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import os
import sys
import json
import torch
import random
import librosa
import argparse
import numpy as np
import torch.utils.data

import speech_utils

from tacotron2_layers import TacotronSTFT

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, sampling_rate_param):
	"""
	Loads wavdata into torch array
	"""
	data, sampling_rate = librosa.core.load( full_path, sr=sampling_rate_param )
	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)
		
		self.mel_basis_p = librosa.filters.mel(
          sr=sampling_rate,
          n_fft=1024,
          n_mels=80,
          htk=True,
          norm=None,
          fmax=None
      )

		# Fix to remove files smaller than sample length
		# for file in self.audio_files:
		# 	audio_data, sample_r = load_wav_to_torch(file, sampling_rate)
		# 	if audio_data.size(0) < segment_length:
		# 		self.audio_files.remove(file)

		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

	# Get mel in OpenSeq2Seq format
	def get_mel(self, audio):
		return torch.squeeze(torch.from_numpy(speech_utils.get_speech_features( 
								audio,
								self.sampling_rate,
								80, 
								features_type="mel",
								n_fft=1024,
								hop_length=256,
								mag_power=1,
								feature_normalize=False,
								mean=0.,
								std=1.,
								data_min=1e-5,
								mel_basis=self.mel_basis_p).T))

	def get_mel_waveglow(self, audio):
		audio_norm = 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, self.sampling_rate)
		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]
			
			# --- Take the segment portion that is not completely silent
			# audio_std = 0
			# tries = 20
			# while (audio_std*MAX_WAV_VALUE) < 1e-5 and tries>0:
			# 	max_audio_start = audio.size(0) - self.segment_length
			# 	audio_start = random.randint(0, max_audio_start)
			# 	segment = audio[audio_start:audio_start+self.segment_length]
			# 	audio_std = segment.std()
			# 	tries -= 1
			# audio = segment
		else:
			audio = torch.nn.functional.pad(audio, (0, self.segment_length - audio.size(0)), 'constant').data

		mel = self.get_mel(audio.numpy())
		# 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')
	parser.add_argument('-s', '--sample_rate', type=int,
						help='sample rate')
	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, sampling_rate_param=args.sample_rate)
		
		melspectrogram = mel2samp.get_mel(audio)
		filename = os.path.basename(filepath)
		new_filepath = args.output_dir + '/' + filename + '.npy'
		print(new_filepath)
		# torch.save(melspectrogram, new_filepath)
		np.save( new_filepath, melspectrogram.numpy() )
