from __future__ import absolute_import, division, print_function, unicode_literals
from warnings import warn
import math
import torch
from typing import Optional
from . import functional as F
from .compliance import kaldi


__all__ = [
    'Spectrogram',
    'AmplitudeToDB',
    'MelScale',
    'MelSpectrogram',
    'MFCC',
    'MuLawEncoding',
    'MuLawDecoding',
    'Resample',
]


class Spectrogram(torch.jit.ScriptModule):
    r"""Create a spectrogram from a audio signal

    Args:
        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins
        win_length (int): Window size. (Default: ``n_fft``)
        hop_length (int, optional): Length of hop between STFT windows. (
            Default: ``win_length // 2``)
        pad (int): Two sided padding of signal. (Default: ``0``)
        window_fn (Callable[[...], torch.Tensor]): A function to create a window tensor
            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
        power (int): Exponent for the magnitude spectrogram,
            (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
        normalized (bool): Whether to normalize by magnitude after stft. (Default: ``False``)
        wkwargs (Dict[..., ...]): Arguments for window function. (Default: ``None``)
    """
    __constants__ = ['n_fft', 'win_length', 'hop_length', 'pad', 'power', 'normalized']

    def __init__(self, n_fft=400, win_length=None, hop_length=None,
                 pad=0, window_fn=torch.hann_window,
                 power=2, normalized=False, wkwargs=None):
        super(Spectrogram, self).__init__()
        self.n_fft = n_fft
        # number of FFT bins. the returned STFT result will have n_fft // 2 + 1
        # number of frequecies due to onesided=True in torch.stft
        self.win_length = win_length if win_length is not None else n_fft
        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
        self.window = torch.jit.Attribute(window, torch.Tensor)
        self.pad = pad
        self.power = power
        self.normalized = normalized

    @torch.jit.script_method
    def forward(self, waveform):
        r"""
        Args:
            waveform (torch.Tensor): Tensor of audio of dimension (channel, time)

        Returns:
            torch.Tensor: Dimension (channel, freq, time), where channel
            is unchanged, freq is ``n_fft // 2 + 1`` where ``n_fft`` is the number of
            Fourier bins, and time is the number of window hops (n_frames).
        """
        return F.spectrogram(waveform, self.pad, self.window, self.n_fft, self.hop_length,
                             self.win_length, self.power, self.normalized)


class AmplitudeToDB(torch.jit.ScriptModule):
    r"""Turns a tensor from the power/amplitude scale to the decibel scale.

    This output depends on the maximum value in the input tensor, and so
    may return different values for an audio clip split into snippets vs. a
    a full clip.

    Args:
        stype (str): scale of input tensor ('power' or 'magnitude'). The
            power being the elementwise square of the magnitude. (Default: ``'power'``)
        top_db (float, optional): minimum negative cut-off in decibels.  A reasonable number
            is 80. (Default: ``None``)
    """
    __constants__ = ['multiplier', 'amin', 'ref_value', 'db_multiplier']

    def __init__(self, stype='power', top_db=None):
        super(AmplitudeToDB, self).__init__()
        self.stype = torch.jit.Attribute(stype, str)
        if top_db is not None and top_db < 0:
            raise ValueError('top_db must be positive value')
        self.top_db = torch.jit.Attribute(top_db, Optional[float])
        self.multiplier = 10.0 if stype == 'power' else 20.0
        self.amin = 1e-10
        self.ref_value = 1.0
        self.db_multiplier = math.log10(max(self.amin, self.ref_value))

    @torch.jit.script_method
    def forward(self, x):
        r"""Numerically stable implementation from Librosa
        https://librosa.github.io/librosa/_modules/librosa/core/spectrum.html

        Args:
            x (torch.Tensor): Input tensor before being converted to decibel scale

        Returns:
            torch.Tensor: Output tensor in decibel scale
        """
        return F.amplitude_to_DB(x, self.multiplier, self.amin, self.db_multiplier, self.top_db)


class MelScale(torch.jit.ScriptModule):
    r"""This turns a normal STFT into a mel frequency STFT, using a conversion
    matrix.  This uses triangular filter banks.

    User can control which device the filter bank (`fb`) is (e.g. fb.to(spec_f.device)).

