# -*- coding: utf-8 -*-

# Copyright 2019 Tomoki Hayashi
#  MIT License (https://opensource.org/licenses/MIT)

"""STFT-based Loss modules."""
import librosa
import torch

from text_to_speech.modules.vocoder.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft


class STFTLoss(torch.nn.Module):
    """STFT loss module."""

    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window",
                 use_mel_loss=False):
        """Initialize STFT loss module."""
        super(STFTLoss, self).__init__()
        self.fft_size = fft_size
        self.shift_size = shift_size
        self.win_length = win_length
        self.window = getattr(torch, window)(win_length)
        self.spectral_convergenge_loss = SpectralConvergengeLoss()
        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
        self.use_mel_loss = use_mel_loss
        self.mel_basis = None

    def forward(self, x, y):
        """Calculate forward propagation.

        Args:
            x (Tensor): Predicted signal (B, T).
            y (Tensor): Groundtruth signal (B, T).

        Returns:
            Tensor: Spectral convergence loss value.
            Tensor: Log STFT magnitude loss value.

        """
        if self.window.device != x.device:
            self.window = self.window.to(x.device)
        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
        if self.use_mel_loss:
            if self.mel_basis is None:
                self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T
            x_mag = x_mag @ self.mel_basis
            y_mag = y_mag @ self.mel_basis

        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)

        return sc_loss, mag_loss


class MultiResolutionSTFTLoss(torch.nn.Module):
    """Multi resolution STFT loss module."""

    def __init__(self,
                 fft_sizes=[1024, 2048, 512],
                 hop_sizes=[120, 240, 50],
                 win_lengths=[600, 1200, 240],
                 window="hann_window",
                 use_mel_loss=False):
        """Initialize Multi resolution STFT loss module.

        Args:
            fft_sizes (list): List of FFT sizes.
            hop_sizes (list): List of hop sizes.
            win_lengths (list): List of window lengths.
            window (str): Window function type.

        """
        super(MultiResolutionSTFTLoss, self).__init__()
        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
        self.stft_losses = torch.nn.ModuleList()
        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
            self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)]

    def forward(self, x, y):
        """Calculate forward propagation.

        Args:
            x (Tensor): Predicted signal (B, T).
            y (Tensor): Groundtruth signal (B, T).

        Returns:
            Tensor: Multi resolution spectral convergence loss value.
            Tensor: Multi resolution log STFT magnitude loss value.

        """
        sc_loss = 0.0
        mag_loss = 0.0
        for f in self.stft_losses:
            sc_l, mag_l = f(x, y)
            sc_loss += sc_l
            mag_loss += mag_l
        sc_loss /= len(self.stft_losses)
        mag_loss /= len(self.stft_losses)

        return sc_loss, mag_loss