#include <iostream>
#include <fstream>
#include <tr1/unordered_map>
#include <tr1/unordered_set>

#include <boost/shared_ptr.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>

#include "timing_stats.h"
#include "translator.h"
#include "phrasebased_translator.h"
#include "aligner.h"
#include "stringlib.h"
#include "forest_writer.h"
#include "hg_io.h"
#include "filelib.h"
#include "sampler.h"
#include "sparse_vector.h"
#include "tagger.h"
#include "lextrans.h"
#include "lexalign.h"
#include "csplit.h"
#include "weights.h"
#include "tdict.h"
#include "ff.h"
#include "ff_factory.h"
#include "hg_intersect.h"
#include "apply_models.h"
#include "viterbi.h"
#include "kbest.h"
#include "inside_outside.h"
#include "exp_semiring.h"
#include "sentence_metadata.h"
#include "../vest/scorer.h"

using namespace std;
using namespace std::tr1;
using boost::shared_ptr;
namespace po = boost::program_options;

bool verbose_feature_functions=true;

// some globals ...
boost::shared_ptr<RandomNumberGenerator<boost::mt19937> > rng;
static const double kMINUS_EPSILON = -1e-6;  // don't be too strict

namespace Hack { void MaxTrans(const Hypergraph& in, int beam_size); }
namespace NgramCache { void Clear(); }

void ShowBanner() {
  cerr << "cdec v1.0 (c) 2009-2010 by Chris Dyer\n";
}

void ConvertSV(const SparseVector<prob_t>& src, SparseVector<double>* trg) {
  for (SparseVector<prob_t>::const_iterator it = src.begin(); it != src.end(); ++it)
    trg->set_value(it->first, it->second);
}


inline string str(char const* name,po::variables_map const& conf) {
  return conf[name].as<string>();
}

shared_ptr<FeatureFunction> make_ff(string const& ffp,bool verbose_feature_functions,char const* pre="") {
  string ff, param;
  SplitCommandAndParam(ffp, &ff, &param);
  cerr << "Feature: " << ff;
  if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
  else cerr << " (no config parameters)\n";
  shared_ptr<FeatureFunction> pf = global_ff_registry->Create(ff, param);
  if (!pf)
    exit(1);
  int nbyte=pf->NumBytesContext();
  if (verbose_feature_functions)
    cerr<<"State is "<<nbyte<<" bytes for "<<pre<<"feature "<<ffp<<endl;
  return pf;
}

// print just the --long_opt names suitable for bash compgen
void print_options(std::ostream &out,po::options_description const& opts) {
  typedef std::vector< shared_ptr<po::option_description> > Ds;
  Ds const& ds=opts.options();
  out << '"';
  for (unsigned i=0;i<ds.size();++i) {
    if (i) out<<' ';
    out<<"--"<<ds[i]->long_name();
  }
  out << '"';
}


