"""
BSD 3-Clause License

Copyright (c) 2018, NVIDIA Corporation
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util


def window_sumsquare(window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None):
    """
    # from librosa 0.6
    Compute the sum-square envelope of a window function at a given hop length.

    This is used to estimate modulation effects induced by windowing
    observations in short-time fourier transforms.

    Parameters
    ----------
    window : string, tuple, number, callable, or list-like
        Window specification, as in `get_window`

    n_frames : int > 0
        The number of analysis frames

    hop_length : int > 0
        The number of samples to advance between frames

    win_length : [optional]
        The length of the window function.  By default, this matches `n_fft`.

    n_fft : int > 0
        The length of each analysis frame.

    dtype : np.dtype
        The data type of the output

    Returns
    -------
    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
        The sum-squared envelope of the window function
    """
    if win_length is None:
        win_length = n_fft

    n = n_fft + hop_length * (n_frames - 1)
    x = np.zeros(n, dtype=dtype)

    # Compute the squared window at the desired length
    win_sq = get_window(window, win_length, fftbins=True)
    win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2
    win_sq = librosa_util.pad_center(win_sq, n_fft)

    # Fill the envelope
    for i in range(n_frames):
        sample = i * hop_length
        x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
    return x


def griffin_lim(magnitudes, stft_fn, n_iters=30):
    """
    PARAMS
    ------
    magnitudes: spectrogram magnitudes
    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
    """

    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
    angles = angles.astype(np.float32)
    angles = torch.autograd.Variable(torch.from_numpy(angles))
    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)

    for i in range(n_iters):
        _, angles = stft_fn.transform(signal)
        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
    return signal


def dynamic_range_compression(x, C=1, clip_val=1e-5):
    """
    PARAMS
    ------
    C: compression factor
    """
    return torch.log(torch.clamp(x, min=clip_val) * C)


def dynamic_range_decompression(x, C=1):
    """
    PARAMS
    ------
    C: compression factor used to compress
    """
    return torch.exp(x) / C