/*

EGYPT Toolkit for Statistical Machine Translation
Written by Yaser Al-Onaizan, Jan Curin, Michael Jahr, Kevin Knight, John Lafferty, Dan Melamed, David Purdy, Franz Och, Noah Smith, and David Yarowsky.

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, 
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, 
USA.

*/

#include <strstream>
#include "getSentence.h"
#include "TTables.h"
#include "model1.h"
#include "model2.h"
#include "model3.h"
#include "hmm.h"
#include "file_spec.h"
#include "defs.h"
#include "vocab.h"
#include "Perplexity.h"
#include "Dictionary.h"
#include "utility.h" 
#include "Parameter.h"
#include "myassert.h"
#include "D4Tables.h"
#include "D5Tables.h"
#include "transpair_model4.h"
#include "transpair_model5.h"

#define ITER_M2 0
#define ITER_MH 5

GLOBAL_PARAMETER3(int,Model1_Iterations,"Model1_Iterations","NO. ITERATIONS MODEL 1","m1","number of iterations for Model 1",PARLEV_ITER,5);
GLOBAL_PARAMETER3(int,Model2_Iterations,"Model2_Iterations","NO. ITERATIONS MODEL 2","m2","number of iterations for Model 2",PARLEV_ITER,ITER_M2);
GLOBAL_PARAMETER3(int,HMM_Iterations,"HMM_Iterations","mh","number of iterations for HMM alignment model","mh",              PARLEV_ITER,ITER_MH);
GLOBAL_PARAMETER3(int,Model3_Iterations,"Model3_Iterations","NO. ITERATIONS MODEL 3","m3","number of iterations for Model 3",PARLEV_ITER,5);
GLOBAL_PARAMETER3(int,Model4_Iterations,"Model4_Iterations","NO. ITERATIONS MODEL 4","m4","number of iterations for Model 4",PARLEV_ITER,5);
GLOBAL_PARAMETER3(int,Model5_Iterations,"Model5_Iterations","NO. ITERATIONS MODEL 5","m5","number of iterations for Model 5",PARLEV_ITER,0);
GLOBAL_PARAMETER3(int,Model6_Iterations,"Model6_Iterations","NO. ITERATIONS MODEL 6","m6","number of iterations for Model 6",PARLEV_ITER,0);


GLOBAL_PARAMETER(float, PROB_SMOOTH,"probSmooth","probability smoothing (floor) value ",PARLEV_OPTHEUR,1e-7);
GLOBAL_PARAMETER(float, MINCOUNTINCREASE,"minCountIncrease","minimal count increase",PARLEV_OPTHEUR,1e-7);

GLOBAL_PARAMETER2(int,Transfer_Dump_Freq,"TRANSFER DUMP FREQUENCY","t2to3","output: dump of transfer from Model 2 to 3",PARLEV_OUTPUT,0);
GLOBAL_PARAMETER2(bool,Verbose,"verbose","v","0: not verbose; 1: verbose",PARLEV_OUTPUT,0);
GLOBAL_PARAMETER(bool,Log,"log","0: no logfile; 1: logfile",PARLEV_OUTPUT,0);


GLOBAL_PARAMETER(double,P0,"p0","fixed value for parameter p_0 in IBM-3/4 (if negative then it is determined in training)",PARLEV_EM,-1.0);
GLOBAL_PARAMETER(double,M5P0,"m5p0","fixed value for parameter p_0 in IBM-5 (if negative then it is determined in training)",PARLEV_EM,-1.0);
GLOBAL_PARAMETER3(bool,Peg,"pegging","p","DO PEGGING? (Y/N)","0: no pegging; 1: do pegging",PARLEV_EM,0);

GLOBAL_PARAMETER(short,OldADBACKOFF,"adbackoff","",-1,0);
GLOBAL_PARAMETER2(unsigned int,MAX_SENTENCE_LENGTH,"ml","MAX SENTENCE LENGTH","maximum sentence length",0,MAX_SENTENCE_LENGTH_ALLOWED);


