# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com. 
https://www.github.com/kyubyong/dc_tts
'''
from __future__ import print_function, division

import numpy as np
import librosa
import os, copy
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
from scipy import signal

from .audio_params import Hyperparams as hp
import tensorflow as tf

def get_spectrograms(fpath):
    '''Parse the wave file in `fpath` and
    Returns normalized melspectrogram and linear spectrogram.

    Args:
      fpath: A string. The full path of a sound file.

    Returns:
      mel: A 2d array of shape (T, n_mels) and dtype of float32.
      mag: A 2d array of shape (T, 1+n_fft/2) and dtype of float32.
    '''
    # Loading sound file
    y, sr = librosa.load(fpath, sr=hp.sr)

    # Trimming
    y, _ = librosa.effects.trim(y)

    # Preemphasis
    y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1])

    # stft
    linear = librosa.stft(y=y,
                          n_fft=hp.n_fft,
                          hop_length=hp.hop_length,
                          win_length=hp.win_length)

    # magnitude spectrogram
    mag = np.abs(linear)  # (1+n_fft//2, T)

    # mel spectrogram
    mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels)  # (n_mels, 1+n_fft//2)
    mel = np.dot(mel_basis, mag)  # (n_mels, t)

    # to decibel
    mel = 20 * np.log10(np.maximum(1e-5, mel))
    mag = 20 * np.log10(np.maximum(1e-5, mag))

    # normalize
    mel = np.clip((mel - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)
    mag = np.clip((mag - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)

    # Transpose
    mel = mel.T.astype(np.float32)  # (T, n_mels)
    mag = mag.T.astype(np.float32)  # (T, 1+n_fft//2)

    return mel, mag

def spectrogram2wav(mag):
    '''# Generate wave file from linear magnitude spectrogram

    Args:
      mag: A numpy array of (T, 1+n_fft//2)

    Returns:
      wav: A 1-D numpy array.
    '''
    # transpose
    mag = mag.T

    # de-noramlize
    mag = (np.clip(mag, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db

    # to amplitude
    mag = np.power(10.0, mag * 0.05)

    # wav reconstruction
    wav = griffin_lim(mag**hp.power)

    # de-preemphasis
    wav = signal.lfilter([1], [1, -hp.preemphasis], wav)

    # trim
    wav, _ = librosa.effects.trim(wav)

    return wav.astype(np.float32)

def griffin_lim(spectrogram):
    '''Applies Griffin-Lim's raw.'''
    X_best = copy.deepcopy(spectrogram)
    for i in range(hp.n_iter):
        X_t = invert_spectrogram(X_best)
        est = librosa.stft(X_t, hp.n_fft, hp.hop_length, win_length=hp.win_length)
        phase = est / np.maximum(1e-8, np.abs(est))
        X_best = spectrogram * phase
    X_t = invert_spectrogram(X_best)
    y = np.real(X_t)

    return y

def invert_spectrogram(spectrogram):
    '''Applies inverse fft.
    Args:
      spectrogram: [1+n_fft//2, t]
    '''
    return librosa.istft(spectrogram, hp.hop_length, win_length=hp.win_length, window="hann")

def plot_alignment(alignment, gs, dir=hp.logdir):
    """Plots the alignment.

    Args:
      alignment: A numpy array with shape of (encoder_steps, decoder_steps)
      gs: (int) global step.
      dir: Output path.
    """
    if not os.path.exists(dir): os.mkdir(dir)

    fig, ax = plt.subplots()
    im = ax.imshow(alignment)

    fig.colorbar(im)
    plt.title('{} Steps'.format(gs))
    plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png')
    plt.close(fig)

def guided_attention(g=0.2):
    '''Guided attention. Refer to page 3 on the paper.'''
    W = np.zeros((hp.max_N, hp.max_T), dtype=np.float32)
    for n_pos in range(W.shape[0]):
        for t_pos in range(W.shape[1]):
            W[n_pos, t_pos] = 1 - np.exp(-(t_pos / float(hp.max_T) - n_pos / float(hp.max_N)) ** 2 / (2 * g * g))
    return W

def learning_rate_decay(init_lr, global_step, warmup_steps = 4000.0):
    '''Noam scheme from tensor2tensor'''
    step = tf.to_float(global_step + 1)
    return init_lr * warmup_steps**0.5 * tf.minimum(step * warmup_steps**-1.5, step**-0.5)

def load_spectrograms(fpath):
    '''Read the wave file in `fpath`
    and extracts spectrograms'''

    fname = os.path.basename(fpath)
    mel, mag = get_spectrograms(fpath)
    t = mel.shape[0]

    # Marginal padding for reduction shape sync.
    num_paddings = hp.r - (t % hp.r) if t % hp.r != 0 else 0
    mel = np.pad(mel, [[0, num_paddings], [0, 0]], mode="constant")
    mag = np.pad(mag, [[0, num_paddings], [0, 0]], mode="constant")

    # Reduction
    mel = mel[::hp.r, :]
    return fname, mel, mag