// $Id$

#include "ConfusionNet.h"
#include <sstream>

#include "FactorCollection.h"
#include "Util.h"
#include "TranslationOptionCollectionConfusionNet.h"
#include "StaticData.h"
#include "Sentence.h"
#include "UserMessage.h"
#include "moses/FF/InputFeature.h"
#include "util/exception.hh"

namespace Moses
{
  struct CNStats {
    size_t created,destr,read,colls,words;
    
    CNStats() : created(0),destr(0),read(0),colls(0),words(0) {}
    ~CNStats() {
      print(std::cerr);
    }

    void createOne() {
      ++created;
    }
    void destroyOne() {
      ++destr;
    }
    
    void collect(const ConfusionNet& cn) {
      ++read;
      colls+=cn.GetSize();
      for(size_t i=0; i<cn.GetSize(); ++i)
	words+=cn[i].size();
    }
    void print(std::ostream& out) const {
      if(created>0) {
	out<<"confusion net statistics:\n"
	  " created:\t"<<created<<"\n"
	  " destroyed:\t"<<destr<<"\n"
	  " succ. read:\t"<<read<<"\n"
	  " columns:\t"<<colls<<"\n"
	  " words:\t"<<words<<"\n"
	  " avg. word/column:\t"<<words/(1.0*colls)<<"\n"
	  " avg. cols/sent:\t"<<colls/(1.0*read)<<"\n"
	  "\n\n";
      }
    }
  };
  
  CNStats stats;

  size_t 
  ConfusionNet::
  GetColumnIncrement(size_t i, size_t j) const
  {
    (void) i;
    (void) j;
    return 1;
  }

  ConfusionNet::
  ConfusionNet()
    : InputType()
  {
    stats.createOne();

    const StaticData& staticData = StaticData::Instance();
    if (staticData.IsChart()) {
      m_defaultLabelSet.insert(StaticData::Instance().GetInputDefaultNonTerminal());
    }
    UTIL_THROW_IF2(&InputFeature::Instance() == NULL, "Input feature must be specified");
  }

  ConfusionNet::
  ~ConfusionNet()
  {
    stats.destroyOne();
  }

  ConfusionNet::
  ConfusionNet(Sentence const& s)
  {
    data.resize(s.GetSize());
    for(size_t i=0; i<s.GetSize(); ++i) {
      ScorePair scorePair;
      std::pair<Word, ScorePair > temp = std::make_pair(s.GetWord(i), scorePair);
      data[i].push_back(temp);
    }
  }

  bool 
  ConfusionNet::
  ReadF(std::istream& in, const std::vector<FactorType>& factorOrder, int format)
  {
    VERBOSE(2, "read confusion net with format "<<format<<"\n");
    switch(format) {
    case 0:
      return ReadFormat0(in,factorOrder);
    case 1:
      return ReadFormat1(in,factorOrder);
    default:
      std::stringstream strme;
      strme << "ERROR: unknown format '"<<format
	    <<"' in ConfusionNet::Read";
      UserMessage::Add(strme.str());
    }
    return false;
  }

  int 
  ConfusionNet::
  Read(std::istream& in,
       const std::vector<FactorType>& factorOrder)
  {
    int rv=ReadF(in,factorOrder,0);
    if(rv) stats.collect(*this);
    return rv;
  }

#if 0
  // Deprecated due to code duplication; 
  // use Word::CreateFromString() instead
  void 
  ConfusionNet::
  String2Word(const std::string& s,Word& w,
	      const std::vector<FactorType>& factorOrder)
  {
    std::vector<std::string> factorStrVector = Tokenize(s, "|");
    for(size_t i=0; i<factorOrder.size(); ++i)
      w.SetFactor(factorOrder[i],
		  FactorCollection::Instance().AddFactor
		  (Input,factorOrder[i], factorStrVector[i]));
  }
#endif

  bool 
  ConfusionNet::
  ReadFormat0(std::istream& in, const std::vector<FactorType>& factorOrder)
  {
    Clear();

    // const StaticData   &staticData   = StaticData::Instance();
    const InputFeature &inputFeature = InputFeature::Instance();
    size_t numInputScores   = inputFeature.GetNumInputScores();
    size_t numRealWordCount = inputFeature.GetNumRealWordsInInput();

    size_t totalCount = numInputScores + numRealWordCount;
    bool addRealWordCount = (numRealWordCount > 0);

    std::string line;
    while(getline(in,line)) {
      std::istringstream is(line);
      std::string word;