GLOBAL_PARAMETER(short, DeficientDistortionForEmptyWord,"DeficientDistortionForEmptyWord","0: IBM-3/IBM-4 as described in (Brown et al. 1993); 1: distortion model of empty word is deficient; 2: distoriton model of empty word is deficient (differently); setting this parameter also helps to avoid that during IBM-3 and IBM-4 training too many words are aligned with the empty word",PARLEV_MODELS,0);
short OutputInAachenFormat=0;
bool Transfer=TRANSFER;
bool Transfer2to3=0;
short NoEmptyWord=0;
bool FEWDUMPS=0;
GLOBAL_PARAMETER(bool,ONLYALDUMPS,"ONLYALDUMPS","1: do not write any files",PARLEV_OUTPUT,0);
GLOBAL_PARAMETER(short,CompactAlignmentFormat,"CompactAlignmentFormat","0: detailled alignment format, 1: compact alignment format ",PARLEV_OUTPUT,0);
GLOBAL_PARAMETER2(bool,NODUMPS,"NODUMPS","NO FILE DUMPS? (Y/N)","1: do not write any files",PARLEV_OUTPUT,0);

GLOBAL_PARAMETER(WordIndex,MAX_FERTILITY,"MAX_FERTILITY","maximal fertility for fertility models",PARLEV_EM,10);

Vector<map< pair<int,int>,char > > ReferenceAlignment;


bool useDict = false  ;
string CoocurrenceFile;
string Prefix, LogFilename, OPath, Usage, 
  SourceVocabFilename, TargetVocabFilename, CorpusFilename, 
  TestCorpusFilename, t_Filename, a_Filename, p0_Filename, d_Filename, 
  n_Filename, dictionary_Filename;

ofstream logmsg ;
const string str2Num(int n){
  string number = "";
  do{
    number.insert((size_t)0, 1, (char)(n % 10 + '0'));
  } while((n /= 10) > 0);
  return(number) ;
}


double LAMBDA=1.09;
sentenceHandler *testCorpus=0,*corpus=0;
Perplexity trainPerp, testPerp, trainViterbiPerp, testViterbiPerp ;

string ReadTablePrefix;


void printGIZAPars(ostream&out)
{
  out << "general parameters:\n"
         "-------------------\n";
  printPars(out,getGlobalParSet(),0);
  out << '\n';

  out << "No. of iterations:\n-"
         "------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_ITER);
  out << '\n';

  out << "parameter for various heuristics in GIZA++ for efficient training:\n"
         "------------------------------------------------------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_OPTHEUR);
  out << '\n';

  out << "parameters for describing the type and amount of output:\n"
         "-----------------------------------------------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_OUTPUT);
  out << '\n';

  out << "parameters describing input files:\n"
         "----------------------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_INPUT);
  out << '\n';

  out << "smoothing parameters:\n"
         "---------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_SMOOTH);
  out << '\n';

  out << "parameters modifying the models:\n"
         "--------------------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_MODELS);
  out << '\n';

  out << "parameters modifying the EM-algorithm:\n"
         "--------------------------------------\n";
  printPars(out,getGlobalParSet(),PARLEV_EM);
  out << '\n';
}

const char*stripPath(const char*fullpath)
  // strip the path info from the file name 
{
  const char *ptr = fullpath + strlen(fullpath) - 1 ;
  while(ptr && ptr > fullpath && *ptr != '/'){ptr--;}
  if( *ptr=='/' )
    return(ptr+1);
  else
    return ptr;
}


void printDecoderConfigFile()
{
  string decoder_config_file = Prefix + ".Decoder.config" ;
  cerr << "writing decoder configuration file to " <<  decoder_config_file.c_str() <<'\n';
  ofstream decoder(decoder_config_file.c_str());
  if(!decoder){
    cerr << "\nCannot write to " << decoder_config_file <<'\n';
    exit(1);
  }
  decoder << "# Template for Configuration File for the Rewrite Decoder\n# Syntax:\n" 
	  << "#         <Variable> = <value>\n#         '#' is the comment character\n"
	  << "#================================================================\n"
	  << "#================================================================\n"
	  << "# LANGUAGE MODEL FILE\n# The full path and file name of the language model file:\n";
  decoder << "LanguageModelFile =\n";
  decoder << "#================================================================\n"
	  << "#================================================================\n"
	  << "# TRANSLATION MODEL FILES\n# The directory where the translation model tables as created\n"
	  << "# by Giza are located:\n#\n"
	  << "# Notes: - All translation model \"source\" files are assumed to be in\n"
	  << "#          TM_RawDataDir, the binaries will be put in TM_BinDataDir\n"
	  << "#\n#        - Attention: RELATIVE PATH NAMES DO NOT WORK!!!\n"
	  << "#\n#        - Absolute paths (file name starts with /) will override\n"
	  << "#          the default directory.\n\n";
  // strip file prefix info and leave only the path name in Prefix
  string path = Prefix.substr(0, Prefix.find_last_of("/")+1);
  if( path=="" )
    path=".";
  decoder << "TM_RawDataDir = " << path << '\n';
  decoder << "TM_BinDataDir = " << path << '\n' << '\n';
  decoder << "# file names of the TM tables\n# Notes:\n"
	  << "# 1. TTable and InversTTable are expected to use word IDs not\n"
	  << "#    strings (Giza produces both, whereby the *.actual.* files\n"
	  << "#    use strings and are THE WRONG CHOICE.\n"
	  << "# 2. FZeroWords, on the other hand, is a simple list of strings\n"
	  << "#    with one word per line. This file is typically edited\n"
	  << "#    manually. Hoeever, this one listed here is generated by GIZA\n\n";
  