void InitCommandLine(int argc, char** argv, po::variables_map* confp) {
  po::variables_map &conf=*confp;
  po::options_description opts("Configuration options");
  opts.add_options()
        ("formalism,f",po::value<string>(),"Decoding formalism; values include SCFG, FST, PB, LexTrans (lexical translation model, also disc training), CSplit (compound splitting), Tagger (sequence labeling), LexAlign (alignment only, or EM training)")
        ("input,i",po::value<string>()->default_value("-"),"Source file")
        ("grammar,g",po::value<vector<string> >()->composing(),"Either SCFG grammar file(s) or phrase tables file(s)")
        ("weights,w",po::value<string>(),"Feature weights file")
    ("prelm_weights",po::value<string>(),"Feature weights file for prelm_beam_prune.  Requires --weights.")
    ("prelm_copy_weights","use --weights as value for --prelm_weights.")
    ("prelm_feature_function",po::value<vector<string> >()->composing(),"Additional feature functions for prelm pass only (in addition to the 0-state subset of feature_function")
    ("keep_prelm_cube_order","when forest rescoring with final models, use the edge ordering from the prelm pruning features*weights.  only meaningful if --prelm_weights given.  UNTESTED but assume that cube pruning gives a sensible result, and that 'good' (as tuned for bleu w/ prelm features) edges come first.")
    ("warn_0_weight","Warn about any feature id that has a 0 weight (this is perfectly safe if you intend 0 weight, though)")
        ("no_freeze_feature_set,Z", "Do not freeze feature set after reading feature weights file")
        ("feature_function,F",po::value<vector<string> >()->composing(), "Additional feature function(s) (-L for list)")
        ("list_feature_functions,L","List available feature functions")
        ("add_pass_through_rules,P","Add rules to translate OOV words as themselves")
	("k_best,k",po::value<int>(),"Extract the k best derivations")
	("unique_k_best,r", "Unique k-best translation list")
        ("aligner,a", "Run as a word/phrase aligner (src & ref required)")
        ("intersection_strategy,I",po::value<string>()->default_value("cube_pruning"), "Intersection strategy for incorporating finite-state features; values include Cube_pruning, Full")
        ("cubepruning_pop_limit,K",po::value<int>()->default_value(200), "Max number of pops from the candidate heap at each node")
        ("goal",po::value<string>()->default_value("S"),"Goal symbol (SCFG & FST)")
        ("scfg_extra_glue_grammar", po::value<string>(), "Extra glue grammar file (Glue grammars apply when i=0 but have no other span restrictions)")
        ("scfg_no_hiero_glue_grammar,n", "No Hiero glue grammar (nb. by default the SCFG decoder adds Hiero glue rules)")
        ("scfg_default_nt,d",po::value<string>()->default_value("X"),"Default non-terminal symbol in SCFG")
        ("scfg_max_span_limit,S",po::value<int>()->default_value(10),"Maximum non-terminal span limit (except \"glue\" grammar)")
        ("show_joshua_visualization,J", "Produce output compatible with the Joshua visualization tools")
        ("show_tree_structure", "Show the Viterbi derivation structure")
        ("show_expected_length", "Show the expected translation length under the model")
        ("show_partition,z", "Compute and show the partition (inside score)")
        ("show_cfg_search_space", "Show the search space as a CFG")
    ("show_features","Show the feature vector for the viterbi translation")
    ("prelm_density_prune", po::value<double>(), "Applied to -LM forest just before final LM rescoring: keep no more than this many times the number of edges used in the best derivation tree (>=1.0)")
    ("density_prune", po::value<double>(), "Keep no more than this many times the number of edges used in the best derivation tree (>=1.0)")
        ("prelm_beam_prune", po::value<double>(), "Prune paths from -LM forest before LM rescoring, keeping paths within exp(alpha>=0)")
        ("coarse_to_fine_beam_prune", po::value<double>(), "Prune paths from coarse parse forest before fine parse, keeping paths within exp(alpha>=0)")
        ("ctf_beam_widen", po::value<double>()->default_value(2.0), "Expand coarse pass beam by this factor if no fine parse is found")
        ("ctf_num_widenings", po::value<int>()->default_value(2), "Widen coarse beam this many times before backing off to full parse")
        ("ctf_no_exhaustive", "Do not fall back to exhaustive parse if coarse-to-fine parsing fails")
        ("beam_prune", po::value<double>(), "Prune paths from +LM forest, keep paths within exp(alpha>=0)")
    ("scale_prune_srclen", "scale beams by the input length (in # of tokens; may not be what you want for lattices")
    ("promise_power",po::value<double>()->default_value(0), "Give more beam budget to more promising previous-pass nodes when pruning - but allocate the same average beams.  0 means off, 1 means beam proportional to inside*outside prob, n means nth power (affects just --cubepruning_pop_limit)")
        ("lexalign_use_null", "Support source-side null words in lexical translation")
        ("tagger_tagset,t", po::value<string>(), "(Tagger) file containing tag set")
        ("csplit_output_plf", "(Compound splitter) Output lattice in PLF format")
        ("csplit_preserve_full_word", "(Compound splitter) Always include the unsegmented form in the output lattice")
        ("extract_rules", po::value<string>(), "Extract the rules used in translation (de-duped) to this file")
        ("graphviz","Show (constrained) translation forest in GraphViz format")
        ("max_translation_beam,x", po::value<int>(), "Beam approximation to get max translation from the chart")
        ("max_translation_sample,X", po::value<int>(), "Sample the max translation from the chart")
        ("pb_max_distortion,D", po::value<int>()->default_value(4), "Phrase-based decoder: maximum distortion")
        ("cll_gradient,G","Compute conditional log-likelihood gradient and write to STDOUT (src & ref required)")
        ("crf_uniform_empirical", "If there are multple references use (i.e., lattice) a uniform distribution rather than posterior weighting a la EM")
    ("get_oracle_forest,OO", "Calculate rescored hypregraph using approximate BLEU scoring of rules")
    ("feature_expectations","Write feature expectations for all features in chart (**OBJ** will be the partition)")
    ("references,R", po::value<vector<string> >(), "Translation reference files")    
        ("vector_format",po::value<string>()->default_value("b64"), "Sparse vector serialization format for feature expectations or gradients, includes (text or b64)")
        ("combine_size,C",po::value<int>()->default_value(1), "When option -G is used, process this many sentence pairs before writing the gradient (1=emit after every sentence pair)")
        ("forest_output,O",po::value<string>(),"Directory to write forests to")
        ("minimal_forests,m","Write minimal forests (excludes Rule information). Such forests can be used for ML/MAP training, but not rescoring, etc.");
  po::options_description clo("Command line options");
  clo.add_options()
        ("config,c", po::value<string>(), "Configuration file")
        ("help,h", "Print this help message and exit")
    ("usage,u", po::value<string>(), "Describe a feature function type")
    ("compgen", "Print just option names suitable for bash command line completion builtin 'compgen'")
    ;

