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 c.backend == "model"
    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)
    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