# Reference: # https://github.com/bytedance/Make-An-Audio-2
from typing import Literal

import torch
import torch.nn as nn
import numpy as np

# following is from librosa

def hz_to_mel(frequencies, *, htk = False):
    frequencies = np.asanyarray(frequencies)

    if htk:
        mels: np.ndarray = 2595.0 * np.log10(1.0 + frequencies / 700.0)
        return mels

    # Fill in the linear part
    f_min = 0.0
    f_sp = 200.0 / 3

    mels = (frequencies - f_min) / f_sp

    # Fill in the log-scale part

    min_log_hz = 1000.0  # beginning of log region (Hz)
    min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
    logstep = np.log(6.4) / 27.0  # step size for log region

    if frequencies.ndim:
        # If we have array data, vectorize
        log_t = frequencies >= min_log_hz
        mels[log_t] = min_log_mel + np.log(frequencies[log_t] / min_log_hz) / logstep
    elif frequencies >= min_log_hz:
        # If we have scalar data, heck directly
        mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep

    return mels

def mel_to_hz(mels, *, htk = False):
    mels = np.asanyarray(mels)

    if htk:
        return 700.0 * (10.0 ** (mels / 2595.0) - 1.0)

    # Fill in the linear scale
    f_min = 0.0
    f_sp = 200.0 / 3
    freqs = f_min + f_sp * mels

    # And now the nonlinear scale
    min_log_hz = 1000.0  # beginning of log region (Hz)
    min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
    logstep = np.log(6.4) / 27.0  # step size for log region

    if mels.ndim:
        # If we have vector data, vectorize
        log_t = mels >= min_log_mel
        freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
    elif mels >= min_log_mel:
        # If we have scalar data, check directly
        freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel))

    return freqs

def mel_frequencies(n_mels = 128, *, fmin = 0.0, fmax = 11025.0, htk = False):
    min_mel = hz_to_mel(fmin, htk=htk)
    max_mel = hz_to_mel(fmax, htk=htk)
    mels = np.linspace(min_mel, max_mel, n_mels)
    hz: np.ndarray = mel_to_hz(mels, htk=htk)
    return hz

def librosa_mel_fn(
    *,
    sr: float,
    n_fft: int,
    n_mels: int = 128,
    fmin: float = 0.0,
    fmax = None,
    htk = False,
    norm = "slaney",
    dtype = np.float32,
) -> np.ndarray:
   
    if fmax is None:
        fmax = float(sr) / 2

    # Initialize the weights
    n_mels = int(n_mels)
    weights = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)

    # Center freqs of each FFT bin
    fftfreqs = np.fft.rfftfreq(n=n_fft, d=1.0 / sr)

    # 'Center freqs' of mel bands - uniformly spaced between limits
    mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax, htk=htk)

    fdiff = np.diff(mel_f)
    ramps = np.subtract.outer(mel_f, fftfreqs)

    for i in range(n_mels):
        # lower and upper slopes for all bins
        lower = -ramps[i] / fdiff[i]
        upper = ramps[i + 2] / fdiff[i + 1]

        # .. then intersect them with each other and zero
        weights[i] = np.maximum(0, np.minimum(lower, upper))

        # Slaney-style mel is scaled to be approx constant energy per channel
        enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
        weights *= enorm[:, np.newaxis]

    return weights


def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
    return norm_fn(torch.clamp(x, min=clip_val) * C)


def spectral_normalize_torch(magnitudes, norm_fn):
    output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
    return output


class MelConverter(nn.Module):

    def __init__(
        self,
        *,
        sampling_rate: float,
        n_fft: int,
        num_mels: int,
        hop_size: int,
        win_size: int,
        fmin: float,
        fmax: float,
        norm_fn,
    ):
        super().__init__()
        self.sampling_rate = sampling_rate
        self.n_fft = n_fft
        self.num_mels = num_mels
        self.hop_size = hop_size
        self.win_size = win_size
        self.fmin = fmin
        self.fmax = fmax
        self.norm_fn = norm_fn

        mel = librosa_mel_fn(sr=self.sampling_rate,
                             n_fft=self.n_fft,
                             n_mels=self.num_mels,
                             fmin=self.fmin,
                             fmax=self.fmax)
        mel_basis = torch.from_numpy(mel).float()
        hann_window = torch.hann_window(self.win_size)

        self.register_buffer('mel_basis', mel_basis)
        self.register_buffer('hann_window', hann_window)

    @property
    def device(self):
        return self.mel_basis.device

    def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
        waveform = waveform.clamp(min=-1., max=1.).to(self.device)

        waveform = torch.nn.functional.pad(
            waveform.unsqueeze(1),
            [int((self.n_fft - self.hop_size) / 2),
             int((self.n_fft - self.hop_size) / 2)],
            mode='reflect')
        waveform = waveform.squeeze(1)

        spec = torch.stft(waveform,
                          self.n_fft,
                          hop_length=self.hop_size,
                          win_length=self.win_size,
                          window=self.hann_window,
                          center=center,
                          pad_mode='reflect',
                          normalized=False,
                          onesided=True,
                          return_complex=True)

        spec = torch.view_as_real(spec)
        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)).float()
        spec = torch.matmul(self.mel_basis, spec)
        spec = spectral_normalize_torch(spec, self.norm_fn)

        return spec


def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter:
    if mode == '16k':
        return MelConverter(sampling_rate=16_000,
                            n_fft=1024,
                            num_mels=80,
                            hop_size=256,
                            win_size=1024,
                            fmin=0,
                            fmax=8_000,
                            norm_fn=torch.log10)
    elif mode == '44k':
        return MelConverter(sampling_rate=44_100,
                            n_fft=2048,
                            num_mels=128,
                            hop_size=512,
                            win_size=2048,
                            fmin=0,
                            fmax=44100 / 2,
                            norm_fn=torch.log)
    else:
        raise ValueError(f'Unknown mode: {mode}')