  int lastmodel;
  if (Model5_Iterations>0)
    lastmodel = 5 ;
  else if (Model4_Iterations>0)
    lastmodel = 4 ;
  else if (Model3_Iterations>0)
    lastmodel = 3 ;
  else if (Model2_Iterations>0)
    lastmodel = 2 ;
  else lastmodel = 1 ;
  string lastModelName = str2Num(lastmodel);
  string p=Prefix + ".t" + /*lastModelName*/"3" +".final";
  decoder << "TTable = " << stripPath(p.c_str()) << '\n';
  p = Prefix + ".ti.final" ;
  decoder << "InverseTTable = " << stripPath(p.c_str()) << '\n';
  p=Prefix + ".n" + /*lastModelName*/"3" + ".final";
  decoder << "NTable = " << stripPath(p.c_str())  << '\n';
  p=Prefix + ".d" + /*lastModelName*/"3" + ".final";
  decoder << "D3Table = " << stripPath(p.c_str())  << '\n';
  p=Prefix + ".D4.final";
  decoder << "D4Table = " << stripPath(p.c_str()) << '\n';
  p=Prefix + ".p0_"+ /*lastModelName*/"3" + ".final";
  decoder << "PZero = " << stripPath(p.c_str()) << '\n';
  decoder << "Source.vcb = " << SourceVocabFilename << '\n';
  decoder << "Target.vcb = " << TargetVocabFilename << '\n';
  //  decoder << "Source.classes = " << SourceVocabFilename + ".classes" << '\n';
  //  decoder << "Target.classes = " << TargetVocabFilename + ".classes" <<'\n';
  decoder << "Source.classes = " << SourceVocabFilename+".classes" << '\n';
  decoder << "Target.classes = " << TargetVocabFilename + ".classes" <<'\n';
  p=Prefix + ".fe0_"+ /*lastModelName*/"3" + ".final";
  decoder << "FZeroWords       = " <<stripPath(p.c_str()) << '\n' ;

  /*  decoder << "# Translation Parameters\n"
      << "# Note: TranslationModel and LanguageModelMode must have NUMBERS as\n"
      << "# values, not words\n"
      << "# CORRECT: LanguageModelMode = 2\n"
      << "# WRONG:   LanguageModelMode = bigrams # WRONG, WRONG, WRONG!!!\n";
      decoder << "TMWeight          = 0.6 # weight of TM for calculating alignment probability\n";
      decoder << "TranslationModel  = "<<lastmodel<<"   # which model to use (3 or 4)\n";
      decoder << "LanguageModelMode = 2   # (2 (bigrams) or 3 (trigrams)\n\n";
      decoder << "# Output Options\n"
      << "TellWhatYouAreDoing = TRUE # print diagnostic messages to stderr\n"
      << "PrintOriginal       = TRUE # repeat original sentence in the output\n"
      << "TopTranslations     = 3    # number of n best translations to be returned\n"
      << "PrintProbabilities  = TRUE # give the probabilities for the translations\n\n";
      
      decoder << "# LOGGING OPTIONS\n"
      << "LogFile = - # empty means: no log, dash means: STDOUT\n"
      << "LogLM = true # log language model lookups\n"
      << "LogTM = true # log translation model lookups\n";
      */
}


void printAllTables(vcbList& eTrainVcbList, vcbList& eTestVcbList,
		    vcbList& fTrainVcbList, vcbList& fTestVcbList, model1& m1)
{
  cerr << "writing Final tables to Disk \n";
  string t_inv_file = Prefix + ".ti.final" ;
  if( !FEWDUMPS)
    m1.getTTable().printProbTableInverse(t_inv_file.c_str(), m1.getEnglishVocabList(), 
					 m1.getFrenchVocabList(), 
					 m1.getETotalWCount(), 
					 m1.getFTotalWCount());
  t_inv_file = Prefix + ".actual.ti.final" ;
  if( !FEWDUMPS )
    m1.getTTable().printProbTableInverse(t_inv_file.c_str(), 
					 eTrainVcbList.getVocabList(), 
					 fTrainVcbList.getVocabList(), 
					 m1.getETotalWCount(), 
					 m1.getFTotalWCount(), true);
  
