#!/usr/bin/env python3
# Copyright 2018 Mycroft AI Inc.
#
# 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.
from itertools import chain
from math import sqrt

import numpy as np
import os
from glob import glob
from os import makedirs
from os.path import join, dirname, abspath
from pip._vendor.distlib._backport import shutil
from prettyparse import create_parser
from random import random

from precise.train_data import TrainData
from precise.util import load_audio
from precise.util import save_audio

usage = '''
    Create a duplicate dataset with added noise
    
    :folder str
        Folder containing source dataset
    
    :-tg --tags-file str -
        Tags file to optionally load from

    :noise_folder str
        Folder with wav files containing noise to be added

    :output_folder str
        Folder to write the duplicate generated dataset
    ...
'''


class NoiseData:
    def __init__(self, noise_folder: str):
        self.noise_data = [
            load_audio(file)
            for file in glob(join(noise_folder, '*.wav'))
        ]
        self.noise_data_id = 0
        self.noise_pos = 0
        self.repeat_count = 0

    def get_fresh_noise(self, n: int) -> np.ndarray:
        noise_audio = np.empty(0)
        while len(noise_audio) < n:
            noise_source = self.noise_data[self.noise_data_id]
            noise_chunk = noise_source[self.noise_pos:self.noise_pos + n - len(noise_audio)]
            self.noise_pos += n - len(noise_audio)
            if self.noise_pos >= len(noise_source):
                self.noise_pos = 0
                self.noise_data_id += 1
                if self.noise_data_id >= len(self.noise_data):
                    self.noise_data_id = 0
                    self.repeat_count += 1
                    if self.repeat_count == 100:
                        print('Warning: Repeating noise 100+ times. Add more to prevent '
                              'overfitting.')

            noise_audio = np.concatenate([noise_audio, noise_chunk])
        return noise_audio

    def noised_audio(self, audio: np.ndarray) -> np.ndarray:
        noise_data = self.get_fresh_noise(len(audio))
        audio_volume = sqrt(sum(audio ** 2))
        noise_volume = sqrt(sum(noise_data ** 2))
        adjusted_noise = audio_volume * noise_data / noise_volume
        ratio = 0.0 + 0.4 * random()
        return ratio * adjusted_noise + (1.0 - ratio) * audio


def main():
    args = create_parser(usage).parse_args()
    args.tags_file = abspath(args.tags_file)
    args.folder = abspath(args.folder)
    args.output_folder = abspath(args.output_folder)

    data = TrainData.from_both(args.tags_file, args.folder, args.folder)
    noise_data = NoiseData(args.noise_folder)
    print('Data:', data)

    def translate_filename(source: str) -> str:
        assert source.startswith(args.folder)
        relative_file = source[len(args.folder):].strip(os.path.sep)
        return join(args.output_folder, relative_file)

    all_filenames = sum(data.train_files + data.test_files, [])
    for i, filename in enumerate(all_filenames):
        print('{0:.2%}  \r'.format(i / (len(all_filenames) - 1)), end='', flush=True)

        audio = load_audio(filename)
        altered = noise_data.noised_audio(audio)
        output_filename = translate_filename(filename)

        makedirs(dirname(output_filename), exist_ok=True)
        save_audio(output_filename, altered)

    print('Done!')

    if args.tags_file and args.tags_file.startswith(args.folder):
        shutil.copy2(args.tags_file, translate_filename(args.tags_file))


if __name__ == '__main__':
    main()