# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle
from paddle import nn
from paddle.nn import functional as F

from parakeet.modules.audio import STFT


class SpectralConvergenceLoss(nn.Layer):
    """Spectral convergence loss module."""

    def __init__(self):
        """Initilize spectral convergence loss module."""
        super().__init__()

    def forward(self, x_mag, y_mag):
        """Calculate forward propagation.
        Args:
            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, C, T).
            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, C, T).
        Returns:
            Tensor: Spectral convergence loss value.
        """
        return paddle.norm(
            y_mag - x_mag, p="fro") / paddle.norm(
                y_mag, p="fro")


class LogSTFTMagnitudeLoss(nn.Layer):
    """Log STFT magnitude loss module."""

    def __init__(self):
        """Initilize los STFT magnitude loss module."""
        super().__init__()

    def forward(self, x_mag, y_mag):
        """Calculate forward propagation.
        Args:
            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
        Returns:
            Tensor: Log STFT magnitude loss value.
        """
        return F.l1_loss(paddle.log(y_mag), paddle.log(x_mag))


class STFTLoss(nn.Layer):
    """STFT loss module."""

    def __init__(self,
                 fft_size=1024,
                 shift_size=120,
                 win_length=600,
                 window="hann"):
        """Initialize STFT loss module."""
        super().__init__()
        self.fft_size = fft_size
        self.shift_size = shift_size
        self.win_length = win_length
        self.stft = STFT(
            n_fft=fft_size,
            hop_length=shift_size,
            win_length=win_length,
            window=window)
        self.spectral_convergence_loss = SpectralConvergenceLoss()
        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()

    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.
        """
        x_mag = self.stft.magnitude(x)
        y_mag = self.stft.magnitude(y)
        sc_loss = self.spectral_convergence_loss(x_mag, y_mag)
        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)

        return sc_loss, mag_loss


class MultiResolutionSTFTLoss(nn.Layer):
    """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", ):
        """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().__init__()
        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
        self.stft_losses = nn.LayerList()
        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
            self.stft_losses.append(STFTLoss(fs, ss, wl, window))

    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