/**
 * Comprehensive tests for @claude-flow/embeddings
 */

import { describe, it, expect, beforeEach } from 'vitest';

// Import directly from modules to avoid heavy dependencies
import {
  MockEmbeddingService,
  cosineSimilarity,
  euclideanDistance,
  dotProduct,
  computeSimilarity,
} from '../src/embedding-service.js';

import {
  chunkText,
  estimateTokens,
  reconstructFromChunks,
} from '../src/chunking.js';

import {
  l2Normalize,
  l1Normalize,
  minMaxNormalize,
  zScoreNormalize,
  normalize,
  l2Norm,
  isNormalized,
} from '../src/normalization.js';

import {
  euclideanToPoincare,
  poincareToEuclidean,
  hyperbolicDistance,
  mobiusAdd,
  isInPoincareBall,
  batchEuclideanToPoincare,
} from '../src/hyperbolic.js';

// =============================================================================
// Mock Embedding Service Tests
// =============================================================================

describe('MockEmbeddingService', () => {
  let service: MockEmbeddingService;

  beforeEach(() => {
    service = new MockEmbeddingService({ dimensions: 384 });
  });

  it('generates embeddings with correct dimensions', async () => {
    const result = await service.embed('test text');
    expect(result.embedding).toBeInstanceOf(Float32Array);
    expect(result.embedding.length).toBe(384);
  });

  it('generates deterministic embeddings for same text', async () => {
    const result1 = await service.embed('hello world');
    const result2 = await service.embed('hello world');
    expect(Array.from(result1.embedding)).toEqual(Array.from(result2.embedding));
  });

  it('generates different embeddings for different text', async () => {
    const result1 = await service.embed('hello');
    const result2 = await service.embed('world');
    expect(Array.from(result1.embedding)).not.toEqual(Array.from(result2.embedding));
  });

  it('caches results and returns faster', async () => {
    await service.embed('cached text');
    const result = await service.embed('cached text');
    expect(result.cached).toBe(true);
    expect(result.latencyMs).toBe(0);
  });

  it('batch embedding works correctly', async () => {
    const texts = ['one', 'two', 'three'];
    const result = await service.embedBatch(texts);
    expect(result.embeddings.length).toBe(3);
    expect(result.embeddings[0].length).toBe(384);
  });
});

// =============================================================================
// Similarity Functions Tests
// =============================================================================

describe('Similarity Functions', () => {
  const vecA = new Float32Array([1, 0, 0]);
  const vecB = new Float32Array([0, 1, 0]);
  const vecC = new Float32Array([1, 0, 0]);

  it('cosine similarity: identical vectors = 1', () => {
    expect(cosineSimilarity(vecA, vecC)).toBeCloseTo(1, 5);
  });

  it('cosine similarity: orthogonal vectors = 0', () => {
    expect(cosineSimilarity(vecA, vecB)).toBeCloseTo(0, 5);
  });

  it('euclidean distance: same vectors = 0', () => {
    expect(euclideanDistance(vecA, vecC)).toBeCloseTo(0, 5);
  });

  it('euclidean distance: orthogonal unit vectors', () => {
    expect(euclideanDistance(vecA, vecB)).toBeCloseTo(Math.sqrt(2), 5);
  });

  it('dot product: orthogonal = 0', () => {
    expect(dotProduct(vecA, vecB)).toBeCloseTo(0, 5);
  });

  it('dot product: same unit vector = 1', () => {
    expect(dotProduct(vecA, vecC)).toBeCloseTo(1, 5);
  });

  it('computeSimilarity with cosine metric', () => {
    const result = computeSimilarity(vecA, vecC, 'cosine');
    expect(result.score).toBeCloseTo(1, 5);
    expect(result.metric).toBe('cosine');
  });
});

// =============================================================================
// Chunking Tests
// =============================================================================

describe('Document Chunking', () => {
  const longText = `
    This is the first sentence. This is the second sentence.
    This is the third sentence with more content.
    And here is the fourth sentence to make it longer.
    Finally, the fifth sentence concludes this paragraph.
  `.trim();

  it('chunks text into multiple parts', () => {
    const result = chunkText(longText, { maxChunkSize: 100 });
    expect(result.totalChunks).toBeGreaterThan(1);
  });

  it('respects max chunk size', () => {
    const result = chunkText(longText, { maxChunkSize: 50, strategy: 'character' });
    for (const chunk of result.chunks) {
      expect(chunk.length).toBeLessThanOrEqual(50);
    }
  });

  it('includes overlap between chunks', () => {
    const result = chunkText(longText, { maxChunkSize: 100, overlap: 20, strategy: 'character' });
    expect(result.chunks.length).toBeGreaterThan(1);
    // Chunks should have some overlapping content
  });

  it('estimates tokens correctly', () => {
    const text = 'Hello world';
    const tokens = estimateTokens(text);
    expect(tokens).toBe(Math.ceil(text.length / 4));
  });

  it('returns original text for short input', () => {
    const result = chunkText('Short text', { maxChunkSize: 100 });
    expect(result.totalChunks).toBe(1);
    expect(result.chunks[0].text).toBe('Short text');
  });

  it('sentence strategy keeps sentences intact', () => {
    const result = chunkText(longText, { maxChunkSize: 200, strategy: 'sentence' });
    // Verify chunks contain complete sentences
    for (const chunk of result.chunks) {
      // Sentences should end with proper punctuation
      const trimmed = chunk.text.trim();
      if (trimmed.length > 0) {
        expect(trimmed.endsWith('.') || trimmed.endsWith('!') || trimmed.endsWith('?') || chunk === result.chunks[result.chunks.length - 1]).toBe(true);
      }
    }
  });
});

