import inspect

import numpy as np
import pytest

from audio_cleanup import AudioCleaner


def test_enhance_batch_without_lengths_typeerror():
    c = AudioCleaner(mode="force")
    assert "model" in c.backend
    import torch

    w = torch.zeros(1, 8000)
    with pytest.raises(TypeError):
        c._enhancer.enhance_batch(w)


def test_process_uses_relative_lengths_one():
    src = inspect.getsource(AudioCleaner._process_inner)
    assert "tensor([1.0])" in src


def test_process_preserves_length_and_int16():
    c = AudioCleaner(mode="force")
    n = 16000
    pcm = (np.sin(2 * np.pi * 440 * np.arange(n) / 16000) * 8000).astype(np.int16)
    out = c.process(pcm)
    assert out.dtype == np.int16
    assert out.shape == pcm.shape


def test_overlap_process_emits_hop_length():
    c = AudioCleaner(mode="force", chunk_s=1.0, hop_s=0.2)
    hop = 3200
    pcm = (np.random.randn(hop) * 500).astype(np.int16)
    out = c.process(pcm)
    assert out.shape == (hop,)


def test_overlap_runs_model_after_enough_context():
    c = AudioCleaner(mode="force", chunk_s=0.5, hop_s=0.25)
    hop = 4000
    for _ in range(4):
        pcm = (np.sin(2 * np.pi * 440 * np.arange(hop) / 16000) * 8000).astype(
            np.int16
        )
        out = c.process(pcm)
        assert out.shape == (hop,)
        assert out.dtype == np.int16


def test_beamform_loads_delaysum_module():
    c = AudioCleaner(mode="off", beamform="force")
    assert c._beamformer is not None
    assert type(c._beamformer).__name__ == "DelaySum_Beamformer"
    assert "beamform" in c.backend


def test_beamform_skips_normalizer_at_native_rate():
    c = AudioCleaner(mode="off", beamform="force", sample_rate=16000)
    assert c._normalizer is None


def test_tdoa_reused_across_hops():
    c = AudioCleaner(mode="off", beamform="force", tdoa_every=5)
    assert c._gccphat is not None
    calls = {"n": 0}
    orig = c._gccphat

    def wrapped(*a, **k):
        calls["n"] += 1
        return orig(*a, **k)

    c._gccphat = wrapped
    hop = 3200
    t = np.arange(hop)
    ch0 = (np.sin(2 * np.pi * 440 * t / 16000) * 8000).astype(np.int16)
    stereo = np.stack([ch0, np.roll(ch0, 2)], axis=1)
    for _ in range(3):
        out = c.process(stereo)
        assert out.shape == (hop,)
    assert calls["n"] == 1


def test_process_records_last_dt_ms():
    c = AudioCleaner(mode="off", beamform="off")
    hop = 3200
    pcm = (np.random.randn(hop) * 500).astype(np.int16)
    c.process(pcm)
    assert c.last_dt_ms >= 0.0


def test_stereo_beamform_then_model_preserves_hop():
    c = AudioCleaner(
        mode="force", beamform="force", chunk_s=0.5, hop_s=0.25)
    assert "model" in c.backend
    assert "beamform" in c.backend
    hop = 4000
    t = np.arange(hop)
    ch0 = (np.sin(2 * np.pi * 440 * t / 16000) * 8000).astype(np.int16)
    ch1 = np.roll(ch0, 3)
    stereo = np.stack([ch0, ch1], axis=1)
    for _ in range(4):
        out = c.process(stereo)
        assert out.dtype == np.int16
        assert out.shape == (hop,)


def test_openvino_cpu_infer_loads_and_preserves_hop():
    c = AudioCleaner(
        mode="force", beamform="off", enhance_infer="openvino",
        chunk_s=0.5, hop_s=0.25)
    assert c.infer_backend == "openvino"
    assert c._ov_compiled is not None
    assert "ov" in c.backend
    hop = 4000
    pcm = (np.sin(2 * np.pi * 440 * np.arange(hop) / 16000) * 8000).astype(
        np.int16)
    for _ in range(4):
        out = c.process(pcm)
        assert out.dtype == np.int16
        assert out.shape == (hop,)