import os
import librosa
import pickle
import copy
import numpy as np
from scipy import signal

_mel_basis = None


class AudioProcessor(object):
    def __init__(self,
                 sample_rate,
                 num_mels,
                 min_level_db,
                 frame_shift_ms,
                 frame_length_ms,
                 ref_level_db,
                 num_freq,
                 power,
                 preemphasis,
                 griffin_lim_iters=None):

        print(" > Setting up Audio Processor...")
        self.sample_rate = sample_rate
        self.num_mels = num_mels
        self.min_level_db = min_level_db
        self.frame_shift_ms = frame_shift_ms
        self.frame_length_ms = frame_length_ms
        self.ref_level_db = ref_level_db
        self.num_freq = num_freq
        self.power = power
        self.preemphasis = preemphasis
        self.griffin_lim_iters = griffin_lim_iters
        self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
        if preemphasis == 0:
            print(" | > Preemphasis is deactive.")

    def save_wav(self, wav, path):
        wav *= 32767 / max(0.01, np.max(np.abs(wav)))
        librosa.output.write_wav(path, wav.astype(np.int16), self.sample_rate)

    def _linear_to_mel(self, spectrogram):
        global _mel_basis
        if _mel_basis is None:
            _mel_basis = self._build_mel_basis()
        return np.dot(_mel_basis, spectrogram)

    def _build_mel_basis(self, ):
        n_fft = (self.num_freq - 1) * 2
        return librosa.filters.mel(
            self.sample_rate, n_fft, n_mels=self.num_mels)

    def _normalize(self, S):
        return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)

    def _denormalize(self, S):
        return (np.clip(S, 0, 1) * -self.min_level_db) + self.min_level_db

    def _stft_parameters(self, ):
        n_fft = (self.num_freq - 1) * 2
        hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
        win_length = int(self.frame_length_ms / 1000.0 * self.sample_rate)
        print(" | > fft size: {}, hop length: {}, win length: {}".format(
            n_fft, hop_length, win_length))
        return n_fft, hop_length, win_length

    def _amp_to_db(self, x):
        min_level = np.exp(self.min_level_db / 20 * np.log(10))
        return 20 * np.log10(np.maximum(min_level, x))

    def _db_to_amp(self, x):
        return np.power(10.0, x * 0.05)

    def apply_preemphasis(self, x):
        if self.preemphasis == 0:
            raise RuntimeError(" !! Preemphasis is applied with factor 0.0. ")
        return signal.lfilter([1, -self.preemphasis], [1], x)

    def apply_inv_preemphasis(self, x):
        if self.preemphasis == 0:
            raise RuntimeError(" !! Preemphasis is applied with factor 0.0. ")
        return signal.lfilter([1], [1, -self.preemphasis], x)

    def spectrogram(self, y):
        if self.preemphasis != 0:
            D = self._stft(self.apply_preemphasis(y))
        else:
            D = self._stft(y)
        S = self._amp_to_db(np.abs(D)) - self.ref_level_db
        return self._normalize(S)

    def inv_spectrogram(self, spectrogram):
        '''Converts spectrogram to waveform using librosa'''
        S = self._denormalize(spectrogram)
        S = self._db_to_amp(S + self.ref_level_db)  # Convert back to linear
        # Reconstruct phase
        if self.preemphasis != 0:
            return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
        else:
            return self._griffin_lim(S**self.power)

    def _griffin_lim(self, S):
        angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
        S_complex = np.abs(S).astype(np.complex)
        y = self._istft(S_complex * angles)
        for i in range(self.griffin_lim_iters):
            angles = np.exp(1j * np.angle(self._stft(y)))
            y = self._istft(S_complex * angles)
        return y

    def melspectrogram(self, y):
        if self.preemphasis != 0:
            D = self._stft(self.apply_preemphasis(y))
        else:
            D = self._stft(y)
        S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db
        return self._normalize(S)

    def _stft(self, y):
        return librosa.stft(
            y=y, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length)

    def _istft(self, y):
        return librosa.istft(y, hop_length=self.hop_length, win_length=self.win_length)

    def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
        window_length = int(self.sample_rate * min_silence_sec)
        hop_length = int(window_length / 4)
        threshold = self._db_to_amp(threshold_db)
        for x in range(hop_length, len(wav) - window_length, hop_length):
            if np.max(wav[x:x + window_length]) < threshold:
                return x + hop_length
        return len(wav)