// =============================================================================
// Normalization Tests
// =============================================================================

describe('Normalization', () => {
  const vec = new Float32Array([3, 4, 0]);
  const vec2 = new Float32Array([1, 2, 3, 4, 5]);

  it('L2 normalize creates unit vector', () => {
    const normalized = l2Normalize(vec);
    const norm = l2Norm(normalized);
    expect(norm).toBeCloseTo(1, 5);
  });

  it('L2 normalize: [3,4,0] -> [0.6, 0.8, 0]', () => {
    const normalized = l2Normalize(vec);
    expect(normalized[0]).toBeCloseTo(0.6, 5);
    expect(normalized[1]).toBeCloseTo(0.8, 5);
    expect(normalized[2]).toBeCloseTo(0, 5);
  });

  it('L1 normalize: sum of absolute values = 1', () => {
    const normalized = l1Normalize(vec2);
    const sum = Array.from(normalized).reduce((a, b) => a + Math.abs(b), 0);
    expect(sum).toBeCloseTo(1, 5);
  });

  it('minMax normalize: values in [0, 1]', () => {
    const normalized = minMaxNormalize(vec2);
    for (const v of normalized) {
      expect(v).toBeGreaterThanOrEqual(0);
      expect(v).toBeLessThanOrEqual(1);
    }
  });

  it('zScore normalize: mean ≈ 0, std ≈ 1', () => {
    const normalized = zScoreNormalize(vec2);
    const mean = Array.from(normalized).reduce((a, b) => a + b, 0) / normalized.length;
    expect(mean).toBeCloseTo(0, 5);
  });

  it('isNormalized detects normalized vectors', () => {
    const normalized = l2Normalize(vec);
    expect(isNormalized(normalized)).toBe(true);
    expect(isNormalized(vec)).toBe(false);
  });

  it('normalize with type option', () => {
    const l2 = normalize(vec, { type: 'l2' });
    expect(l2Norm(l2)).toBeCloseTo(1, 5);

    const l1 = normalize(vec, { type: 'l1' });
    const l1Sum = Array.from(l1).reduce((a, b) => a + Math.abs(b), 0);
    expect(l1Sum).toBeCloseTo(1, 5);
  });
});

// =============================================================================
// Hyperbolic Embeddings Tests
// =============================================================================

describe('Hyperbolic Embeddings', () => {
  const eucVec = new Float32Array([0.5, 0.3, 0.2]);
  const origin = new Float32Array([0, 0, 0]);

  it('euclideanToPoincare: origin maps to origin', () => {
    const poincare = euclideanToPoincare(origin);
    expect(Array.from(poincare).every(v => Math.abs(v) < 1e-10)).toBe(true);
  });

  it('euclideanToPoincare: result stays in ball', () => {
    const poincare = euclideanToPoincare(eucVec);
    expect(isInPoincareBall(poincare)).toBe(true);
  });

  it('poincareToEuclidean: round trip', () => {
    const poincare = euclideanToPoincare(eucVec);
    const back = poincareToEuclidean(poincare);
    for (let i = 0; i < eucVec.length; i++) {
      expect(back[i]).toBeCloseTo(eucVec[i], 4);
    }
  });

  it('hyperbolicDistance: self = 0', () => {
    const p = euclideanToPoincare(new Float32Array([0.1, 0.1, 0.1]));
    expect(hyperbolicDistance(p, p)).toBeCloseTo(0, 5);
  });

  it('hyperbolicDistance: symmetry', () => {
    const a = euclideanToPoincare(new Float32Array([0.1, 0.2, 0.1]));
    const b = euclideanToPoincare(new Float32Array([0.3, 0.1, 0.2]));
    expect(hyperbolicDistance(a, b)).toBeCloseTo(hyperbolicDistance(b, a), 5);
  });

  it('mobiusAdd: identity element', () => {
    const p = euclideanToPoincare(new Float32Array([0.2, 0.1, 0.1]));
    const result = mobiusAdd(p, origin);
    for (let i = 0; i < p.length; i++) {
      expect(result[i]).toBeCloseTo(p[i], 5);
    }
  });

  it('isInPoincareBall: points inside', () => {
    expect(isInPoincareBall(new Float32Array([0.5, 0.3, 0.2]))).toBe(true);
    expect(isInPoincareBall(new Float32Array([0.9, 0.9, 0.9]))).toBe(false);
  });

  it('batchEuclideanToPoincare: converts multiple', () => {
    const vecs = [
      new Float32Array([0.1, 0.1]),
      new Float32Array([0.2, 0.2]),
      new Float32Array([0.3, 0.3]),
    ];
    const results = batchEuclideanToPoincare(vecs);
    expect(results.length).toBe(3);
    for (const r of results) {
      expect(isInPoincareBall(r)).toBe(true);
    }
  });
});