# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.


import mmap
from pathlib import Path
import io
from typing import BinaryIO, List, Optional, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F

SF_AUDIO_FILE_EXTENSIONS = {".wav", ".flac", ".ogg"}
FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS = {".npy", ".wav", ".flac", ".ogg"}


def convert_waveform(
    waveform: Union[np.ndarray, torch.Tensor],
    sample_rate: int,
    normalize_volume: bool = False,
    to_mono: bool = False,
    to_sample_rate: Optional[int] = None,
) -> Tuple[Union[np.ndarray, torch.Tensor], int]:
    """convert a waveform:
    - to a target sample rate
    - from multi-channel to mono channel
    - volume normalization

    Args:
        waveform (numpy.ndarray or torch.Tensor): 2D original waveform
            (channels x length)
        sample_rate (int): original sample rate
        normalize_volume (bool): perform volume normalization
        to_mono (bool): convert to mono channel if having multiple channels
        to_sample_rate (Optional[int]): target sample rate
    Returns:
        waveform (numpy.ndarray): converted 2D waveform (channels x length)
        sample_rate (float): target sample rate
    """
    try:
        import torchaudio.sox_effects as ta_sox
    except ImportError:
        raise ImportError("Please install torchaudio: pip install torchaudio")

    effects = []
    if normalize_volume:
        effects.append(["gain", "-n"])
    if to_sample_rate is not None and to_sample_rate != sample_rate:
        effects.append(["rate", f"{to_sample_rate}"])
    if to_mono and waveform.shape[0] > 1:
        effects.append(["channels", "1"])
    if len(effects) > 0:
        is_np_input = isinstance(waveform, np.ndarray)
        _waveform = torch.from_numpy(waveform) if is_np_input else waveform
        converted, converted_sample_rate = ta_sox.apply_effects_tensor(
            _waveform, sample_rate, effects
        )
        if is_np_input:
            converted = converted.numpy()
        return converted, converted_sample_rate
    return waveform, sample_rate


def get_waveform(
    path_or_fp: Union[str, BinaryIO],
    normalization: bool = True,
    mono: bool = True,
    frames: int = -1,
    start: int = 0,
    always_2d: bool = True,
    output_sample_rate: Optional[int] = None,
    normalize_volume: bool = False,
) -> Tuple[np.ndarray, int]:
    """Get the waveform and sample rate of a 16-bit WAV/FLAC/OGG Vorbis audio.

    Args:
        path_or_fp (str or BinaryIO): the path or file-like object
        normalization (bool): normalize values to [-1, 1] (Default: True)
        mono (bool): convert multi-channel audio to mono-channel one
        frames (int): the number of frames to read. (-1 for reading all)
        start (int): Where to start reading. A negative value counts from the end.
        always_2d (bool): always return 2D array even for mono-channel audios
        output_sample_rate (Optional[int]): output sample rate
        normalize_volume (bool): normalize volume
    Returns:
        waveform (numpy.ndarray): 1D or 2D waveform (channels x length)
        sample_rate (float): sample rate
    """
    if isinstance(path_or_fp, str):
        ext = Path(path_or_fp).suffix
        if ext not in SF_AUDIO_FILE_EXTENSIONS:
            raise ValueError(f"Unsupported audio format: {ext}")

    try:
        import soundfile as sf
    except ImportError:
        raise ImportError("Please install soundfile: pip install soundfile")

    waveform, sample_rate = sf.read(
        path_or_fp, dtype="float32", always_2d=True, frames=frames, start=start
    )
    waveform = waveform.T  # T x C -> C x T
    waveform, sample_rate = convert_waveform(
        waveform,
        sample_rate,
        normalize_volume=normalize_volume,
        to_mono=mono,
        to_sample_rate=output_sample_rate,
    )

    if not normalization:
        waveform *= 2**15  # denormalized to 16-bit signed integers
    if not always_2d:
        waveform = waveform.squeeze(axis=0)
    return waveform, sample_rate


def get_features_from_npy_or_audio(path):
    ext = Path(path).suffix
    if ext not in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS:
        raise ValueError(f'Unsupported file format for "{path}"')
    return np.load(path) if ext == ".npy" else get_fbank(path)