  string perp_filename = Prefix + ".perp" ;
  ofstream of_perp(perp_filename.c_str());
  
  cout << "Writing PERPLEXITY report to: " << perp_filename << '\n';
  if(!of_perp){
    cerr << "\nERROR: Cannot write to " << perp_filename <<'\n';
    exit(1);
  }
  
  if (testCorpus)
    generatePerplexityReport(trainPerp, testPerp, trainViterbiPerp, 
			     testViterbiPerp, of_perp, (*corpus).getTotalNoPairs1(), 
			     (*testCorpus).getTotalNoPairs1(),
			     true);
  else 
    generatePerplexityReport(trainPerp, testPerp, trainViterbiPerp, testViterbiPerp, 
			     of_perp, (*corpus).getTotalNoPairs1(), 0, true);
  
  string eTrainVcbFile = Prefix + ".trn.src.vcb" ;
  ofstream of_eTrainVcb(eTrainVcbFile.c_str());
  cout << "Writing source vocabulary list to : " << eTrainVcbFile << '\n';
  if(!of_eTrainVcb){
    cerr << "\nERROR: Cannot write to " << eTrainVcbFile <<'\n';
    exit(1);
  }
  eTrainVcbList.printVocabList(of_eTrainVcb) ;
  
  string fTrainVcbFile = Prefix + ".trn.trg.vcb" ;
  ofstream of_fTrainVcb(fTrainVcbFile.c_str());
  cout << "Writing source vocabulary list to : " << fTrainVcbFile << '\n';
  if(!of_fTrainVcb){
    cerr << "\nERROR: Cannot write to " << fTrainVcbFile <<'\n';
    exit(1);
  }
  fTrainVcbList.printVocabList(of_fTrainVcb) ;
  
  //print test vocabulary list 
  
  string eTestVcbFile = Prefix + ".tst.src.vcb" ;
  ofstream of_eTestVcb(eTestVcbFile.c_str());
  cout << "Writing source vocabulary list to : " << eTestVcbFile << '\n';
  if(!of_eTestVcb){
    cerr << "\nERROR: Cannot write to " << eTestVcbFile <<'\n';
    exit(1);
  }
  eTestVcbList.printVocabList(of_eTestVcb) ;
  
  string fTestVcbFile = Prefix + ".tst.trg.vcb" ;
  ofstream of_fTestVcb(fTestVcbFile.c_str());
  cout << "Writing source vocabulary list to : " << fTestVcbFile << '\n';
  if(!of_fTestVcb){
    cerr << "\nERROR: Cannot write to " << fTestVcbFile <<'\n';
    exit(1);
  }
  fTestVcbList.printVocabList(of_fTestVcb) ;
  printDecoderConfigFile();
  if (testCorpus)
    printOverlapReport(m1.getTTable(), *testCorpus, eTrainVcbList, 
		       fTrainVcbList, eTestVcbList, fTestVcbList);
  
}

bool readNextSent(istream&is,map< pair<int,int>,char >&s,int&number)
{
  string x;
  if( !(is >> x) ) return 0;
  if( x=="SENT:" ) is >> x;
  int n=atoi(x.c_str());
  if( number==-1 )
    number=n;
  else
    if( number!=n )
      {
	cerr << "ERROR: readNextSent: DIFFERENT NUMBERS: " << number << " " << n << '\n';
	return 0;
      }
  int nS,nP,nO;
  nS=nP=nO=0;
  while( is >> x )
    {
      if( x=="SENT:" )
	return 1;
      int n1,n2;
      is >> n1 >> n2;
      map< pair<int,int>,char >::const_iterator i=s.find(pair<int,int>(n1,n2));
      if( i==s.end()||i->second=='P' )
	s[pair<int,int>(n1,n2)]=x[0];
      massert(x[0]=='S'||x[0]=='P');
      nS+= (x[0]=='S');
      nP+= (x[0]=='P');
      nO+= (!(x[0]=='S'||x[0]=='P'));
    }
  return 1;
}

bool emptySent(map< pair<int,int>,char >&x)
{
  x = map< pair<int,int>,char >();
  return 1;
}

