#!/usr/bin/env python
  
  # Copyright 2012  Johns Hopkins University (author: Daniel Povey)
  
  # This file is as ./gen_topo.py used to be (before we extended the transition-model
  # code to support having a different self-loop pdf-class).  It is included
  # here for baseline and testing purposes.
  
  
  # Generate a topology file.  This allows control of the number of states in the
  # non-silence HMMs, and in the silence HMMs.  This is a modified version of
  # 'utils/gen_topo.pl' that generates a different type of topology, one that we
  # believe should be useful in the 'chain' model.  Note: right now it doesn't
  # have any real options, and it treats silence and nonsilence the same.  The
  # intention is that you write different versions of this script, or add options,
  # if you experiment with it.
  
  from __future__ import print_function
  import argparse
  
  
  parser = argparse.ArgumentParser(description="Usage: steps/nnet3/chain/gen_topo.py "
                                               "<colon-separated-nonsilence-phones> <colon-separated-silence-phones>"
                                               "e.g.:  steps/nnet3/chain/gen_topo.pl 4:5:6:7:8:9:10 1:2:3
  ",
                                   epilog="See egs/swbd/s5c/local/chain/train_tdnn_a.sh for example of usage.");
  parser.add_argument("nonsilence_phones", type=str,
                      help="List of non-silence phones as integers, separated by colons, e.g. 4:5:6:7:8:9");
  parser.add_argument("silence_phones", type=str,
                      help="List of silence phones as integers, separated by colons, e.g. 1:2:3");
  
  args = parser.parse_args()
  
  silence_phones = [ int(x) for x in args.silence_phones.split(":") ]
  nonsilence_phones = [ int(x) for x in args.nonsilence_phones.split(":") ]
  all_phones = silence_phones +  nonsilence_phones
  
  print("<Topology>")
  print("<TopologyEntry>")
  print("<ForPhones>")
  print(" ".join([str(x) for x in all_phones]))
  print("</ForPhones>")
  # The next two lines may look like a bug, but they are as intended.  State 0 has
  # no self-loop, it happens exactly once.  And it can go either to state 1 (with
  # a self-loop) or to state 2, so we can have zero or more instances of state 1
  # following state 0.
  # We make the transition-probs 0.5 so they normalize, to keep the code happy.
  # In fact, we always set the transition probability scale to 0.0 in the 'chain'
  # code, so they are never used.
  print("<State> 0 <PdfClass> 0 <Transition> 1 0.5 <Transition> 2 0.5 </State>")
  print("<State> 1 <PdfClass> 1 <Transition> 1 0.5 <Transition> 2 0.5 </State>")
  print("<State> 2 </State>")
  print("</TopologyEntry>")
  print("</Topology>")