def get_features_or_waveform_from_stored_zip(
    path,
    byte_offset,
    byte_size,
    need_waveform=False,
    use_sample_rate=None,
):
    assert path.endswith(".zip")
    data = read_from_stored_zip(path, byte_offset, byte_size)
    f = io.BytesIO(data)
    if is_npy_data(data):
        features_or_waveform = np.load(f)
    elif is_sf_audio_data(data):
        features_or_waveform = (
            get_waveform(f, always_2d=False, output_sample_rate=use_sample_rate)[0]
            if need_waveform
            else get_fbank(f)
        )
    else:
        raise ValueError(f'Unknown file format for "{path}"')
    return features_or_waveform


def get_features_or_waveform(path: str, need_waveform=False, use_sample_rate=None):
    """Get speech features from .npy file or waveform from .wav/.flac file.
    The file may be inside an uncompressed ZIP file and is accessed via byte
    offset and length.

    Args:
        path (str): File path in the format of "<.npy/.wav/.flac path>" or
        "<zip path>:<byte offset>:<byte length>".
        need_waveform (bool): return waveform instead of features.
        use_sample_rate (int): change sample rate for the input wave file

    Returns:
        features_or_waveform (numpy.ndarray): speech features or waveform.
    """
    _path, slice_ptr = parse_path(path)
    if len(slice_ptr) == 0:
        if need_waveform:
            return get_waveform(
                _path, always_2d=False, output_sample_rate=use_sample_rate
            )[0]
        return get_features_from_npy_or_audio(_path)
    elif len(slice_ptr) == 2:
        features_or_waveform = get_features_or_waveform_from_stored_zip(
            _path,
            slice_ptr[0],
            slice_ptr[1],
            need_waveform=need_waveform,
            use_sample_rate=use_sample_rate,
        )
    else:
        raise ValueError(f"Invalid path: {path}")

    return features_or_waveform


def _get_kaldi_fbank(
    waveform: np.ndarray, sample_rate: int, n_bins=80
) -> Optional[np.ndarray]:
    """Get mel-filter bank features via PyKaldi."""
    try:
        from kaldi.feat.fbank import Fbank, FbankOptions
        from kaldi.feat.mel import MelBanksOptions
        from kaldi.feat.window import FrameExtractionOptions
        from kaldi.matrix import Vector

        mel_opts = MelBanksOptions()
        mel_opts.num_bins = n_bins
        frame_opts = FrameExtractionOptions()
        frame_opts.samp_freq = sample_rate
        opts = FbankOptions()
        opts.mel_opts = mel_opts
        opts.frame_opts = frame_opts
        fbank = Fbank(opts=opts)
        features = fbank.compute(Vector(waveform.squeeze()), 1.0).numpy()
        return features
    except ImportError:
        return None


def _get_torchaudio_fbank(
    waveform: np.ndarray, sample_rate, n_bins=80
) -> Optional[np.ndarray]:
    """Get mel-filter bank features via TorchAudio."""
    try:
        import torchaudio.compliance.kaldi as ta_kaldi

        waveform = torch.from_numpy(waveform)
        features = ta_kaldi.fbank(
            waveform, num_mel_bins=n_bins, sample_frequency=sample_rate
        )
        return features.numpy()
    except ImportError:
        return None


def get_fbank(path_or_fp: Union[str, BinaryIO], n_bins=80) -> np.ndarray:
    """Get mel-filter bank features via PyKaldi or TorchAudio. Prefer PyKaldi
    (faster CPP implementation) to TorchAudio (Python implementation). Note that
    Kaldi/TorchAudio requires 16-bit signed integers as inputs and hence the
    waveform should not be normalized."""
    waveform, sample_rate = get_waveform(path_or_fp, normalization=False)

    features = _get_kaldi_fbank(waveform, sample_rate, n_bins)
    if features is None:
        features = _get_torchaudio_fbank(waveform, sample_rate, n_bins)
    if features is None:
        raise ImportError(
            "Please install pyKaldi or torchaudio to enable "
            "online filterbank feature extraction"
        )

