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# *****************************************************************************\
# from tacotron2.layers import TacotronSTFT
import os
import random
import argparse
import json
import torch
import torch.utils.data
import sys
from scipy.io.wavfile import read

# We're using the audio processing from TacoTron2 to make sure it matches
sys.path.insert(0, 'tacotron2')

MAX_WAV_VALUE = 32768.0


def files_to_list(filename):
    """
    Takes a text file of filenames and makes a list of filenames
    """
    with open(filename, encoding='utf-8') as f:
        files = f.readlines()

    files = [f.rstrip() for f in files]
    return files


# def load_wav_to_torch(full_path):
#     """
#     Loads wavdata into torch array
#     """
#     sampling_rate, data = read(full_path)
#     return torch.from_numpy(data).float(), sampling_rate


# class Mel2Samp(torch.utils.data.Dataset):
#     """
#     This is the main class that calculates the spectrogram and returns the
#     spectrogram, audio pair.
#     """

#     def __init__(self, training_files, segment_length, filter_length,
#                  hop_length, win_length, sampling_rate, mel_fmin, mel_fmax):
#         self.audio_files = files_to_list(training_files)
#         random.seed(1234)
#         random.shuffle(self.audio_files)
#         self.stft = TacotronSTFT(filter_length=filter_length,
#                                  hop_length=hop_length,
#                                  win_length=win_length,
#                                  sampling_rate=sampling_rate,
#                                  mel_fmin=mel_fmin, mel_fmax=mel_fmax)
#         self.segment_length = segment_length
#         self.sampling_rate = sampling_rate

#     def get_mel(self, audio):
#         audio_norm = audio / MAX_WAV_VALUE
#         audio_norm = audio_norm.unsqueeze(0)
#         audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
#         melspec = self.stft.mel_spectrogram(audio_norm)
#         melspec = torch.squeeze(melspec, 0)
#         return melspec

#     def __getitem__(self, index):
#         # Read audio
#         filename = self.audio_files[index]
#         audio, sampling_rate = load_wav_to_torch(filename)
#         if sampling_rate != self.sampling_rate:
#             raise ValueError("{} SR doesn't match target {} SR".format(
#                 sampling_rate, self.sampling_rate))

#         # Take segment
#         if audio.size(0) >= self.segment_length:
#             max_audio_start = audio.size(0) - self.segment_length
#             audio_start = random.randint(0, max_audio_start)
#             audio = audio[audio_start:audio_start+self.segment_length]
#         else:
#             audio = torch.nn.functional.pad(
#                 audio, (0, self.segment_length - audio.size(0)), 'constant').data

#         mel = self.get_mel(audio)
#         audio = audio / MAX_WAV_VALUE

#         return (mel, audio)

#     def __len__(self):
#         return len(self.audio_files)


# # ===================================================================
# # Takes directory of clean audio and makes directory of spectrograms
# # Useful for making test sets
# # ===================================================================
# if __name__ == "__main__":
#     # Get defaults so it can work with no Sacred
#     parser = argparse.ArgumentParser()
#     parser.add_argument('-f', "--filelist_path", required=True)
#     parser.add_argument('-c', '--config', type=str,
#                         help='JSON file for configuration')
#     parser.add_argument('-o', '--output_dir', type=str,
#                         help='Output directory')
#     args = parser.parse_args()

#     with open(args.config) as f:
#         data = f.read()
#     data_config = json.loads(data)["data_config"]
#     mel2samp = Mel2Samp(**data_config)

#     filepaths = files_to_list(args.filelist_path)

#     # Make directory if it doesn't exist
#     if not os.path.isdir(args.output_dir):
#         os.makedirs(args.output_dir)
#         os.chmod(args.output_dir, 0o775)

#     for filepath in filepaths:
#         audio, sr = load_wav_to_torch(filepath)
#         melspectrogram = mel2samp.get_mel(audio)
#         filename = os.path.basename(filepath)
#         new_filepath = args.output_dir + '/' + filename + '.pt'
#         print(new_filepath)
#         torch.save(melspectrogram, new_filepath)