void ReadAlignment(const string&x,Vector<map< pair<int,int>,char > >&a)
{
  ifstream infile(x.c_str());
  a.clear();
  map< pair<int,int>,char >sent;
  int number=0;
  while( emptySent(sent) && (readNextSent(infile,sent,number)) )
    {
      if( int(a.size())!=number )
	cerr << "ERROR: ReadAlignment: " << a.size() << " " << number << '\n';
      a.push_back(sent);
      number++;
    }
  cout << "Read: " << a.size() << " sentences in reference alignment." << '\n';
}
    

void initGlobals(void)
{
  NODUMPS = false ;
  Prefix = Get_File_Spec();
  LogFilename= Prefix + ".log";
  MAX_SENTENCE_LENGTH = MAX_SENTENCE_LENGTH_ALLOWED ;
}

void convert(const map< pair<int,int>,char >&reference,alignment&x)
{
  int l=x.get_l();
  int m=x.get_m();
  for(map< pair<int,int>,char >::const_iterator i=reference.begin();i!=reference.end();++i)
    {
      if( i->first.first+1>int(m) )
	{
	  cerr << "ERROR m to big: " << i->first.first << " " << i->first.second+1 << " " << l << " " << m << " is wrong.\n";
	  continue;
	}
      if( i->first.second+1>int(l) )
	{
	  cerr << "ERROR l to big: " << i->first.first << " " << i->first.second+1 << " " << l << " " << m << " is wrong.\n";
	  continue;
	}
      if( x(i->first.first+1)!=0 )
	cerr << "ERROR: position " << i->first.first+1 << " already set\n";
      x.set(i->first.first+1,i->first.second+1);
    }
}
double ErrorsInAlignment(const map< pair<int,int>,char >&reference,const Vector<WordIndex>&test,int l,int&missing,int&toomuch,int&eventsMissing,int&eventsToomuch,int pair_no)
{
  int err=0;
  for(unsigned int j=1;j<test.size();j++)
    {
      if( test[j]>0 )
	{
	  map< pair<int,int>,char >::const_iterator i=reference.find(make_pair(test[j]-1,j-1));
	  if( i==reference.end() )
	    {
	      toomuch++;
	      err++;
	    }
	  else
	    if( !(i->second=='S' || i->second=='P'))
	      cerr << "ERROR: wrong symbol in reference alignment '" << i->second << ' ' << int(i->second) << " no:" << pair_no<< "'\n";
	  eventsToomuch++;
	}
    }
  for(map< pair<int,int>,char >::const_iterator i=reference.begin();i!=reference.end();++i)
    {
      if( i->second=='S' )
	{
	  unsigned int J=i->first.second+1;
	  unsigned int I=i->first.first+1;
	  if( int(J)>=int(test.size())||int(I)>int(l)||int(J)<1||int(I)<1 )
	    cerr << "ERROR: alignment outside of range in reference alignment" << J << " " << test.size() << " (" << I << " " << l << ") no:" << pair_no << '\n';
	  else
	    {
	      if(test[J]!=I)
		{
		  missing++;
		  err++;
		}
	    }
	  eventsMissing++;
	}
    }
  if( Verbose )
    cout << err << " errors in sentence\n";
  if( eventsToomuch+eventsMissing )
    return (toomuch+missing)/(eventsToomuch+eventsMissing);
  else
    return 1.0;
}


vcbList *globeTrainVcbList,*globfTrainVcbList;

double StartTraining(int&result)
{ 
  double errors=0.0;
  vcbList eTrainVcbList, fTrainVcbList;
  globeTrainVcbList=&eTrainVcbList;
  globfTrainVcbList=&fTrainVcbList;


  string repFilename = Prefix + ".gizacfg" ;
  ofstream of2(repFilename.c_str());
  writeParameters(of2,getGlobalParSet(),-1) ;

  cout << "reading vocabulary files \n";
  eTrainVcbList.setName(SourceVocabFilename.c_str());
  fTrainVcbList.setName(TargetVocabFilename.c_str());
  eTrainVcbList.readVocabList();
  fTrainVcbList.readVocabList();
  cout << "Source vocabulary list has " << eTrainVcbList.uniqTokens() << " unique tokens \n";
  cout << "Target vocabulary list has " << fTrainVcbList.uniqTokens() << " unique tokens \n";
  
  vcbList eTestVcbList(eTrainVcbList) ;
  vcbList fTestVcbList(fTrainVcbList) ;
  
  corpus = new sentenceHandler(CorpusFilename.c_str(), &eTrainVcbList, &fTrainVcbList);