  po::options_description dconfig_options, dcmdline_options;
  dconfig_options.add(opts);
  dcmdline_options.add(opts).add(clo);

  po::store(parse_command_line(argc, argv, dcmdline_options), conf);
  if (conf.count("compgen")) {
    print_options(cout,dcmdline_options);
    cout << endl;
    exit(0);
  }
  ShowBanner();
  if (conf.count("config")) {
    const string cfg = str("config",conf);
    cerr << "Configuration file: " << cfg << endl;
    ifstream config(cfg.c_str());
    po::store(po::parse_config_file(config, dconfig_options), conf);
  }
  po::notify(conf);

  if (conf.count("list_feature_functions")) {
    cerr << "Available feature functions (specify with -F; describe with -u FeatureName):\n";
    global_ff_registry->DisplayList();
    cerr << endl;
    exit(1);
  }


  if (conf.count("usage")) {
    cout<<global_ff_registry->usage(str("usage",conf),true,true)<<endl;
    exit(0);
  }
  if (conf.count("help")) {
    cout << dcmdline_options << endl;
    exit(0);
  }
  if (conf.count("help") || conf.count("formalism") == 0) {
    cerr << dcmdline_options << endl;
    exit(1);
  }

  const string formalism = LowercaseString(str("formalism",conf));
  if (formalism != "scfg" && formalism != "fst" && formalism != "lextrans" && formalism != "pb" && formalism != "csplit" && formalism != "tagger" && formalism != "lexalign") {
    cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit', 'lextrans', 'lexalign', or 'tagger'\n";
    cerr << dcmdline_options << endl;
    exit(1);
  }
}

// TODO move out of cdec into some sampling decoder file
void SampleRecurse(const Hypergraph& hg, const vector<SampleSet>& ss, int n, vector<WordID>* out) {
  const SampleSet& s = ss[n];
  int i = rng->SelectSample(s);
  const Hypergraph::Edge& edge = hg.edges_[hg.nodes_[n].in_edges_[i]];
  vector<vector<WordID> > ants(edge.tail_nodes_.size());
  for (int j = 0; j < ants.size(); ++j)
    SampleRecurse(hg, ss, edge.tail_nodes_[j], &ants[j]);

  vector<const vector<WordID>*> pants(ants.size());
  for (int j = 0; j < ants.size(); ++j) pants[j] = &ants[j];
  edge.rule_->ESubstitute(pants, out);
}

struct SampleSort {
  bool operator()(const pair<int,string>& a, const pair<int,string>& b) const {
    return a.first > b.first;
  }
};

// TODO move out of cdec into some sampling decoder file
void MaxTranslationSample(Hypergraph* hg, const int samples, const int k) {
  unordered_map<string, int, boost::hash<string> > m;
  hg->PushWeightsToGoal();
  const int num_nodes = hg->nodes_.size();
  vector<SampleSet> ss(num_nodes);
  for (int i = 0; i < num_nodes; ++i) {
    SampleSet& s = ss[i];
    const vector<int>& in_edges = hg->nodes_[i].in_edges_;
    for (int j = 0; j < in_edges.size(); ++j) {
      s.add(hg->edges_[in_edges[j]].edge_prob_);
    }
  }
  for (int i = 0; i < samples; ++i) {
    vector<WordID> yield;
    SampleRecurse(*hg, ss, hg->nodes_.size() - 1, &yield);
    const string trans = TD::GetString(yield);
    ++m[trans];
  }
  vector<pair<int, string> > dist;
  for (unordered_map<string, int, boost::hash<string> >::iterator i = m.begin();
         i != m.end(); ++i) {
    dist.push_back(make_pair(i->second, i->first));
  }
  sort(dist.begin(), dist.end(), SampleSort());
  if (k) {
    for (int i = 0; i < k; ++i)
      cout << dist[i].first << " ||| " << dist[i].second << endl;
  } else {
    cout << dist[0].second << endl;
  }
}

// TODO decoder output should probably be moved to another file
void DumpKBest(const int sent_id, const Hypergraph& forest, const int k, const bool unique, const char *kbest_out_filename_, float doc_src_length, float tmp_src_length, const DocScorer &ds, Score* doc_score) {
cerr << "In kbest\n";

 ofstream kbest_out;
 kbest_out.open(kbest_out_filename_);
 cerr << "Output kbest to " << kbest_out_filename_;
 
