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// Currently the builds are expected to run only on Node.js,

// but Deno tests pass as well.

//

// Bun supports `node:assert`, but not `node:test`.

// Using `require` we can make the tests compatible with Bun.

//

// const isBun = typeof Bun !== "undefined";

// let assert, test;

// if (isBun) {

// assert = require('node:assert');

// test = require('bun:test');

// } else {

// assert = require('node:assert');

// test = require('node:test');

// }

//

// That, however, leads to other issues, like the following:

//

// require is not defined in ES module scope, you can use import instead

//

// https://bun.sh/docs/runtime/nodejs-apis

// https://bun.sh/guides/util/detect-bun

import test from "node:test";

import assert from "node:assert";

import * as simsimd from "./dist/esm/simsimd.js";

import * as fallback from "./dist/esm/fallback.js";

function assertAlmostEqual(actual, expected, tolerance = 1e-6) {

const lowerBound = expected - tolerance;

const upperBound = expected + tolerance;

assert(

actual >= lowerBound && actual <= upperBound,

`Expected ${actual} to be almost equal to ${expected}`

);

}

test("Distance from itself", () => {

const f32s = new Float32Array([1.0, 2.0, 3.0]);

assertAlmostEqual(simsimd.sqeuclidean(f32s, f32s), 0.0, 0.01);

assertAlmostEqual(simsimd.cosine(f32s, f32s), 0.0, 0.01);

const f64s = new Float64Array([1.0, 2.0, 3.0]);

assertAlmostEqual(simsimd.sqeuclidean(f64s, f64s), 0.0, 0.01);

assertAlmostEqual(simsimd.cosine(f64s, f64s), 0.0, 0.01);

const f32sNormalized = new Float32Array([

1.0 / Math.sqrt(14),

2.0 / Math.sqrt(14),

3.0 / Math.sqrt(14),

]);

assertAlmostEqual(simsimd.inner(f32sNormalized, f32sNormalized), 1.0, 0.01);

const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]);

assertAlmostEqual(

simsimd.kullbackleibler(f32sDistribution, f32sDistribution),

0.0,

0.01

);

assertAlmostEqual(

simsimd.jensenshannon(f32sDistribution, f32sDistribution),

0.0,

0.01

);

const u8s = new Uint8Array([1, 2, 3]);

assertAlmostEqual(simsimd.hamming(u8s, u8s), 0.0, 0.01);

assertAlmostEqual(simsimd.jaccard(u8s, u8s), 0.0, 0.01);

});

test("Distance from itself JS fallback", () => {

const f32s = new Float32Array([1.0, 2.0, 3.0]);

assertAlmostEqual(fallback.sqeuclidean(f32s, f32s), 0.0, 0.01);

assertAlmostEqual(fallback.cosine(f32s, f32s), 0.0, 0.01);

const arrNormalized = new Float32Array([

1.0 / Math.sqrt(14),

2.0 / Math.sqrt(14),

3.0 / Math.sqrt(14),

]);

assertAlmostEqual(fallback.inner(arrNormalized, arrNormalized), 1.0, 0.01);

const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]);

assertAlmostEqual(

fallback.kullbackleibler(f32sDistribution, f32sDistribution),

0.0,

0.01

);

assertAlmostEqual(

fallback.jensenshannon(f32sDistribution, f32sDistribution),

0.0,

0.01

);

const u8s = new Uint8Array([1, 2, 3]);

assertAlmostEqual(fallback.hamming(u8s, u8s), 0.0, 0.01);

assertAlmostEqual(fallback.jaccard(u8s, u8s), 0.0, 0.01);

});

const f32Array1 = new Float32Array([1.0, 2.0, 3.0]);

const f32Array2 = new Float32Array([4.0, 5.0, 6.0]);

test("Squared Euclidean Distance", () => {

const result = simsimd.sqeuclidean(f32Array1, f32Array2);

assertAlmostEqual(result, 27.0, 0.01);

});

test("Inner Distance", () => {

const result = simsimd.inner(f32Array1, f32Array2);

assertAlmostEqual(result, 32.0, 0.01);

});

test("Cosine Similarity", () => {

const result = simsimd.cosine(f32Array1, f32Array2);

assertAlmostEqual(result, 0.029, 0.01);

});

test("Squared Euclidean Distance JS", () => {

const result = fallback.sqeuclidean(f32Array1, f32Array2);

assertAlmostEqual(result, 27.0, 0.01);

});

test("Inner Distance JS", () => {

const result = fallback.inner(f32Array1, f32Array2);

assertAlmostEqual(result, 32.0, 0.01);

});

test("Cosine Similarity JS", () => {

const result = fallback.cosine(f32Array1, f32Array2);

assertAlmostEqual(result, 0.029, 0.01);

});

test("Squared Euclidean Distance C vs JS", () => {

const result = simsimd.sqeuclidean(f32Array1, f32Array2);

const resultjs = fallback.sqeuclidean(f32Array1, f32Array2);

assertAlmostEqual(resultjs, result, 0.01);

});

test("Inner Distance C vs JS", () => {

const result = simsimd.inner(f32Array1, f32Array2);

const resultjs = fallback.inner(f32Array1, f32Array2);

assertAlmostEqual(resultjs, result, 0.01);

});

test("Cosine Similarity C vs JS", () => {

const result = simsimd.cosine(f32Array1, f32Array2);

const resultjs = fallback.cosine(f32Array1, f32Array2);

assertAlmostEqual(resultjs, result, 0.01);

});

test("Hamming C vs JS", () => {

const u8s = new Uint8Array([1, 2, 3]);

const result = simsimd.hamming(u8s, u8s);

const resultjs = fallback.hamming(u8s, u8s);

assertAlmostEqual(resultjs, result, 0.01);

});

test("Jaccard C vs JS", () => {

const u8s = new Uint8Array([1, 2, 3]);

const result = simsimd.jaccard(u8s, u8s);

const resultjs = fallback.jaccard(u8s, u8s);

assertAlmostEqual(resultjs, result, 0.01);

});

test("Kullback-Leibler C vs JS", () => {

const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]);

const result = simsimd.kullbackleibler(f32sDistribution, f32sDistribution);

const resultjs = fallback.kullbackleibler(f32sDistribution, f32sDistribution);

assertAlmostEqual(resultjs, result, 0.01);

});

test("Jensen-Shannon C vs JS", () => {

const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]);

const result = simsimd.jensenshannon(f32sDistribution, f32sDistribution);

const resultjs = fallback.jensenshannon(f32sDistribution, f32sDistribution);

assertAlmostEqual(resultjs, result, 0.01);

});