from logging import root
import pytest
import torch
import numpy as np

from enhancer.utils.io import Audio
from enhancer.utils.config import Files
from enhancer.utils.fileprocessor import Fileprocessor

def test_io_channel():

    input_audio = np.random.rand(2,32000)
    audio = Audio(mono=True,return_tensor=False)
    output_audio = audio(input_audio)
    assert output_audio.shape[0] == 1

def test_io_resampling():

    input_audio = np.random.rand(1,32000)
    resampled_audio = Audio.resample_audio(input_audio,16000,8000)

    input_audio = torch.rand(1,32000)
    resampled_audio_pt = Audio.pt_resample_audio(input_audio,16000,8000)

    assert resampled_audio.shape[1] == resampled_audio_pt.size(1) == 16000

def test_fileprocessor_vctk():

    fp = Fileprocessor.from_name("vctk","tests/data/vctk/clean_testset_wav",
                                "tests/data/vctk/noisy_testset_wav",48000)
    matching_dict = fp.prepare_matching_dict()
    assert len(matching_dict)==2

@pytest.mark.parametrize("dataset_name",["vctk","dns-2020"])
def test_fileprocessor_names(dataset_name):
     fp = Fileprocessor.from_name(dataset_name,"clean_dir","noisy_dir",16000)
     assert hasattr(fp.matching_function, '__call__')

def test_fileprocessor_invaliname():
    with pytest.raises(ValueError):
        fp = Fileprocessor.from_name("undefined","clean_dir","noisy_dir",16000).prepare_matching_dict()