import numpy as np
import struct
import pytest
from unittest.mock import patch, MagicMock


def test_pack_unpack_roundtrip():
    from app.pipeline.recognition import pack_embedding, unpack_embedding
    original = np.array([0.1, 0.2, 0.3, -0.5], dtype=np.float32)
    packed = pack_embedding(original)
    recovered = unpack_embedding(packed)
    np.testing.assert_allclose(recovered, original, rtol=1e-6)


def test_cosine_similarity_identical():
    from app.pipeline.recognition import cosine_similarity
    v = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    assert cosine_similarity(v, v) == pytest.approx(1.0)


def test_cosine_similarity_orthogonal():
    from app.pipeline.recognition import cosine_similarity
    a = np.array([1.0, 0.0], dtype=np.float32)
    b = np.array([0.0, 1.0], dtype=np.float32)
    assert cosine_similarity(a, b) == pytest.approx(0.0)


def test_cosine_similarity_zero_vector():
    from app.pipeline.recognition import cosine_similarity
    a = np.array([0.0, 0.0], dtype=np.float32)
    b = np.array([1.0, 0.0], dtype=np.float32)
    assert cosine_similarity(a, b) == 0.0


def test_find_best_match_known():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    ref = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    query = np.array([0.98, 0.2, 0.0], dtype=np.float32)
    query /= np.linalg.norm(query)
    ref /= np.linalg.norm(ref)
    status, sid, conf = find_best_speaker_match(
        query, [(1, pack_embedding(ref))], known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "known"
    assert sid == 1
    assert conf >= 0.85


def test_find_best_match_ambiguous():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    ref = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    # cos similarity ~0.707 (45 degrees)
    query = np.array([1.0, 1.0, 0.0], dtype=np.float32)
    query /= np.linalg.norm(query)
    ref /= np.linalg.norm(ref)
    status, sid, conf = find_best_speaker_match(
        query, [(1, pack_embedding(ref))], known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "ambiguous"
    assert sid == 1


def test_find_best_match_unknown():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    ref = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    query = np.array([0.0, 1.0, 0.0], dtype=np.float32)  # orthogonal = 0 similarity
    status, sid, conf = find_best_speaker_match(
        query, [(1, pack_embedding(ref))], known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "unknown"
    assert sid is None


def test_find_best_match_empty_embeddings():
    from app.pipeline.recognition import find_best_speaker_match
    query = np.array([1.0, 0.0], dtype=np.float32)
    status, sid, conf = find_best_speaker_match(query, [])
    assert status == "unknown"
    assert sid is None


def test_find_best_match_takes_max_per_speaker():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    # Speaker 1 has two embeddings — one poor, one good
    ref_bad = np.array([0.0, 1.0, 0.0], dtype=np.float32)
    ref_good = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    query = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    stored = [(1, pack_embedding(ref_bad)), (1, pack_embedding(ref_good))]
    status, sid, conf = find_best_speaker_match(
        query, stored, known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "known"
    assert sid == 1


def test_embed_audio_calls_resemblyzer(mocker):
    from app.pipeline.recognition import embed_audio
    mock_wav = np.zeros(16000, dtype=np.float32)
    mock_emb = np.ones(256, dtype=np.float32)
    mocker.patch("app.pipeline.recognition.preprocess_wav", return_value=mock_wav)
    mock_encoder = MagicMock()
    mock_encoder.embed_utterance.return_value = mock_emb
    mocker.patch("app.pipeline.recognition.get_encoder", return_value=mock_encoder)
    result = embed_audio(b"\x00" * 100)
    assert result.shape == (256,)
    mock_encoder.embed_utterance.assert_called_once_with(mock_wav)