text: #!/usr/bin/python3
  '''Copyright (c) 2017-2018 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
     PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
     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 lpcnet
  import sys
  import numpy as np
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding
  from ulaw import ulaw2lin, lin2ulaw
  from mdense import MDense
  from diffembed import diff_Embed
  import h5py
  import re
  
  # Flag for dumping e2e (differentiable lpc) network weights
  flag_e2e = False
  
  max_rnn_neurons = 1
  max_conv_inputs = 1
  max_mdense_tmp = 1
  
  def printVector(f, vector, name, dtype='float', dotp=False):
      if dotp:
          vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
          vector = vector.transpose((2, 0, 3, 1))
      v = np.reshape(vector, (-1));
      #print('static const float ', name, '[', len(v), '] = \n', file=f)
      f.write('static const {} {}[{}] = {{\n   '.format(dtype, name, len(v)))
      for i in range(0, len(v)):
          f.write('{}'.format(v[i]))
          if (i!=len(v)-1):
              f.write(',')
          else:
              break;
          if (i%8==7):
              f.write("\n   ")
          else:
              f.write(" ")
      #print(v, file=f)
      f.write('\n};\n\n')
      return;
  
  def printSparseVector(f, A, name, have_diag=True):
      N = A.shape[0]
      M = A.shape[1]
      W = np.zeros((0,), dtype='int')
      W0 = np.zeros((0,))
      if have_diag:
          diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
          A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
          A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
          A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
          printVector(f, diag, name + '_diag')
      AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
      idx = np.zeros((0,), dtype='int')
      for i in range(M//8):
          pos = idx.shape[0]
          idx = np.append(idx, -1)
          nb_nonzero = 0
          for j in range(N//4):
              block = A[j*4:(j+1)*4, i*8:(i+1)*8]
              qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
              if np.sum(np.abs(block)) > 1e-10:
                  nb_nonzero = nb_nonzero + 1
                  idx = np.append(idx, j*4)
                  vblock = qblock.transpose((1,0)).reshape((-1,))
                  W0 = np.concatenate([W0, block.reshape((-1,))])
                  W = np.concatenate([W, vblock])
          idx[pos] = nb_nonzero
      f.write('#ifdef DOT_PROD\n')
      printVector(f, W, name, dtype='qweight')
      f.write('#else /*DOT_PROD*/\n')
      printVector(f, W0, name, dtype='qweight')
      f.write('#endif /*DOT_PROD*/\n')
      #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
      printVector(f, idx, name + '_idx', dtype='int')
      return AQ
  
  def dump_layer_ignore(self, f, hf):
      print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
      return False
  Layer.dump_layer = dump_layer_ignore
  
  def dump_sparse_gru(self, f, hf):
      global max_rnn_neurons
      name = 'sparse_' + self.name
      print("printing layer " + name + " of type sparse " + self.__class__.__name__)
      weights = self.get_weights()
      qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
      printVector(f, weights[-1], name + '_bias')
      subias = weights[-1].copy()
      subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
      printVector(f, subias, name + '_subias')
      if hasattr(self, 'activation'):
          activation = self.activation.__name__.upper()
      else:
          activation = 'TANH'
      if hasattr(self, 'reset_after') and not self.reset_after:
          reset_after = 0
      else:
          reset_after = 1
      neurons = weights[0].shape[1]//3
      max_rnn_neurons = max(max_rnn_neurons, neurons)
      f.write('const SparseGRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_recurrent_weights_diag,\n   {}_recurrent_weights,\n   {}_recurrent_weights_idx,\n   {}, ACTIVATION_{}, {}\n}};\n\n'
              .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
      hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
      hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
      hf.write('extern const SparseGRULayer {};\n\n'.format(name));
      return True
  
  def dump_grub(self, f, hf, gru_a_size):
      global max_rnn_neurons
      name = self.name
      print("printing layer " + name + " of type " + self.__class__.__name__)
      weights = self.get_weights()
      qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False)
  
      f.write('#ifdef DOT_PROD\n')
      qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
      printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
      f.write('#else /*DOT_PROD*/\n')
      printVector(f, weights[1], name + '_recurrent_weights')
      f.write('#endif /*DOT_PROD*/\n')
  
      printVector(f, weights[-1], name + '_bias')
      subias = weights[-1].copy()
      subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
      subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
      printVector(f, subias, name + '_subias')
      if hasattr(self, 'activation'):
          activation = self.activation.__name__.upper()
      else:
          activation = 'TANH'
      if hasattr(self, 'reset_after') and not self.reset_after:
          reset_after = 0
      else:
          reset_after = 1
      neurons = weights[0].shape[1]//3
      max_rnn_neurons = max(max_rnn_neurons, neurons)
      f.write('const GRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_weights,\n   {}_weights_idx,\n   {}_recurrent_weights,\n   {}, {}, ACTIVATION_{}, {}\n}};\n\n'
              .format(name, name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after))
      hf.write('extern const GRULayer {};\n\n'.format(name));
      return True
  
  def dump_gru_layer_dummy(self, f, hf):
      name = self.name
      weights = self.get_weights()
      hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
      hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
      return True;
  
  GRU.dump_layer = dump_gru_layer_dummy
  
  def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
      printVector(f, weights, name + '_weights')
      printVector(f, bias, name + '_bias')
      f.write('const DenseLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, ACTIVATION_{}\n}};\n\n'
              .format(name, name, name, weights.shape[0], weights.shape[1], activation))
      hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
      hf.write('extern const DenseLayer {};\n\n'.format(name));
  
  def dump_dense_layer(self, f, hf):
      name = self.name
      print("printing layer " + name + " of type " + self.__class__.__name__)
      weights = self.get_weights()
      activation = self.activation.__name__.upper()
      dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
      return False
  
