# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# 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.
"""STFT-based loss modules."""

import tensorflow as tf


class TFSpectralConvergence(tf.keras.layers.Layer):
    """Spectral convergence loss."""

    def __init__(self):
        """Initialize."""
        super().__init__()

    def call(self, y_mag, x_mag):
        """Calculate forward propagation.
        Args:
            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
        Returns:
            Tensor: Spectral convergence loss value.
        """
        return tf.norm(y_mag - x_mag, ord="fro", axis=(-2, -1)) / tf.norm(
            y_mag, ord="fro", axis=(-2, -1)
        )


class TFLogSTFTMagnitude(tf.keras.layers.Layer):
    """Log STFT magnitude loss module."""

    def __init__(self):
        """Initialize."""
        super().__init__()

    def call(self, y_mag, x_mag):
        """Calculate forward propagation.
        Args:
            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
        Returns:
            Tensor: Spectral convergence loss value.
        """
        return tf.abs(tf.math.log(y_mag) - tf.math.log(x_mag))


class TFSTFT(tf.keras.layers.Layer):
    """STFT loss module."""

    def __init__(self, frame_length=600, frame_step=120, fft_length=1024):
        """Initialize."""
        super().__init__()
        self.frame_length = frame_length
        self.frame_step = frame_step
        self.fft_length = fft_length
        self.spectral_convergenge_loss = TFSpectralConvergence()
        self.log_stft_magnitude_loss = TFLogSTFTMagnitude()

    def call(self, y, x):
        """Calculate forward propagation.
        Args:
            y (Tensor): Groundtruth signal (B, T).
            x (Tensor): Predicted signal (B, T).
        Returns:
            Tensor: Spectral convergence loss value (pre-reduce).
            Tensor: Log STFT magnitude loss value (pre-reduce).
        """
        x_mag = tf.abs(
            tf.signal.stft(
                signals=x,
                frame_length=self.frame_length,
                frame_step=self.frame_step,
                fft_length=self.fft_length,
            )
        )
        y_mag = tf.abs(
            tf.signal.stft(
                signals=y,
                frame_length=self.frame_length,
                frame_step=self.frame_step,
                fft_length=self.fft_length,
            )
        )

        # add small number to prevent nan value.
        # compatible with pytorch version.
        x_mag = tf.clip_by_value(tf.math.sqrt(x_mag ** 2 + 1e-7), 1e-7, 1e3)
        y_mag = tf.clip_by_value(tf.math.sqrt(y_mag ** 2 + 1e-7), 1e-7, 1e3)

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

        return sc_loss, mag_loss


class TFMultiResolutionSTFT(tf.keras.layers.Layer):
    """Multi resolution STFT loss module."""

    def __init__(
        self,
        fft_lengths=[1024, 2048, 512],
        frame_lengths=[600, 1200, 240],
        frame_steps=[120, 240, 50],
    ):
        """Initialize Multi resolution STFT loss module.
        Args:
            frame_lengths (list): List of FFT sizes.
            frame_steps (list): List of hop sizes.
            fft_lengths (list): List of window lengths.
        """
        super().__init__()
        assert len(frame_lengths) == len(frame_steps) == len(fft_lengths)
        self.stft_losses = []
        for frame_length, frame_step, fft_length in zip(
            frame_lengths, frame_steps, fft_lengths
        ):
            self.stft_losses.append(TFSTFT(frame_length, frame_step, fft_length))

    def call(self, y, x):
        """Calculate forward propagation.
        Args:
            y (Tensor): Groundtruth signal (B, T).
            x (Tensor): Predicted 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(y, x)
            sc_loss += tf.reduce_mean(sc_l, axis=list(range(1, len(sc_l.shape))))
            mag_loss += tf.reduce_mean(mag_l, axis=list(range(1, len(mag_l.shape))))

        sc_loss /= len(self.stft_losses)
        mag_loss /= len(self.stft_losses)

        return sc_loss, mag_loss