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import os
import random
import argparse
import json
import torch
import torch.utils.data
import sys
from scipy.io.wavfile import read
from tqdm import tqdm
import hydra
from omegaconf import DictConfig, OmegaConf

# We're using the audio processing from TacoTron2 to make sure it matches
from .stft import TacotronSTFT

MAX_WAV_VALUE = 32768.0

def files_to_list(data_path):
    """
    Load all .wav files in data_path
    """
    files = [os.path.join(data_path, f.rstrip()) for f in os.listdir(data_path) if len(f)>=4 and f[-4:]=='.wav']
    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, data_path, valid, segment_length, filter_length,
                 hop_length, win_length, sampling_rate, mel_fmin, mel_fmax):
        self.audio_files = files_to_list(data_path)
        self.valid = valid
        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 self.valid:
            # whole audio for valid set
            pass
        else: # training
            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
        audio = audio.unsqueeze(0) # (1, length)

        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
# ===================================================================
# Original code from philsyn/diffwave-Vocoder
# Not used in this codebase, but can be used to create a folder of preprocessed spectrograms
@hydra.main(version_base=None, config_path="../configs/", config_name="config")
def main(cfg: DictConfig) -> None:
    print(OmegaConf.to_yaml(cfg.dataset))
    OmegaConf.set_struct(cfg, False)  # Allow writing keys
    # 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)["validset_config"]
    # data_config = json.loads(data)["dataset_config"]
    cfg.dataset.pop("_name_")
    mel2samp = Mel2Samp(**cfg.dataset)

    filepaths = files_to_list(cfg.dataset.data_path)
    filepaths = sorted(filepaths)

    # Make directory if it doesn't exist
    os.makedirs(cfg.output_dir, mode=0o775, exist_ok=True)

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

if __name__ == "__main__":
    main()