  Dense.dump_layer = dump_dense_layer
  
  def dump_mdense_layer(self, f, hf):
      global max_mdense_tmp
      name = self.name
      print("printing layer " + name + " of type " + self.__class__.__name__)
      weights = self.get_weights()
      printVector(f, np.transpose(weights[0], (0, 2, 1)), name + '_weights')
      printVector(f, np.transpose(weights[1], (1, 0)), name + '_bias')
      printVector(f, np.transpose(weights[2], (1, 0)), name + '_factor')
      activation = self.activation.__name__.upper()
      max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2])
      f.write('const MDenseLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}_factor,\n   {}, {}, {}, ACTIVATION_{}\n}};\n\n'
              .format(name, name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
      hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))
      hf.write('extern const MDenseLayer {};\n\n'.format(name));
      return False
  MDense.dump_layer = dump_mdense_layer
  
  def dump_conv1d_layer(self, f, hf):
      global max_conv_inputs
      name = self.name
      print("printing layer " + name + " of type " + self.__class__.__name__)
      weights = self.get_weights()
      printVector(f, weights[0], name + '_weights')
      printVector(f, weights[-1], name + '_bias')
      activation = self.activation.__name__.upper()
      max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0])
      f.write('const Conv1DLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, {}, ACTIVATION_{}\n}};\n\n'
              .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
      hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
      hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
      hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
      hf.write('extern const Conv1DLayer {};\n\n'.format(name));
      return True
  Conv1D.dump_layer = dump_conv1d_layer
  
  
  def dump_embedding_layer_impl(name, weights, f, hf):
      printVector(f, weights, name + '_weights')
      f.write('const EmbeddingLayer {} = {{\n   {}_weights,\n   {}, {}\n}};\n\n'
              .format(name, name, weights.shape[0], weights.shape[1]))
      hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
      hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
  
  def dump_embedding_layer(self, f, hf):
      name = self.name
      print("printing layer " + name + " of type " + self.__class__.__name__)
      weights = self.get_weights()[0]
      dump_embedding_layer_impl(name, weights, f, hf)
      return False
  Embedding.dump_layer = dump_embedding_layer
  diff_Embed.dump_layer = dump_embedding_layer
  
  filename = sys.argv[1]
  with h5py.File(filename, "r") as f:
      units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
      units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
      cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape)
      e2e = 'rc2lpc' in f['model_weights']
  
  model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = e2e, cond_size=cond_size)
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
  #model.summary()
  
  model.load_weights(filename, by_name=True)
  
  if len(sys.argv) > 2:
      cfile = sys.argv[2];
      hfile = sys.argv[3];
  else:
      cfile = 'nnet_data.c'
      hfile = 'nnet_data.h'
  
  
  f = open(cfile, 'w')
  hf = open(hfile, 'w')
  
  
  f.write('/*This file is automatically generated from a Keras model*/\n')
  f.write('/*based on model {}*/\n\n'.format(sys.argv[1]))
  f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "{}"\n\n'.format(hfile))
  
  hf.write('/*This file is automatically generated from a Keras model*/\n\n')
  hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "nnet.h"\n\n')
  
  if e2e:
      hf.write('/* This is an end-to-end model */\n')
      hf.write('#define END2END\n\n')
  else:
      hf.write('/* This is *not* an end-to-end model */\n')
      hf.write('/* #define END2END */\n\n')
  
  embed_size = lpcnet.embed_size
  
  E = model.get_layer('embed_sig').get_weights()[0]
  W = model.get_layer('gru_a').get_weights()[0][:embed_size,:]
  dump_embedding_layer_impl('gru_a_embed_sig', np.dot(E, W), f, hf)
  W = model.get_layer('gru_a').get_weights()[0][embed_size:2*embed_size,:]
  dump_embedding_layer_impl('gru_a_embed_pred', np.dot(E, W), f, hf)
  W = model.get_layer('gru_a').get_weights()[0][2*embed_size:3*embed_size,:]
  dump_embedding_layer_impl('gru_a_embed_exc', np.dot(E, W), f, hf)
  W = model.get_layer('gru_a').get_weights()[0][3*embed_size:,:]
  #FIXME: dump only half the biases
  b = model.get_layer('gru_a').get_weights()[2]
  dump_dense_layer_impl('gru_a_dense_feature', W, b, 'LINEAR', f, hf)
  
  W = model.get_layer('gru_b').get_weights()[0][model.rnn_units1:,:]
  b = model.get_layer('gru_b').get_weights()[2]
  # Set biases to zero because they'll be included in the GRU input part
  # (we need regular and SU biases)
  dump_dense_layer_impl('gru_b_dense_feature', W, 0*b, 'LINEAR', f, hf)
  dump_grub(model.get_layer('gru_b'), f, hf, model.rnn_units1)
  
  layer_list = []
  for i, layer in enumerate(model.layers):
      if layer.dump_layer(f, hf):
          layer_list.append(layer.name)
  
  dump_sparse_gru(model.get_layer('gru_a'), f, hf)
  
  hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
  hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
  hf.write('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp))
  
  
  hf.write('typedef struct {\n')
  for i, name in enumerate(layer_list):
      hf.write('  float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper())) 
  hf.write('} NNetState;\n')
  
  hf.write('\n\n#endif\n')
  
  f.close()
  hf.close()
  