_truncation,
    clippy::cast_sign_loss,
    clippy::cast_precision_loss
)]
pub(crate) fn speed_perturbation(samples: &[f32], sample_rate: u32, factor: f32) -> Vec<f32> {
    if samples.is_empty() || (factor - 1.0).abs() < 1e-6 {
        return samples.to_vec();
    }
    // Speed perturbation via resampling: change the effective rate
    // new_rate = sample_rate * factor
    let effective_rate = (sample_rate as f32 * factor) as u32;
    // Resample from effective_rate back to sample_rate
    // This produces the same duration as original but with shifted pitch
    resample_audio(samples, effective_rate, sample_rate)
}

// ── Audio utility regression tests ─────────────────────────────────────
// In-place regression net for speed perturbation, gain, noise, and pink noise.
// The utilities are gated `any(test, voice-tests)`, so the tests run in the
// default build.

#[cfg(test)]
mod audio_util_tests {
    use super::{add_noise, apply_gain, generate_pink_noise, speed_perturbation};
    use rand::SeedableRng;

    #[test]
    fn test_speed_perturbation_identity() {
        // rate=1.0 should return approximately the original
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..100 — exact in f32
        let pcm: Vec<f32> = (0..100).map(|i| (i as f32) / 100.0).collect();
        let result = speed_perturbation(&pcm, 16000, 1.0);
        assert_eq!(result.len(), pcm.len(), "identity should preserve length");
        for (a, b) in pcm.iter().zip(result.iter()) {
            assert!((a - b).abs() < 1e-5, "identity should preserve values");
        }
    }

    #[test]
    fn test_speed_perturbation_rates() {
        // Slow down: rate=0.5 should produce more samples
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..100 — exact in f32
        let pcm: Vec<f32> = (0..100).map(|i| (i as f32) / 100.0).collect();
        let slowed = speed_perturbation(&pcm, 16000, 0.5);
        assert!(
            slowed.len() > pcm.len(),
            "rate < 1 should increase sample count"
        );
        // Speed up: rate=2.0 should produce fewer samples
        let sped_up = speed_perturbation(&pcm, 16000, 2.0);
        assert!(
            sped_up.len() < pcm.len(),
            "rate > 1 should decrease sample count"
        );
    }

    #[test]
    fn test_speed_perturbation_determinism() {
        // Same input + same rate → same output
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..100 — exact in f32
        let pcm: Vec<f32> = (0..100).map(|i| (i as f32) / 100.0).collect();
        let a = speed_perturbation(&pcm, 16000, 0.95);
        let b = speed_perturbation(&pcm, 16000, 0.95);
        assert_eq!(a, b, "deterministic speed perturbation");
    }

    #[test]
    fn test_apply_gain_determinism() {
        // apply_gain must be deterministic: same PCM + same gain_db → same output
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..50 — exact in f32
        let pcm: Vec<f32> = (0..50).map(|i| (i as f32 - 25.0) / 25.0).collect();
        let a = apply_gain(&pcm, -3.0);
        let b = apply_gain(&pcm, -3.0);
        assert_eq!(a, b, "apply_gain must be deterministic");
    }

    #[test]
    fn test_apply_gain_db_conversion() {
        // 0 dB → unity gain (output == input)
        let pcm: Vec<f32> = vec![0.5, -0.3, 0.1, -0.7, 0.9];
        let unity = apply_gain(&pcm, 0.0);
        for (a, b) in pcm.iter().zip(unity.iter()) {
            assert!((a - b).abs() < 1e-6, "0 dB gain should be unity");
        }
        // -6 dB → amplitude halved (10^(-6/20) ≈ 0.5)
        let attenuated = apply_gain(&pcm, -6.0);
        let expected_amp = 10.0_f32.powf(-6.0 / 20.0);
        for (orig, atten) in pcm.iter().zip(attenuated.iter()) {
            assert!(
                (atten - orig * expected_amp).abs() < 1e-6,
                "-6 dB gain should multiply by {expected_amp}"
            );
        }
        // +6 dB → amplitude doubled (10^(6/20) ≈ 2.0)
        let amplified = apply_gain(&pcm, 6.0);
        let expected_amp2 = 10.0_f32.powf(6.0 / 20.0);
        for (orig, amp) in pcm.iter().zip(amplified.iter()) {
            assert!(
                (amp - orig * expected_amp2).abs() < 1e-6,
                "+6 dB gain should multiply by {expected_amp2}"
            );
        }
    }

    #[test]
    fn test_add_noise_determinism() {
        // Same PCM + same SNR + same seed → same output
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..200 — exact in f32
        let pcm: Vec<f32> = (0..200).map(|i| (i as f32 - 100.0) / 100.0).collect();
        let a = add_noise(&pcm, 25.0, 42);
        let b = add_noise(&pcm, 25.0, 42);
        assert_eq!(a.len(), b.len(), "add_noise output length should match");
        assert_eq!(a, b, "add_noise with same seed must be deterministic");
    }

    #[test]
    fn test_add_noise_seed_variation() {
        // Different seed → different output
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..200 — exact in f32
        let pcm: Vec<f32> = (0..200).map(|i| (i as f32 - 100.0) / 100.0).collect();
        let a = add_noise(&pcm, 25.0, 42);
        let b = add_noise(&pcm, 25.0, 99);
        assert_ne!(a, b, "different seeds should produce different output");
    }

