# coding: utf-8
__author__ = "Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/"

import os
import librosa
import soundfile as sf
import numpy as np
import argparse


def stft(wave, nfft, hl):
    wave_left = np.asfortranarray(wave[0])
    wave_right = np.asfortranarray(wave[1])
    spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)
    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)
    spec = np.asfortranarray([spec_left, spec_right])
    return spec


def istft(spec, hl, length):
    spec_left = np.asfortranarray(spec[0])
    spec_right = np.asfortranarray(spec[1])
    wave_left = librosa.istft(spec_left, hop_length=hl, length=length)
    wave_right = librosa.istft(spec_right, hop_length=hl, length=length)
    wave = np.asfortranarray([wave_left, wave_right])
    return wave


def absmax(a, *, axis):
    dims = list(a.shape)
    dims.pop(axis)
    indices = np.ogrid[tuple(slice(0, d) for d in dims)]
    argmax = np.abs(a).argmax(axis=axis)
    indices.insert((len(a.shape) + axis) % len(a.shape), argmax)
    return a[tuple(indices)]


def absmin(a, *, axis):
    dims = list(a.shape)
    dims.pop(axis)
    indices = np.ogrid[tuple(slice(0, d) for d in dims)]
    argmax = np.abs(a).argmin(axis=axis)
    indices.insert((len(a.shape) + axis) % len(a.shape), argmax)
    return a[tuple(indices)]


def lambda_max(arr, axis=None, key=None, keepdims=False):
    idxs = np.argmax(key(arr), axis)
    if axis is not None:
        idxs = np.expand_dims(idxs, axis)
        result = np.take_along_axis(arr, idxs, axis)
        if not keepdims:
            result = np.squeeze(result, axis=axis)
        return result
    else:
        return arr.flatten()[idxs]


def lambda_min(arr, axis=None, key=None, keepdims=False):
    idxs = np.argmin(key(arr), axis)
    if axis is not None:
        idxs = np.expand_dims(idxs, axis)
        result = np.take_along_axis(arr, idxs, axis)
        if not keepdims:
            result = np.squeeze(result, axis=axis)
        return result
    else:
        return arr.flatten()[idxs]


def average_waveforms(pred_track, weights, algorithm):
    """
    :param pred_track: shape = (num, channels, length)
    :param weights: shape = (num, )
    :param algorithm: One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft
    :return: averaged waveform in shape (channels, length)
    """

    pred_track = np.array(pred_track)
    final_length = pred_track.shape[-1]

    mod_track = []
    for i in range(pred_track.shape[0]):
        if algorithm == "avg_wave":
            mod_track.append(pred_track[i] * weights[i])
        elif algorithm in ["median_wave", "min_wave", "max_wave"]:
            mod_track.append(pred_track[i])
        elif algorithm in ["avg_fft", "min_fft", "max_fft", "median_fft"]:
            spec = stft(pred_track[i], nfft=2048, hl=1024)
            if algorithm in ["avg_fft"]:
                mod_track.append(spec * weights[i])
            else:
                mod_track.append(spec)
    pred_track = np.array(mod_track)

    if algorithm in ["avg_wave"]:
        pred_track = pred_track.sum(axis=0)
        pred_track /= np.array(weights).sum().T
    elif algorithm in ["median_wave"]:
        pred_track = np.median(pred_track, axis=0)
    elif algorithm in ["min_wave"]:
        pred_track = np.array(pred_track)
        pred_track = lambda_min(pred_track, axis=0, key=np.abs)
    elif algorithm in ["max_wave"]:
        pred_track = np.array(pred_track)
        pred_track = lambda_max(pred_track, axis=0, key=np.abs)
    elif algorithm in ["avg_fft"]:
        pred_track = pred_track.sum(axis=0)
        pred_track /= np.array(weights).sum()
        pred_track = istft(pred_track, 1024, final_length)
    elif algorithm in ["min_fft"]:
        pred_track = np.array(pred_track)
        pred_track = lambda_min(pred_track, axis=0, key=np.abs)
        pred_track = istft(pred_track, 1024, final_length)
    elif algorithm in ["max_fft"]:
        pred_track = np.array(pred_track)
        pred_track = absmax(pred_track, axis=0)
        pred_track = istft(pred_track, 1024, final_length)
    elif algorithm in ["median_fft"]:
        pred_track = np.array(pred_track)
        pred_track = np.median(pred_track, axis=0)
        pred_track = istft(pred_track, 1024, final_length)
    return pred_track


def ensemble_files(args):
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--files",
        type=str,
        required=True,
        nargs="+",
        help="Path to all audio-files to ensemble",
    )
    parser.add_argument(
        "--type",
        type=str,
        default="avg_wave",
        help="One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft",
    )
    parser.add_argument(
        "--weights",
        type=float,
        nargs="+",
        help="Weights to create ensemble. Number of weights must be equal to number of files",
    )
    parser.add_argument(
        "--output",
        default="res.wav",
        type=str,
        help="Path to wav file where ensemble result will be stored",
    )
    if args is None:
        args = parser.parse_args()
    else:
        args = parser.parse_args(args)

    print("Ensemble type: {}".format(args.type))
    print("Number of input files: {}".format(len(args.files)))
    if args.weights is not None:
        weights = args.weights
    else:
        weights = np.ones(len(args.files))
    print("Weights: {}".format(weights))
    print("Output file: {}".format(args.output))
    data = []
    for f in args.files:
        if not os.path.isfile(f):
            print("Error. Can't find file: {}. Check paths.".format(f))
            exit()
        print("Reading file: {}".format(f))
        wav, sr = librosa.load(f, sr=None, mono=False)
        # wav, sr = sf.read(f)
        print("Waveform shape: {} sample rate: {}".format(wav.shape, sr))
        data.append(wav)
    data = np.array(data)
    res = average_waveforms(data, weights, args.type)
    print("Result shape: {}".format(res.shape))
    sf.write(args.output, res.T, sr, "FLOAT")


if __name__ == "__main__":
    ensemble_files(None)