      Column col;
      while(is>>word) {
	Word w;
	// String2Word(word,w,factorOrder);
	w.CreateFromString(Input,factorOrder,StringPiece(word),false,false);
	std::vector<float> probs(totalCount, 0.0);
	for(size_t i=0; i < numInputScores; i++) {
	  double prob;
	  if (!(is>>prob)) {
	    TRACE_ERR("ERROR: unable to parse CN input - bad link probability, or wrong number of scores\n");
	    return false;
	  }
	  if(prob<0.0) {
	    VERBOSE(1, "WARN: negative prob: "<<prob<<" ->set to 0.0\n");
	    prob=0.0;
	  } else if (prob>1.0) {
	    VERBOSE(1, "WARN: prob > 1.0 : "<<prob<<" -> set to 1.0\n");
	    prob=1.0;
	  }
	  probs[i] = (std::max(static_cast<float>(log(prob)),LOWEST_SCORE));

	}
	//store 'real' word count in last feature if we have one more weight than we do arc scores and not epsilon
	if (addRealWordCount && word!=EPSILON && word!="")
	  probs.back() = -1.0;

	ScorePair scorePair(probs);

	col.push_back(std::make_pair(w,scorePair));
      }
      if(col.size()) {
	data.push_back(col);
	ShrinkToFit(data.back());
      } else break;
    }
    return !data.empty();
  }

  bool 
  ConfusionNet::
  ReadFormat1(std::istream& in, const std::vector<FactorType>& factorOrder)
  {
    Clear();
    std::string line;
    if(!getline(in,line)) return 0;
    size_t s;
    if(getline(in,line)) s=atoi(line.c_str());
    else return 0;
    data.resize(s);
    for(size_t i=0; i<data.size(); ++i) {
      if(!getline(in,line)) return 0;
      std::istringstream is(line);
      if(!(is>>s)) return 0;
      std::string word;
      double prob;
      data[i].resize(s);
      for(size_t j=0; j<s; ++j)
	if(is>>word>>prob) {
	  //TODO: we are only reading one prob from this input format, should read many... but this function is unused anyway. -JS
	  data[i][j].second.denseScores = std::vector<float> (1);
	  data[i][j].second.denseScores.push_back((float) log(prob));
	  if(data[i][j].second.denseScores[0]<0) {
	    VERBOSE(1, "WARN: neg costs: "<<data[i][j].second.denseScores[0]<<" -> set to 0\n");
	    data[i][j].second.denseScores[0]=0.0;
	  }
	  // String2Word(word,data[i][j].first,factorOrder);
	  Word& w = data[i][j].first;
	  w.CreateFromString(Input,factorOrder,StringPiece(word),false,false);
	} else return 0;
    }
    return !data.empty();
  }

  void ConfusionNet::Print(std::ostream& out) const
  {
    out<<"conf net: "<<data.size()<<"\n";
    for(size_t i=0; i<data.size(); ++i) {
      out<<i<<" -- ";
      for(size_t j=0; j<data[i].size(); ++j) {
	out<<"("<<data[i][j].first.ToString()<<", ";

	// dense
	std::vector<float>::const_iterator iterDense;
	for(iterDense = data[i][j].second.denseScores.begin(); 
	    iterDense < data[i][j].second.denseScores.end(); 
	    ++iterDense) {
	  out<<", "<<*iterDense;
	}

	// sparse
	std::map<StringPiece, float>::const_iterator iterSparse;
	for(iterSparse = data[i][j].second.sparseScores.begin(); 
	    iterSparse != data[i][j].second.sparseScores.end(); 
	    ++iterSparse) {
	  out << ", " << iterSparse->first << "=" << iterSparse->second;
	}

	out<<") ";
      }
      out<<"\n";
    }
    out<<"\n\n";
  }

#ifdef _WIN32
#pragma warning(disable:4716)
#endif
  Phrase 
  ConfusionNet::
  GetSubString(const WordsRange&) const
  {
    UTIL_THROW2("ERROR: call to ConfusionNet::GetSubString\n");
    //return Phrase(Input);
  }

  std::string 
  ConfusionNet::
  GetStringRep(const std::vector<FactorType> /* factorsToPrint */) const  //not well defined yet
  {
    TRACE_ERR("ERROR: call to ConfusionNet::GeStringRep\n");
    return "";
  }
#ifdef _WIN32
#pragma warning(disable:4716)
#endif
  const Word& ConfusionNet::GetWord(size_t) const
  {
    UTIL_THROW2("ERROR: call to ConfusionNet::GetFactorArray\n");
  }
#ifdef _WIN32
#pragma warning(default:4716)
#endif
  std::ostream& operator<<(std::ostream& out,const ConfusionNet& cn)
  {
    cn.Print(out);
    return out;
  }

  TranslationOptionCollection*
  ConfusionNet::
  CreateTranslationOptionCollection() const
  {
    size_t maxNoTransOptPerCoverage 
      = StaticData::Instance().GetMaxNoTransOptPerCoverage();
    float translationOptionThreshold 
      = StaticData::Instance().GetTranslationOptionThreshold();
    TranslationOptionCollection *rv 
      = new TranslationOptionCollectionConfusionNet
      (*this, maxNoTransOptPerCoverage, translationOptionThreshold);
    assert(rv);
    return rv;
  }

}