#!/usr/bin/python3
'''Copyright (c) 2018 Mozilla

   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions
   are met:

   - Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.

   - Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''

import math
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation, GaussianNoise
from tensorflow.compat.v1.keras.layers import CuDNNGRU
from tensorflow.keras import backend as K
from tensorflow.keras.constraints import Constraint
from tensorflow.keras.initializers import Initializer
from tensorflow.keras.callbacks import Callback
from mdense import MDense
import numpy as np
import h5py
import sys
from tf_funcs import *
from diffembed import diff_Embed
from parameters import set_parameter

frame_size = 160
pcm_bits = 8
embed_size = 128
pcm_levels = 2**pcm_bits

def interleave(p, samples):
    p2=tf.expand_dims(p, 3)
    nb_repeats = pcm_levels//(2*p.shape[2])
    p3 = tf.reshape(tf.repeat(tf.concat([1-p2, p2], 3), nb_repeats), (-1, samples, pcm_levels))
    return p3

def tree_to_pdf(p, samples):
    return interleave(p[:,:,1:2], samples) * interleave(p[:,:,2:4], samples) * interleave(p[:,:,4:8], samples) * interleave(p[:,:,8:16], samples) \
         * interleave(p[:,:,16:32], samples) * interleave(p[:,:,32:64], samples) * interleave(p[:,:,64:128], samples) * interleave(p[:,:,128:256], samples)

def tree_to_pdf_train(p):
    #FIXME: try not to hardcode the 2400 samples (15 frames * 160 samples/frame)
    return tree_to_pdf(p, 2400)

def tree_to_pdf_infer(p):
    return tree_to_pdf(p, 1)

def quant_regularizer(x):
    Q = 128
    Q_1 = 1./Q
    #return .01 * tf.reduce_mean(1 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x))))
    return .01 * tf.reduce_mean(K.sqrt(K.sqrt(1.0001 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x))))))

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

    def on_batch_end(self, batch, logs=None):
        #print("batch number", self.batch)
        self.batch += 1
        if self.quantize or (self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end:
            #print("constrain");
            layer = self.model.get_layer('gru_a')
            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)
            #print ("density = ", density)
            for k in range(nb):
                density = self.final_density[k]
                if self.batch < self.t_end and not self.quantize:
                    r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
                    density = 1 - (1-self.final_density[k])*(1 - r*r*r)
                A = p[:, k*N:(k+1)*N]
                A = A - np.diag(np.diag(A))
                #This is needed because of the CuDNNGRU strange weight ordering
                A = np.transpose(A, (1, 0))
                L=np.reshape(A, (N//4, 4, N//8, 8))
                S=np.sum(L*L, axis=-1)
                S=np.sum(S, axis=1)
                SS=np.sort(np.reshape(S, (-1,)))
                thresh = SS[round(N*N//32*(1-density))]
                mask = (S>=thresh).astype('float32')
                mask = np.repeat(mask, 4, axis=0)
                mask = np.repeat(mask, 8, axis=1)
                mask = np.minimum(1, mask + np.diag(np.ones((N,))))
                #This is needed because of the CuDNNGRU strange weight ordering
                mask = np.transpose(mask, (1, 0))
                p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask
                #print(thresh, np.mean(mask))
            if self.quantize and ((self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end):
                if self.batch < self.t_end:
                    threshold = .5*(self.batch - self.t_start)/(self.t_end - self.t_start)
                else:
                    threshold = .5
                quant = np.round(p*128.)
                res = p*128.-quant
                mask = (np.abs(res) <= threshold).astype('float32')
                p = mask/128.*quant + (1-mask)*p

            w[1] = p
            layer.set_weights(w)

class SparsifyGRUB(Callback):
    def __init__(self, t_start, t_end, interval, grua_units, density, quantize=False):
        super(SparsifyGRUB, self).__init__()
        self.batch = 0
        self.t_start = t_start
        self.t_end = t_end
        self.interval = interval
        self.final_density = density
        self.grua_units = grua_units
        self.quantize = quantize

    def on_batch_end(self, batch, logs=None):
        #print("batch number", self.batch)
        self.batch += 1
        if self.quantize or (self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end:
            #print("constrain");
            layer = self.model.get_layer('gru_b')
            w = layer.get_weights()
            p = w[0]
            N = p.shape[0]
            M = p.shape[1]//3
            for k in range(3):
                density = self.final_density[k]
                if self.batch < self.t_end and not self.quantize:
                    r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
                    density = 1 - (1-self.final_density[k])*(1 - r*r*r)
                A = p[:, k*M:(k+1)*M]
                #This is needed because of the CuDNNGRU strange weight ordering
                A = np.reshape(A, (M, N))
                A = np.transpose(A, (1, 0))
                N2 = self.grua_units
                A2 = A[:N2, :]
                L=np.reshape(A2, (N2//4, 4, M//8, 8))
                S=np.sum(L*L, axis=-1)
                S=np.sum(S, axis=1)
                SS=np.sort(np.reshape(S, (-1,)))
                thresh = SS[round(M*N2//32*(1-density))]
                mask = (S>=thresh).astype('float32')
                mask = np.repeat(mask, 4, axis=0)
                mask = np.repeat(mask, 8, axis=1)
                A = np.concatenate([A2*mask, A[N2:,:]], axis=0)
                #This is needed because of the CuDNNGRU strange weight ordering
                A = np.transpose(A, (1, 0))
                A = np.reshape(A, (N, M))
                p[:, k*M:(k+1)*M] = A
                #print(thresh, np.mean(mask))
            if self.quantize and ((self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end):
                if self.batch < self.t_end:
                    threshold = .5*(self.batch - self.t_start)/(self.t_end - self.t_start)
                else:
                    threshold = .5
                quant = np.round(p*128.)
                res = p*128.-quant
                mask = (np.abs(res) <= threshold).astype('float32')
                p = mask/128.*quant + (1-mask)*p

            w[0] = 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.astype("float32")