    Args:
        n_mels (int): Number of mel filterbanks. (Default: ``128``)
        sample_rate (int): Sample rate of audio signal. (Default: ``16000``)
        f_min (float): Minimum frequency. (Default: ``0.``)
        f_max (float, optional): Maximum frequency. (Default: ``sample_rate // 2``)
        n_stft (int, optional): Number of bins in STFT. Calculated from first input
            if None is given.  See ``n_fft`` in :class:`Spectrogram`.
    """
    __constants__ = ['n_mels', 'sample_rate', 'f_min', 'f_max']

    def __init__(self, n_mels=128, sample_rate=16000, f_min=0., f_max=None, n_stft=None):
        super(MelScale, self).__init__()
        self.n_mels = n_mels
        self.sample_rate = sample_rate
        self.f_max = f_max if f_max is not None else float(sample_rate // 2)
        assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max)
        self.f_min = f_min
        fb = torch.empty(0) if n_stft is None else F.create_fb_matrix(
            n_stft, self.f_min, self.f_max, self.n_mels)
        self.fb = torch.jit.Attribute(fb, torch.Tensor)

    @torch.jit.script_method
    def forward(self, specgram):
        r"""
        Args:
            specgram (torch.Tensor): A spectrogram STFT of dimension (channel, freq, time)

        Returns:
            torch.Tensor: Mel frequency spectrogram of size (channel, ``n_mels``, time)
        """
        if self.fb.numel() == 0:
            tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels)
            # Attributes cannot be reassigned outside __init__ so workaround
            self.fb.resize_(tmp_fb.size())
            self.fb.copy_(tmp_fb)

        # (channel, frequency, time).transpose(...) dot (frequency, n_mels)
        # -> (channel, time, n_mels).transpose(...)
        mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2)
        return mel_specgram


class MelSpectrogram(torch.jit.ScriptModule):
    r"""Create MelSpectrogram for a raw audio signal. This is a composition of Spectrogram
    and MelScale.

    Sources
        * https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe
        * https://timsainb.github.io/spectrograms-mfccs-and-inversion-in-python.html
        * http://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html

    Args:
        sample_rate (int): Sample rate of audio signal. (Default: ``16000``)
        win_length (int): Window size. (Default: ``n_fft``)
        hop_length (int, optional): Length of hop between STFT windows. (
            Default: ``win_length // 2``)
        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins
        f_min (float): Minimum frequency. (Default: ``0.``)
        f_max (float, optional): Maximum frequency. (Default: ``None``)
        pad (int): Two sided padding of signal. (Default: ``0``)
        n_mels (int): Number of mel filterbanks. (Default: ``128``)
        window_fn (Callable[[...], torch.Tensor]): A function to create a window tensor
            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
        wkwargs (Dict[..., ...]): Arguments for window function. (Default: ``None``)

    Example
        >>> waveform, sample_rate = torchaudio.load('test.wav', normalization=True)
        >>> mel_specgram = transforms.MelSpectrogram(sample_rate)(waveform)  # (channel, n_mels, time)
    """
    __constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad', 'n_mels', 'f_min']

    def __init__(self, sample_rate=16000, n_fft=400, win_length=None, hop_length=None, f_min=0., f_max=None,
                 pad=0, n_mels=128, window_fn=torch.hann_window, wkwargs=None):
        super(MelSpectrogram, self).__init__()
        self.sample_rate = sample_rate
        self.n_fft = n_fft
        self.win_length = win_length if win_length is not None else n_fft
        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
        self.pad = pad
        self.n_mels = n_mels  # number of mel frequency bins
        self.f_max = torch.jit.Attribute(f_max, Optional[float])
        self.f_min = f_min
        self.spectrogram = Spectrogram(n_fft=self.n_fft, win_length=self.win_length,
                                       hop_length=self.hop_length,
                                       pad=self.pad, window_fn=window_fn, power=2,
                                       normalized=False, wkwargs=wkwargs)
        self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1)

    @torch.jit.script_method
    def forward(self, waveform):
        r"""
        Args:
            waveform (torch.Tensor): Tensor of audio of dimension (channel, time)

        Returns:
            torch.Tensor: Mel frequency spectrogram of size (channel, ``n_mels``, time)
        """
        specgram = self.spectrogram(waveform)
        mel_specgram = self.mel_scale(specgram)
        return mel_specgram


class MFCC(torch.jit.ScriptModule):
    r"""Create the Mel-frequency cepstrum coefficients from an audio signal

    By default, this calculates the MFCC on the DB-scaled Mel spectrogram.
    This is not the textbook implementation, but is implemented here to
    give consistency with librosa.

    This output depends on the maximum value in the input spectrogram, and so
    may return different values for an audio clip split into snippets vs. a
    a full clip.