    #[test]
    fn test_add_noise_snr_approximation() {
        // At very high SNR (100 dB), the output should be very close to input
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..1000 — exact in f32
        let pcm: Vec<f32> = (0..1000).map(|i| (i as f32 - 500.0) / 500.0).collect();
        let noisy = add_noise(&pcm, 100.0, 42);
        // Compute actual SNR of output vs input
        let signal_power: f32 = pcm.iter().map(|&s| s * s).sum();
        let noise_power: f32 = pcm
            .iter()
            .zip(noisy.iter())
            .map(|(&s, &n)| (n - s) * (n - s))
            .sum();
        let actual_snr_db = 10.0 * (signal_power / noise_power.max(1e-10)).log10();
        assert!(
            actual_snr_db > 80.0,
            "at 100 dB target, actual SNR should be high (was {actual_snr_db:.1} dB)"
        );
    }

    #[test]
    fn test_generate_pink_noise_properties() {
        // Pink noise should have positive and negative values
        let noise = generate_pink_noise(1000, rand::rngs::StdRng::seed_from_u64(42));
        assert_eq!(noise.len(), 1000);
        let has_positive = noise.iter().any(|&s| s > 0.0);
        let has_negative = noise.iter().any(|&s| s < 0.0);
        assert!(has_positive, "pink noise should have positive values");
        assert!(has_negative, "pink noise should have negative values");
        // RMS should be near 1.0 (normalized)
        let rms = super::compute_rms(&noise);
        assert!(
            (rms - 1.0).abs() < 0.1,
            "pink noise RMS should be near 1.0 (was {rms})"
        );
    }

    #[test]
    fn test_augmentation_variants_are_different() {
        // Verify that the 4 augmentation strategies produce different results
        #[expect(clippy::cast_precision_loss)] // i ∈ 0..500 — exact in f32
        let pcm: Vec<f32> = (0..500).map(|i| (i as f32 - 250.0) / 250.0).collect();
        let speed_down = speed_perturbation(&pcm, 16000, 0.95);
        let speed_up = speed_perturbation(&pcm, 16000, 1.05);
        let volume_down = apply_gain(&pcm, -3.0);
        let noise = add_noise(&pcm, 25.0, 42);

        // Each variant should differ from original AND each other
        assert_ne!(speed_down, pcm, "speed-down should differ from original");
        assert_ne!(speed_up, pcm, "speed-up should differ from original");
        assert_ne!(volume_down, pcm, "volume-down should differ from original");
        assert_ne!(noise, pcm, "noise should differ from original");

        // Verify they also differ from each other (different transforms)
        assert_ne!(
            speed_down, speed_up,
            "speed-down and speed-up should differ"
        );
        assert_ne!(
            speed_down, volume_down,
            "speed-down and volume-down should differ"
        );
        assert_ne!(speed_down, noise, "speed-down and noise should differ");
    }
}

// ── Shared Gaussian sampler seeded-sequence equivalence ────────────────
// The shared helper consumes exactly the same RNG draws as the inline formula
// it was extracted from, so seeded outputs stay byte-identical. These tests
// prove it over ~8000 seeded draw pairs.

#[cfg(test)]
mod gaussian_sampler_tests {
    use super::{gaussian_pair_from_uniforms, sample_gaussian_pair_clamped};
    use rand::RngExt;
    use rand::SeedableRng;

    /// Reference: the bench's pre-extraction EPSILON-clamp pair sampler
    /// (2 draws per 2 samples, cos+sin).
    fn reference_bench_pair(rng: &mut impl rand::Rng) -> (f32, f32) {
        let u1: f32 = rng.random::<f32>().max(f32::EPSILON);
        let u2: f32 = rng.random::<f32>().max(f32::EPSILON);
        let z1 = (-2.0 * u1.ln()).sqrt() * (2.0 * core::f32::consts::PI * u2).cos();
        let z2 = (-2.0 * u1.ln()).sqrt() * (2.0 * core::f32::consts::PI * u2).sin();
        (z1, z2)
    }

    #[test]
    fn seeded_bench_sequence_byte_identical() {
        // Bench seed 43; 8000 samples = 4000 draw pairs (2 draws per 2 samples).
        const DRAW_PAIRS: usize = 4000;
        let mut shared_rng = rand::rngs::StdRng::seed_from_u64(43);
        let mut ref_rng = rand::rngs::StdRng::seed_from_u64(43);
        for i in 0..DRAW_PAIRS {
            let shared = sample_gaussian_pair_clamped(&mut shared_rng);
            let reference = reference_bench_pair(&mut ref_rng);
            #[expect(clippy::float_cmp)] // same-seed RNG must be byte-identical
            {
                assert_eq!(shared.0, reference.0, "bench z1 divergence at pair {i}");
            }
            #[expect(clippy::float_cmp)] // same-seed RNG must be byte-identical
            {
                assert_eq!(shared.1, reference.1, "bench z2 divergence at pair {i}");
            }
        }
    }

    #[test]
    fn pair_math_matches_inline_formulas() {
        // gaussian_pair_from_uniforms must equal the inline cos/sin formulas.
        for &(u1, u2) in &[(0.1, 0.2), (0.5, 0.9), (0.001, 0.999), (0.25, 0.75)] {
            let (z1, z2) = gaussian_pair_from_uniforms(u1, u2);
            let r = (-2.0 * u1.ln()).sqrt();
            let theta = 2.0 * std::f32::consts::PI * u2;
            #[expect(clippy::float_cmp)]
            // identical expression ordering — bit-identical by construction
            {
                assert_eq!(z1, r * theta.cos(), "z1 for ({u1}, {u2})");
            }
            #[expect(clippy::float_cmp)]
            // identical expression ordering — bit-identical by construction
            {
                assert_eq!(z2, r * theta.sin(), "z2 for ({u1}, {u2})");
            }
        }
    }
}