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

class WeightClip(Constraint):
    '''Clips the weights incident to each hidden unit to be inside a range
    '''
    def __init__(self, c=2):
        self.c = c

    def __call__(self, p):
        # Ensure that abs of adjacent weights don't sum to more than 127. Otherwise there's a risk of
        # saturation when implementing dot products with SSSE3 or AVX2.
        return self.c*p/tf.maximum(self.c, tf.repeat(tf.abs(p[:, 1::2])+tf.abs(p[:, 0::2]), 2, axis=1))
        #return K.clip(p, -self.c, self.c)

    def get_config(self):
        return {'name': self.__class__.__name__,
            'c': self.c}

constraint = WeightClip(0.992)

def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_size=128, training=False, adaptation=False, quantize=False, flag_e2e = False, cond_size=128, lpc_order=16, lpc_gamma=1., lookahead=2):
    pcm = Input(shape=(None, 1), batch_size=batch_size)
    dpcm = Input(shape=(None, 3), batch_size=batch_size)
    feat = Input(shape=(None, nb_used_features), batch_size=batch_size)
    pitch = Input(shape=(None, 1), batch_size=batch_size)
    dec_feat = Input(shape=(None, cond_size))
    dec_state1 = Input(shape=(rnn_units1,))
    dec_state2 = Input(shape=(rnn_units2,))

    padding = 'valid' if training else 'same'
    fconv1 = Conv1D(cond_size, 3, padding=padding, activation='tanh', name='feature_conv1')
    fconv2 = Conv1D(cond_size, 3, padding=padding, activation='tanh', name='feature_conv2')
    pembed = Embedding(256, 64, name='embed_pitch')
    cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])

    cfeat = fconv2(fconv1(cat_feat))

    fdense1 = Dense(cond_size, activation='tanh', name='feature_dense1')
    fdense2 = Dense(cond_size, activation='tanh', name='feature_dense2')

    if flag_e2e and quantize:
        fconv1.trainable = False
        fconv2.trainable = False
        fdense1.trainable = False
        fdense2.trainable = False

    cfeat = fdense2(fdense1(cfeat))

    error_calc = Lambda(lambda x: tf_l2u(x[0] - tf.roll(x[1],1,axis = 1)))
    if flag_e2e:
        lpcoeffs = diff_rc2lpc(name = "rc2lpc")(cfeat)
    else:
        lpcoeffs = Input(shape=(None, lpc_order), batch_size=batch_size)

    real_preds = diff_pred(name = "real_lpc2preds")([pcm,lpcoeffs])
    weighting = lpc_gamma ** np.arange(1, 17).astype('float32')
    weighted_lpcoeffs = Lambda(lambda x: x[0]*x[1])([lpcoeffs, weighting])
    tensor_preds = diff_pred(name = "lpc2preds")([pcm,weighted_lpcoeffs])
    past_errors = error_calc([pcm,tensor_preds])

    embed = diff_Embed(name='embed_sig',initializer = PCMInit())
    cpcm = Concatenate()([tf_l2u(pcm),tf_l2u(tensor_preds),past_errors])
    cpcm = GaussianNoise(.3)(cpcm)
    cpcm = Reshape((-1, embed_size*3))(embed(cpcm))
    cpcm_decoder = Reshape((-1, embed_size*3))(embed(dpcm))


    rep = Lambda(lambda x: K.repeat_elements(x, frame_size, 1))

    quant = quant_regularizer if quantize else None

    if training:
        rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a', stateful=True,
              recurrent_constraint = constraint, recurrent_regularizer=quant)
        rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b', stateful=True,
               kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
    else:
        rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a', stateful=True,
              recurrent_constraint = constraint, recurrent_regularizer=quant)
        rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b', stateful=True,
               kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)

    rnn_in = Concatenate()([cpcm, rep(cfeat)])
    md = MDense(pcm_levels, activation='sigmoid', name='dual_fc')
    gru_out1, _ = rnn(rnn_in)
    gru_out1 = GaussianNoise(.005)(gru_out1)
    gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)]))
    ulaw_prob = Lambda(tree_to_pdf_train)(md(gru_out2))

    if adaptation:
        rnn.trainable=False
        rnn2.trainable=False
        md.trainable=False
        embed.Trainable=False

    m_out = Concatenate(name='pdf')([tensor_preds,real_preds,ulaw_prob])
    if not flag_e2e:
        model = Model([pcm, feat, pitch, lpcoeffs], m_out)
    else:
        model = Model([pcm, feat, pitch], [m_out, cfeat])
    model.rnn_units1 = rnn_units1
    model.rnn_units2 = rnn_units2
    model.nb_used_features = nb_used_features
    model.frame_size = frame_size

    if not flag_e2e:
        encoder = Model([feat, pitch], cfeat)
        dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat])
    else:
        encoder = Model([feat, pitch], [cfeat,lpcoeffs])
        dec_rnn_in = Concatenate()([cpcm_decoder, 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 = Lambda(tree_to_pdf_infer)(md(dec_gru_out2))

    if flag_e2e:
        decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
    else:
        decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])

    # add parameters to model
    set_parameter(model, 'lpc_gamma', lpc_gamma, dtype='float64')
    set_parameter(model, 'flag_e2e', flag_e2e, dtype='bool')
    set_parameter(model, 'lookahead', lookahead, dtype='int32')

    return model, encoder, decoder