    return features


def is_npy_data(data: bytes) -> bool:
    return data[0] == 147 and data[1] == 78


def is_sf_audio_data(data: bytes) -> bool:
    is_wav = data[0] == 82 and data[1] == 73 and data[2] == 70
    is_flac = data[0] == 102 and data[1] == 76 and data[2] == 97
    is_ogg = data[0] == 79 and data[1] == 103 and data[2] == 103
    return is_wav or is_flac or is_ogg


def mmap_read(path: str, offset: int, length: int) -> bytes:
    with open(path, "rb") as f:
        with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_o:
            data = mmap_o[offset : offset + length]
    return data


def read_from_stored_zip(zip_path: str, offset: int, length: int) -> bytes:
    return mmap_read(zip_path, offset, length)


def parse_path(path: str) -> Tuple[str, List[int]]:
    """Parse data path which is either a path to
    1. a .npy/.wav/.flac/.ogg file
    2. a stored ZIP file with slicing info: "[zip_path]:[offset]:[length]"

      Args:
          path (str): the data path to parse

      Returns:
          file_path (str): the file path
          slice_ptr (list of int): empty in case 1;
            byte offset and length for the slice in case 2
    """

    if Path(path).suffix in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS:
        _path, slice_ptr = path, []
    else:
        _path, *slice_ptr = path.split(":")
        if not Path(_path).is_file():
            raise FileNotFoundError(f"File not found: {_path}")
    assert len(slice_ptr) in {0, 2}, f"Invalid path: {path}"
    slice_ptr = [int(i) for i in slice_ptr]
    return _path, slice_ptr


def get_window(window_fn: callable, n_fft: int, win_length: int) -> torch.Tensor:
    padding = n_fft - win_length
    assert padding >= 0
    return F.pad(window_fn(win_length), (padding // 2, padding - padding // 2))


def get_fourier_basis(n_fft: int) -> torch.Tensor:
    basis = np.fft.fft(np.eye(n_fft))
    basis = np.vstack(
        [np.real(basis[: n_fft // 2 + 1, :]), np.imag(basis[: n_fft // 2 + 1, :])]
    )
    return torch.from_numpy(basis).float()


def get_mel_filters(
    sample_rate: int, n_fft: int, n_mels: int, f_min: float, f_max: float
) -> torch.Tensor:
    try:
        import librosa
    except ImportError:
        raise ImportError("Please install librosa: pip install librosa")
    basis = librosa.filters.mel(sample_rate, n_fft, n_mels, f_min, f_max)
    return torch.from_numpy(basis).float()


class TTSSpectrogram(torch.nn.Module):
    def __init__(
        self,
        n_fft: int,
        win_length: int,
        hop_length: int,
        window_fn: callable = torch.hann_window,
        return_phase: bool = False,
    ) -> None:
        super(TTSSpectrogram, self).__init__()
        self.n_fft = n_fft
        self.hop_length = hop_length
        self.return_phase = return_phase

        basis = get_fourier_basis(n_fft).unsqueeze(1)
        basis *= get_window(window_fn, n_fft, win_length)
        self.register_buffer("basis", basis)

    def forward(
        self, waveform: torch.Tensor
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        padding = (self.n_fft // 2, self.n_fft // 2)
        x = F.pad(waveform.unsqueeze(1), padding, mode="reflect")
        x = F.conv1d(x, self.basis, stride=self.hop_length)
        real_part = x[:, : self.n_fft // 2 + 1, :]
        imag_part = x[:, self.n_fft // 2 + 1 :, :]
        magnitude = torch.sqrt(real_part**2 + imag_part**2)
        if self.return_phase:
            phase = torch.atan2(imag_part, real_part)
            return magnitude, phase
        return magnitude


class TTSMelScale(torch.nn.Module):
    def __init__(
        self, n_mels: int, sample_rate: int, f_min: float, f_max: float, n_stft: int
    ) -> None:
        super(TTSMelScale, self).__init__()
        basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max)
        self.register_buffer("basis", basis)

    def forward(self, specgram: torch.Tensor) -> torch.Tensor:
        return torch.matmul(self.basis, specgram)