    Args:
        sample_rate (int): Sample rate of audio signal. (Default: ``16000``)
        n_mfcc (int): Number of mfc coefficients to retain. (Default: ``40``)
        dct_type (int): type of DCT (discrete cosine transform) to use. (Default: ``2``)
        norm (str, optional): norm to use. (Default: ``'ortho'``)
        log_mels (bool): whether to use log-mel spectrograms instead of db-scaled. (Default:
            ``False``)
        melkwargs (dict, optional): arguments for MelSpectrogram. (Default: ``None``)
    """
    __constants__ = ['sample_rate', 'n_mfcc', 'dct_type', 'top_db', 'log_mels']

    def __init__(self, sample_rate=16000, n_mfcc=40, dct_type=2, norm='ortho', log_mels=False,
                 melkwargs=None):
        super(MFCC, self).__init__()
        supported_dct_types = [2]
        if dct_type not in supported_dct_types:
            raise ValueError('DCT type not supported'.format(dct_type))
        self.sample_rate = sample_rate
        self.n_mfcc = n_mfcc
        self.dct_type = dct_type
        self.norm = torch.jit.Attribute(norm, Optional[str])
        self.top_db = 80.0
        self.amplitude_to_DB = AmplitudeToDB('power', self.top_db)

        if melkwargs is not None:
            self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate, **melkwargs)
        else:
            self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate)

        if self.n_mfcc > self.MelSpectrogram.n_mels:
            raise ValueError('Cannot select more MFCC coefficients than # mel bins')
        dct_mat = F.create_dct(self.n_mfcc, self.MelSpectrogram.n_mels, self.norm)
        self.dct_mat = torch.jit.Attribute(dct_mat, torch.Tensor)
        self.log_mels = log_mels

    @torch.jit.script_method
    def forward(self, waveform):
        r"""
        Args:
            waveform (torch.Tensor): Tensor of audio of dimension (channel, time)

        Returns:
            torch.Tensor: specgram_mel_db of size (channel, ``n_mfcc``, time)
        """
        mel_specgram = self.MelSpectrogram(waveform)
        if self.log_mels:
            log_offset = 1e-6
            mel_specgram = torch.log(mel_specgram + log_offset)
        else:
            mel_specgram = self.amplitude_to_DB(mel_specgram)
        # (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc)
        # -> (channel, time, n_mfcc).tranpose(...)
        mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
        return mfcc


class MuLawEncoding(torch.jit.ScriptModule):
    r"""Encode signal based on mu-law companding.  For more info see the
    `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_

    This algorithm assumes the signal has been scaled to between -1 and 1 and
    returns a signal encoded with values from 0 to quantization_channels - 1

    Args:
        quantization_channels (int): Number of channels (Default: ``256``)
    """
    __constants__ = ['quantization_channels']

    def __init__(self, quantization_channels=256):
        super(MuLawEncoding, self).__init__()
        self.quantization_channels = quantization_channels

    @torch.jit.script_method
    def forward(self, x):
        r"""
        Args:
            x (torch.Tensor): A signal to be encoded

        Returns:
            x_mu (torch.Tensor): An encoded signal
        """
        return F.mu_law_encoding(x, self.quantization_channels)


class MuLawDecoding(torch.jit.ScriptModule):
    r"""Decode mu-law encoded signal.  For more info see the
    `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_

    This expects an input with values between 0 and quantization_channels - 1
    and returns a signal scaled between -1 and 1.

    Args:
        quantization_channels (int): Number of channels (Default: ``256``)
    """
    __constants__ = ['quantization_channels']

    def __init__(self, quantization_channels=256):
        super(MuLawDecoding, self).__init__()
        self.quantization_channels = quantization_channels

    @torch.jit.script_method
    def forward(self, x_mu):
        r"""
        Args:
            x_mu (torch.Tensor): A mu-law encoded signal which needs to be decoded

        Returns:
            torch.Tensor: The signal decoded
        """
        return F.mu_law_decoding(x_mu, self.quantization_channels)


class Resample(torch.nn.Module):
    r"""Resamples a signal from one frequency to another. A resampling method can
    be given.

    Args:
        orig_freq (float): The original frequency of the signal. (Default: ``16000``)
        new_freq (float): The desired frequency. (Default: ``16000``)
        resampling_method (str): The resampling method (Default: ``'sinc_interpolation'``)
    """
    def __init__(self, orig_freq=16000, new_freq=16000, resampling_method='sinc_interpolation'):
        super(Resample, self).__init__()
        self.orig_freq = orig_freq
        self.new_freq = new_freq
        self.resampling_method = resampling_method

    def forward(self, waveform):
        r"""
        Args:
            waveform (torch.Tensor): The input signal of dimension (channel, time)

        Returns:
            torch.Tensor: Output signal of dimension (channel, time)
        """
        if self.resampling_method == 'sinc_interpolation':
            return kaldi.resample_waveform(waveform, self.orig_freq, self.new_freq)

        raise ValueError('Invalid resampling method: %s' % (self.resampling_method))