  if (TestCorpusFilename == "NONE")
    TestCorpusFilename = "";

  if (TestCorpusFilename != ""){
    cout << "Test corpus will be read from: " << TestCorpusFilename << '\n';
      testCorpus= new sentenceHandler(TestCorpusFilename.c_str(), 
						       &eTestVcbList, &fTestVcbList);
      cout << " Test total # sentence pairs : " <<(*testCorpus).getTotalNoPairs1()<<" weighted:"<<(*testCorpus).getTotalNoPairs2() <<'\n';

      cout << "Size of the source portion of test corpus: " << eTestVcbList.totalVocab() << " tokens\n";
      cout << "Size of the target portion of test corpus: " << fTestVcbList.totalVocab() << " tokens \n";
      cout << "In source portion of the test corpus, only " << eTestVcbList.uniqTokensInCorpus() << " unique tokens appeared\n";
      cout << "In target portion of the test corpus, only " << fTestVcbList.uniqTokensInCorpus() << " unique tokens appeared\n";
      cout << "ratio (target/source) : " << double(fTestVcbList.totalVocab()) /
	eTestVcbList.totalVocab() << '\n';
  }
  
  cout << " Train total # sentence pairs (weighted): " << corpus->getTotalNoPairs2() << '\n';
  cout << "Size of source portion of the training corpus: " << eTrainVcbList.totalVocab()-corpus->getTotalNoPairs2() << " tokens\n";
  cout << "Size of the target portion of the training corpus: " << fTrainVcbList.totalVocab() << " tokens \n";
  cout << "In source portion of the training corpus, only " << eTrainVcbList.uniqTokensInCorpus() << " unique tokens appeared\n";
  cout << "In target portion of the training corpus, only " << fTrainVcbList.uniqTokensInCorpus() << " unique tokens appeared\n";
  cout << "lambda for PP calculation in IBM-1,IBM-2,HMM:= " << double(fTrainVcbList.totalVocab()) << "/(" << eTrainVcbList.totalVocab() << "-" << corpus->getTotalNoPairs2() << ")=";
  LAMBDA = double(fTrainVcbList.totalVocab()) / (eTrainVcbList.totalVocab()-corpus->getTotalNoPairs2());
  cout << "= " << LAMBDA << '\n';
  // load dictionary
  Dictionary *dictionary;  
  if (useDict) dictionary = new Dictionary(dictionary_Filename.c_str());
  else dictionary = new Dictionary("");
  int minIter=0;
#ifdef BINARY_SEARCH_FOR_TTABLE
  if( CoocurrenceFile.length()==0 )
    {
      cerr << "ERROR: NO COOCURRENCE FILE GIVEN!\n";
      abort();
    }
  //ifstream coocs(CoocurrenceFile.c_str());
  tmodel<COUNT, PROB> tTable(CoocurrenceFile);
#else
  tmodel<COUNT, PROB> tTable;
#endif

