import os
from pathlib import Path

from torchaudio.datasets.libritts import LIBRITTS
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase

_UTTERANCE_IDS = [
    [19, 198, "000000", "000000"],
    [26, 495, "000004", "000000"],
]
_ORIGINAL_TEXT = "this is the original text."
_NORMALIZED_TEXT = "this is the normalized text."


def get_mock_dataset(root_dir):
    """
    root_dir: directory to the mocked dataset
    """
    mocked_data = []
    base_dir = os.path.join(root_dir, "LibriTTS", "train-clean-100")
    for i, utterance_id in enumerate(_UTTERANCE_IDS):
        filename = f'{"_".join(str(u) for u in utterance_id)}.wav'
        file_dir = os.path.join(base_dir, str(utterance_id[0]), str(utterance_id[1]))
        os.makedirs(file_dir, exist_ok=True)
        path = os.path.join(file_dir, filename)

        data = get_whitenoise(sample_rate=24000, duration=2, n_channels=1, dtype="int16", seed=i)
        save_wav(path, data, 24000)
        mocked_data.append(normalize_wav(data))

        original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt'
        path_original = os.path.join(file_dir, original_text_filename)
        with open(path_original, "w") as file_:
            file_.write(_ORIGINAL_TEXT)

        normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt'
        path_normalized = os.path.join(file_dir, normalized_text_filename)
        with open(path_normalized, "w") as file_:
            file_.write(_NORMALIZED_TEXT)
    return mocked_data, _UTTERANCE_IDS, _ORIGINAL_TEXT, _NORMALIZED_TEXT


class TestLibriTTS(TempDirMixin, TorchaudioTestCase):

    root_dir = None
    data = []
    _utterance_ids, _original_text, _normalized_text = [], [], []

    @classmethod
    def setUpClass(cls):
        cls.root_dir = cls.get_base_temp_dir()
        cls.data, cls._utterance_ids, cls._original_text, cls._normalized_text = get_mock_dataset(cls.root_dir)

    def _test_libritts(self, dataset):
        n_ites = 0
        for i, (
            waveform,
            sample_rate,
            original_text,
            normalized_text,
            speaker_id,
            chapter_id,
            utterance_id,
        ) in enumerate(dataset):
            expected_ids = self._utterance_ids[i]
            expected_data = self.data[i]
            self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
            assert sample_rate == 24000
            assert speaker_id == expected_ids[0]
            assert chapter_id == expected_ids[1]
            assert original_text == self._original_text
            assert normalized_text == self._normalized_text
            assert utterance_id == f'{"_".join(str(u) for u in expected_ids[-4:])}'
            n_ites += 1
        assert n_ites == len(self._utterance_ids)

    def test_libritts_str(self):
        dataset = LIBRITTS(self.root_dir)
        self._test_libritts(dataset)

    def test_libritts_path(self):
        dataset = LIBRITTS(Path(self.root_dir))
        self._test_libritts(dataset)

import os
from pathlib import Path

from torchaudio.datasets.libritts import LIBRITTS
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase

_UTTERANCE_IDS = [
    [19, 198, "000000", "000000"],
    [26, 495, "000004", "000000"],
]
_ORIGINAL_TEXT = "this is the original text."
_NORMALIZED_TEXT = "this is the normalized text."


def get_mock_dataset(root_dir):
    """
    root_dir: directory to the mocked dataset
    """
    mocked_data = []
    base_dir = os.path.join(root_dir, "LibriTTS", "train-clean-100")
    for i, utterance_id in enumerate(_UTTERANCE_IDS):
        filename = f'{"_".join(str(u) for u in utterance_id)}.wav'
        file_dir = os.path.join(base_dir, str(utterance_id[0]), str(utterance_id[1]))
        os.makedirs(file_dir, exist_ok=True)
        path = os.path.join(file_dir, filename)

        data = get_whitenoise(sample_rate=24000, duration=2, n_channels=1, dtype="int16", seed=i)
        save_wav(path, data, 24000)
        mocked_data.append(normalize_wav(data))

        original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt'
        path_original = os.path.join(file_dir, original_text_filename)
        with open(path_original, "w") as file_:
            file_.write(_ORIGINAL_TEXT)

        normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt'
        path_normalized = os.path.join(file_dir, normalized_text_filename)
        with open(path_normalized, "w") as file_:
            file_.write(_NORMALIZED_TEXT)
    return mocked_data, _UTTERANCE_IDS, _ORIGINAL_TEXT, _NORMALIZED_TEXT


class TestLibriTTS(TempDirMixin, TorchaudioTestCase):
    backend = "default"

    root_dir = None
    data = []
    _utterance_ids, _original_text, _normalized_text = [], [], []

    @classmethod
    def setUpClass(cls):
        cls.root_dir = cls.get_base_temp_dir()
        cls.data, cls._utterance_ids, cls._original_text, cls._normalized_text = get_mock_dataset(cls.root_dir)

    def _test_libritts(self, dataset):
        n_ites = 0
        for i, (
            waveform,
            sample_rate,
            original_text,
            normalized_text,
            speaker_id,
            chapter_id,
            utterance_id,
        ) in enumerate(dataset):
            expected_ids = self._utterance_ids[i]
            expected_data = self.data[i]
            self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
            assert sample_rate == 24000
            assert speaker_id == expected_ids[0]
            assert chapter_id == expected_ids[1]
            assert original_text == self._original_text
            assert normalized_text == self._normalized_text
            assert utterance_id == f'{"_".join(str(u) for u in expected_ids[-4:])}'
            n_ites += 1
        assert n_ites == len(self._utterance_ids)

    def test_libritts_str(self):
        dataset = LIBRITTS(self.root_dir)
        self._test_libritts(dataset)

    def test_libritts_path(self):
        dataset = LIBRITTS(Path(self.root_dir))
        self._test_libritts(dataset)
