#!/usr/bin/python3

import math
from keras.models import Model
from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from keras.callbacks import Callback
from mdense import MDense
import numpy as np
import h5py
import sys

rnn_units1=256
rnn_units2=32
pcm_bits = 8
embed_size = 128
pcm_levels = 2**pcm_bits
nb_used_features = 38

class Sparsify(Callback):
    def __init__(self, t_start, t_end, interval, density):
        super(Sparsify, self).__init__()
        self.batch = 0
        self.t_start = t_start
        self.t_end = t_end
        self.interval = interval
        self.final_density = density

    def on_batch_end(self, batch, logs=None):
        #print("batch number", self.batch)
        self.batch += 1
        if self.batch < self.t_start or ((self.batch-self.t_start) % self.interval != 0 and self.batch < self.t_end):
            #print("don't constrain");
            pass
        else:
            #print("constrain");
            layer = self.model.get_layer('cu_dnngru_1')
            w = layer.get_weights()
            p = w[1]
            nb = p.shape[1]//p.shape[0]
            N = p.shape[0]
            #print("nb = ", nb, ", N = ", N);
            #print(p.shape)
            density = self.final_density
            if self.batch < self.t_end:
                r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
                density = 1 - (1-self.final_density)*(1 - r*r*r)
            #print ("density = ", density)
            for k in range(nb):
                A = p[:, k*N:(k+1)*N]
                L=np.reshape(A, (N, N//16, 16))
                S=np.sum(L*L, axis=-1)
                SS=np.sort(np.reshape(S, (-1,)))
                thresh = SS[round(N*N//16*(1-density))]
                mask = (S>=thresh).astype('float32');
                mask = np.repeat(mask, 16, axis=1)
                p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask
                #print(thresh, np.mean(mask))
            w[1] = p
            layer.set_weights(w)
            

class PCMInit(Initializer):
    def __init__(self, gain=.1, seed=None):
        self.gain = gain
        self.seed = seed

    def __call__(self, shape, dtype=None):
        num_rows = 1
        for dim in shape[:-1]:
            num_rows *= dim
        num_cols = shape[-1]
        flat_shape = (num_rows, num_cols)
        if self.seed is not None:
            np.random.seed(self.seed)
        a = np.random.uniform(-1.7321, 1.7321, flat_shape)
        #a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows
        #a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]
        a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))
        return self.gain * a

    def get_config(self):
        return {
            'gain': self.gain,
            'seed': self.seed
        }

def new_wavernn_model():
    pcm = Input(shape=(None, 2))
    exc = Input(shape=(None, 1))
    feat = Input(shape=(None, nb_used_features))
    pitch = Input(shape=(None, 1))
    dec_feat = Input(shape=(None, 128))
    dec_state1 = Input(shape=(rnn_units1,))
    dec_state2 = Input(shape=(rnn_units2,))

    fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
    fconv2 = Conv1D(102, 3, padding='same', activation='tanh')

    embed = Embedding(256, embed_size, embeddings_initializer=PCMInit())
    cpcm = Reshape((-1, embed_size*2))(embed(pcm))
    embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit())
    cexc = Reshape((-1, embed_size))(embed2(exc))

    pembed = Embedding(256, 64)
    cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
    
    cfeat = fconv2(fconv1(cat_feat))

    fdense1 = Dense(128, activation='tanh')
    fdense2 = Dense(128, activation='tanh')

    cfeat = Add()([cfeat, cat_feat])
    cfeat = fdense2(fdense1(cfeat))
    
    rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))

    rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True)
    rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True)
    rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)])
    md = MDense(pcm_levels, activation='softmax')
    gru_out1, _ = rnn(rnn_in)
    gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)]))
    ulaw_prob = md(gru_out2)
    
    model = Model([pcm, exc, feat, pitch], ulaw_prob)
    encoder = Model([feat, pitch], cfeat)
    
    dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat])
    dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1)
    dec_gru_out2, state2 = rnn2(Concatenate()([dec_gru_out1, dec_feat]), initial_state=dec_state2)
    dec_ulaw_prob = md(dec_gru_out2)

    decoder = Model([pcm, exc, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
    return model, encoder, decoder