  model1 m1(CorpusFilename.c_str(), eTrainVcbList, fTrainVcbList,tTable,trainPerp, 
	    *corpus,&testPerp, testCorpus, 
	    trainViterbiPerp, &testViterbiPerp);
   amodel<PROB>  aTable(false);
   amodel<COUNT> aCountTable(false);
   model2 m2(m1,aTable,aCountTable);
   hmm h(m2);
   model3 m3(m2); 
   if(ReadTablePrefix.length() )
     {
       string number = "final";
       string tfile,afilennfile,dfile,d4file,p0file,afile,nfile; //d5file
       tfile = ReadTablePrefix + ".t3." + number ;
       afile = ReadTablePrefix + ".a3." + number ;
       nfile = ReadTablePrefix + ".n3." + number ;
       dfile = ReadTablePrefix + ".d3." + number ;
       d4file = ReadTablePrefix + ".d4." + number ;
       //d5file = ReadTablePrefix + ".d5." + number ;
       p0file = ReadTablePrefix + ".p0_3." + number ;
       tTable.readProbTable(tfile.c_str());
       aTable.readTable(afile.c_str());
       m3.dTable.readTable(dfile.c_str());
       m3.nTable.readNTable(nfile.c_str());
       sentPair sent ;
       double p0;
       ifstream p0f(p0file.c_str());
       p0f >> p0;
       d4model d4m(MAX_SENTENCE_LENGTH);
       d4m.makeWordClasses(m1.Elist,m1.Flist,SourceVocabFilename+".classes",TargetVocabFilename+".classes");
       d4m.readProbTable(d4file.c_str());
       //d5model d5m(d4m);
       //d5m.makeWordClasses(m1.Elist,m1.Flist,SourceVocabFilename+".classes",TargetVocabFilename+".classes");
       //d5m.readProbTable(d5file.c_str());
       makeSetCommand("model4smoothfactor","0.0",getGlobalParSet(),2);
       //makeSetCommand("model5smoothfactor","0.0",getGlobalParSet(),2);
       if( corpus||testCorpus )
	 {
	   sentenceHandler *x=corpus;
	   if(x==0)
	     x=testCorpus;
	   cout << "Text corpus exists.\n";
	   x->rewind();
	   while(x&&x->getNextSentence(sent)){
	     Vector<WordIndex>& es = sent.eSent;
	     Vector<WordIndex>& fs = sent.fSent;
	     int l=es.size()-1;
	     int m=fs.size()-1;
	     transpair_model4 tm4(es,fs,m1.tTable,m2.aTable,m3.dTable,m3.nTable,1-p0,p0,&d4m);
	     alignment al(l,m);
	     cout << "I use the alignment " << sent.sentenceNo-1 << '\n';
	     //convert(ReferenceAlignment[sent.sentenceNo-1],al);
	     transpair_model3 tm3(es,fs,m1.tTable,m2.aTable,m3.dTable,m3.nTable,1-p0,p0,0);
	     double p=tm3.prob_of_target_and_alignment_given_source(al,1);
	     cout << "Sentence " << sent.sentenceNo << " has IBM-3 prob " << p << '\n';
	     p=tm4.prob_of_target_and_alignment_given_source(al,3,1);
	     cout << "Sentence " << sent.sentenceNo << " has IBM-4 prob " << p << '\n';
	     //transpair_model5 tm5(es,fs,m1.tTable,m2.aTable,m3.dTable,m3.nTable,1-p0,p0,&d5m);
	     //p=tm5.prob_of_target_and_alignment_given_source(al,3,1);
	     //cout << "Sentence " << sent.sentenceNo << " has IBM-5 prob " << p << '\n';
	   }
	 }
       else
	 {
	   cout << "No corpus exists.\n";
	 }
    }
   else 
     {
       // initialize model1
       bool seedModel1 = false ;
       if(Model1_Iterations > 0){
	 if (t_Filename != "NONE" && t_Filename != ""){
	   seedModel1 = true ;
	   m1.load_table(t_Filename.c_str());
	 }
	 minIter=m1.em_with_tricks(Model1_Iterations,seedModel1,*dictionary, useDict);
	 errors=m1.errorsAL();
       }
       
	 {
	   if(Model2_Iterations > 0){
	     m2.initialize_table_uniformly(*corpus);
	     minIter=m2.em_with_tricks(Model2_Iterations);
	     errors=m2.errorsAL();
	   }
	   if(HMM_Iterations > 0){
	     cout << "NOTE: I am doing iterations with the HMM model!\n";
	     h.makeWordClasses(m1.Elist,m1.Flist,SourceVocabFilename+".classes",TargetVocabFilename+".classes");
	     h.initialize_table_uniformly(*corpus);
	     minIter=h.em_with_tricks(HMM_Iterations);
	     errors=h.errorsAL();
	   }
	   
	   if(Transfer2to3||HMM_Iterations==0){
	     if( HMM_Iterations>0 )
	       cout << "WARNING: transfor is not needed, as results are overwritten bei transfer from HMM.\n";
	     string test_alignfile = Prefix +".tst.A2to3";
	     if (testCorpus)
	       m2.em_loop(testPerp, *testCorpus,Transfer_Dump_Freq==1&&!NODUMPS,test_alignfile.c_str(), testViterbiPerp, true);
	     if (testCorpus)
	       cout << "\nTransfer: TEST CROSS-ENTROPY " << testPerp.cross_entropy() << " PERPLEXITY " << testPerp.perplexity() << "\n\n";
	     if (Transfer == TRANSFER_SIMPLE)
	       m3.transferSimple(*corpus, Transfer_Dump_Freq==1&&!NODUMPS,trainPerp, trainViterbiPerp);
	     else  
	       m3.transfer(*corpus, Transfer_Dump_Freq==1&&!NODUMPS, trainPerp, trainViterbiPerp);
	     errors=m3.errorsAL();
	   }
	   
