import librosa
import numpy as np
import torch
from typing import Tuple

from .nets_utils import make_pad_mask


class LogMel(torch.nn.Module):
    """Convert STFT to fbank feats

    The arguments is same as librosa.filters.mel

    Args:
        fs: number > 0 [scalar] sampling rate of the incoming signal
        n_fft: int > 0 [scalar] number of FFT components
        n_mels: int > 0 [scalar] number of Mel bands to generate
        fmin: float >= 0 [scalar] lowest frequency (in Hz)
        fmax: float >= 0 [scalar] highest frequency (in Hz).
            If `None`, use `fmax = fs / 2.0`
        htk: use HTK formula instead of Slaney
        norm: {None, 1, np.inf} [scalar]
            if 1, divide the triangular mel weights by the width of the mel band
            (area normalization).  Otherwise, leave all the triangles aiming for
            a peak value of 1.0

    """

    def __init__(
        self,
        fs: int = 16000,
        n_fft: int = 512,
        n_mels: int = 80,
        fmin: float = None,
        fmax: float = None,
        htk: bool = False,
        norm=1,
    ):
        super().__init__()

        fmin = 0 if fmin is None else fmin
        fmax = fs / 2 if fmax is None else fmax
        _mel_options = dict(
            sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, htk=htk, norm=norm
        )
        self.mel_options = _mel_options

        # Note(kamo): The mel matrix of librosa is different from kaldi.
        melmat = librosa.filters.mel(**_mel_options)
        # melmat: (D2, D1) -> (D1, D2)
        self.register_buffer("melmat", torch.from_numpy(melmat.T).float())
        inv_mel = np.linalg.pinv(melmat)
        self.register_buffer("inv_melmat", torch.from_numpy(inv_mel.T).float())

    def extra_repr(self):
        return ", ".join(f"{k}={v}" for k, v in self.mel_options.items())

    def forward(
        self, feat: torch.Tensor, ilens: torch.Tensor = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # feat: (B, T, D1) x melmat: (D1, D2) -> mel_feat: (B, T, D2)
        mel_feat = torch.matmul(feat, self.melmat)

        logmel_feat = (mel_feat + 1e-20).log()
        # Zero padding
        if ilens is not None:
            logmel_feat = logmel_feat.masked_fill(
                make_pad_mask(ilens, logmel_feat, 1), 0.0
            )
        else:
            ilens = feat.new_full(
                [feat.size(0)], fill_value=feat.size(1), dtype=torch.long
            )
        return logmel_feat, ilens