import numpy as np
import torch
import torchaudio
from librosa.filters import mel as librosa_mel_fn


def dynamic_range_compression(x, C=1, clip_val=1e-5):
    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)


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


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


mel_basis = {}
hann_window = {}


def mel_spectrogram(
    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
    global mel_basis, hann_window  # pylint: disable=global-statement
    if f"{str(fmax)}_{str(y.device)}" not in mel_basis:
        mel = librosa_mel_fn(
            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
        )
        mel_basis[str(fmax) + "_" + str(y.device)] = (
            torch.from_numpy(mel).float().to(y.device)
        )
        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)

    y = torch.nn.functional.pad(
        y.unsqueeze(1),
        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
        mode="reflect",
    )
    y = y.squeeze(1)

    spec = torch.view_as_real(
        torch.stft(
            y,
            n_fft,
            hop_length=hop_size,
            win_length=win_size,
            window=hann_window[str(y.device)],
            center=center,
            pad_mode="reflect",
            normalized=False,
            onesided=True,
            return_complex=True,
        )
    )

    spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))

    spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
    spec = spectral_normalize_torch(spec)

    return spec


class MelExtractor(object):
    def __init__(
        self,
        num_mels: int = 80,
        n_fft: int = 1920,
        hop_size: int = 480,
        win_size: int = 1920,
        sampling_rate: int = 24000,
        fmin: int = 0,
        fmax: int = 8000,
        center: bool = False,
    ):
        super().__init__()
        self.num_mels = num_mels
        self.n_fft = n_fft
        self.hop_size = hop_size
        self.win_size = win_size
        self.sampling_rate = sampling_rate
        self.fmin = fmin
        self.fmax = fmax
        self.center = center

    def __call__(self, audio: torch.Tensor, audio_sr: int):
        """Args:
            audio(torch.Tensor): shape (1, t)
        Returns:
            mel(torch.Tensor): shape (1, num_mels, t')
        """
        if audio_sr != self.sampling_rate:
            audio = torchaudio.functional.resample(
                audio, orig_freq=audio_sr, new_freq=self.sampling_rate
            )
            audio_sr = self.sampling_rate
        mel = mel_spectrogram(
            audio,
            self.n_fft,
            self.num_mels,
            self.sampling_rate,
            self.hop_size,
            self.win_size,
            self.fmin,
            self.fmax,
            self.center,
        )  # (1, num_mels, t)
        return mel