	   if( HMM_Iterations>0 )
	     m3.setHMM(&h);
	   if(Model3_Iterations > 0 || Model4_Iterations > 0 || Model5_Iterations || Model6_Iterations
	      )
	     {
	       minIter=m3.viterbi(Model3_Iterations,Model4_Iterations,Model5_Iterations,Model6_Iterations);
	       errors=m3.errorsAL();
	     }
	   if (FEWDUMPS||!NODUMPS)
	     {
	       printAllTables(eTrainVcbList,eTestVcbList,fTrainVcbList,fTestVcbList,m1 );
	     }
	 }
     }
   result=minIter;
   return errors;
}

int main(int argc, char* argv[])
{
#ifdef BINARY_SEARCH_FOR_TTABLE
  getGlobalParSet().insert(new Parameter<string>("CoocurrenceFile",ParameterChangedFlag,"",CoocurrenceFile,PARLEV_SPECIAL));
#endif
  getGlobalParSet().insert(new Parameter<string>("ReadTablePrefix",ParameterChangedFlag,"optimized",ReadTablePrefix,-1));
  getGlobalParSet().insert(new Parameter<string>("S",ParameterChangedFlag,"source vocabulary file name",SourceVocabFilename,PARLEV_INPUT));
  getGlobalParSet().insert(new Parameter<string>("SOURCE VOCABULARY FILE",ParameterChangedFlag,"source vocabulary file name",SourceVocabFilename,-1));
  getGlobalParSet().insert(new Parameter<string>("T",ParameterChangedFlag,"target vocabulary file name",TargetVocabFilename,PARLEV_INPUT));
  getGlobalParSet().insert(new Parameter<string>("TARGET VOCABULARY FILE",ParameterChangedFlag,"target vocabulary file name",TargetVocabFilename,-1));
  getGlobalParSet().insert(new Parameter<string>("C",ParameterChangedFlag,"training corpus file name",CorpusFilename,PARLEV_INPUT));
  getGlobalParSet().insert(new Parameter<string>("CORPUS FILE",ParameterChangedFlag,"training corpus file name",CorpusFilename,-1));
  getGlobalParSet().insert(new Parameter<string>("TC",ParameterChangedFlag,"test corpus file name",TestCorpusFilename,PARLEV_INPUT));
  getGlobalParSet().insert(new Parameter<string>("TEST CORPUS FILE",ParameterChangedFlag,"test corpus file name",TestCorpusFilename,-1));
  getGlobalParSet().insert(new Parameter<string>("d",ParameterChangedFlag,"dictionary file name",dictionary_Filename,PARLEV_INPUT));
  getGlobalParSet().insert(new Parameter<string>("DICTIONARY",ParameterChangedFlag,"dictionary file name",dictionary_Filename,-1));
  getGlobalParSet().insert(new Parameter<string>("l",ParameterChangedFlag,"log file name",LogFilename,PARLEV_OUTPUT));
  getGlobalParSet().insert(new Parameter<string>("LOG FILE",ParameterChangedFlag,"log file name",LogFilename,-1));

  getGlobalParSet().insert(new Parameter<string>("o",ParameterChangedFlag,"output file prefix",Prefix,PARLEV_OUTPUT));
  getGlobalParSet().insert(new Parameter<string>("OUTPUT FILE PREFIX",ParameterChangedFlag,"output file prefix",Prefix,-1));
  getGlobalParSet().insert(new Parameter<string>("OUTPUT PATH",ParameterChangedFlag,"output path",OPath,PARLEV_OUTPUT));

  time_t st1, fn;
  st1 = time(NULL);                    // starting time

  string temp(argv[0]);
  Usage = temp + " <config_file> [options]\n";
  if(argc < 2)
    {
      printHelp();    
      exit(1);
    }
  
  initGlobals() ;
  parseArguments(argc, argv);
  
  if (Log)
    logmsg.open(LogFilename.c_str(), ios::out);
  
  printGIZAPars(cout);
  int a=-1;
  double errors=0.0;
  if( OldADBACKOFF!=0 )
    cerr << "WARNING: Parameter -adBackOff does not exist further; use CompactADTable instead.\n";
  if( MAX_SENTENCE_LENGTH > MAX_SENTENCE_LENGTH_ALLOWED )
    cerr << "ERROR: MAX_SENTENCE_LENGTH is too big " << MAX_SENTENCE_LENGTH << " > " << MAX_SENTENCE_LENGTH_ALLOWED << '\n';
    errors=StartTraining(a);
  fn = time(NULL);    // finish time
  cout << '\n' << "Entire Training took: " << difftime(fn, st1) << " seconds\n";
  cout << "Program Finished at: "<< ctime(&fn) << '\n';
  cout << "==========================================================\n";
  return 0;
}