 //add length (f side) src length of this sentence to the psuedo-doc src length count
 float curr_src_length = doc_src_length + tmp_src_length;

 if (unique) {
    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique> kbest(forest, k);
    for (int i = 0; i < k; ++i) {
      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique>::Derivation* d =
        kbest.LazyKthBest(forest.nodes_.size() - 1, i);
      if (!d) break;
      //calculate score in context of psuedo-doc
      Score* sentscore = ds[sent_id]->ScoreCandidate(d->yield);
      sentscore->PlusEquals(*doc_score,float(1));
      float bleu = curr_src_length * sentscore->ComputeScore();
      kbest_out << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
		<< d->feature_values << " ||| " << log(d->score) << " ||| " << bleu << endl;
      // cout << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
      //     << d->feature_values << " ||| " << log(d->score) << endl;
    }
  } else {
    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k);
    for (int i = 0; i < k; ++i) {
      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
        kbest.LazyKthBest(forest.nodes_.size() - 1, i);
      if (!d) break;
      cout << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
           << d->feature_values << " ||| " << log(d->score) << endl;
    }
  }
}

struct ELengthWeightFunction {
  double operator()(const Hypergraph::Edge& e) const {
    return e.rule_->ELength() - e.rule_->Arity();
  }
};


struct TRPHash {
  size_t operator()(const TRulePtr& o) const { return reinterpret_cast<size_t>(o.get()); }
};
static void ExtractRulesDedupe(const Hypergraph& hg, ostream* os) {
  static unordered_set<TRulePtr, TRPHash> written;
  for (int i = 0; i < hg.edges_.size(); ++i) {
    const TRulePtr& rule = hg.edges_[i].rule_;
    if (written.insert(rule).second) {
      (*os) << rule->AsString() << endl;
    }
  }
}

void register_feature_functions();

bool beam_param(po::variables_map const& conf,string const& name,double *val,bool scale_srclen=false,double srclen=1)
{
  if (conf.count(name)) {
    *val=conf[name].as<double>()*(scale_srclen?srclen:1);
    return true;
  }
  return false;
}

bool prelm_weights_string(po::variables_map const& conf,string &s)
{
  if (conf.count("prelm_weights")) {
    s=str("prelm_weights",conf);
    return true;
  }
  if (conf.count("prelm_copy_weights")) {
    s=str("weights",conf);
    return true;
  }
  return false;
}


void forest_stats(Hypergraph &forest,string name,bool show_tree,bool show_features,FeatureWeights *weights=0) {
    cerr << viterbi_stats(forest,name,true,show_tree);
    if (show_features) {
      cerr << name<<"     features: ";
/*      Hypergraph::Edge const* best=forest.ViterbiGoalEdge();
      if (!best)
        cerr << name<<" has no goal edge.";
      else
        cerr<<best->feature_values_;
*/
      cerr << ViterbiFeatures(forest,weights);
      cerr << endl;
    }
}

void maybe_prune(Hypergraph &forest,po::variables_map const& conf,string nbeam,string ndensity,string forestname,double srclen) {
    double beam_prune=0,density_prune=0;
    bool use_beam_prune=beam_param(conf,nbeam,&beam_prune,conf.count("scale_prune_srclen"),srclen);
    bool use_density_prune=beam_param(conf,ndensity,&density_prune);
    if (use_beam_prune || use_density_prune) {
      double presize=forest.edges_.size();
      vector<bool> preserve_mask,*pm=0;
      if (conf.count("csplit_preserve_full_word")) {
        preserve_mask.resize(forest.edges_.size());
        preserve_mask[CompoundSplit::GetFullWordEdgeIndex(forest)] = true;
        pm=&preserve_mask;
      }
      forest.PruneInsideOutside(beam_prune,density_prune,pm,false,1,conf["promise_power"].as<double>());
      if (!forestname.empty()) forestname=" "+forestname;
      forest_stats(forest,"  Pruned "+forestname+" forest",false,false);
      cerr << "  Pruned "<<forestname<<" forest portion of edges kept: "<<forest.edges_.size()/presize<<endl;
    }
}

void show_models(po::variables_map const& conf,ModelSet &ms,char const* header) {
  cerr<<header<<": ";
  ms.show_features(cerr,cerr,conf.count("warn_0_weight"));
}


int main(int argc, char** argv) {
  global_ff_registry.reset(new FFRegistry);
  register_feature_functions();
  po::variables_map conf;
  InitCommandLine(argc, argv, &conf);
  const bool write_gradient = conf.count("cll_gradient");
  const bool feature_expectations = conf.count("feature_expectations");
  if (write_gradient && feature_expectations) {
    cerr << "You can only specify --gradient or --feature_expectations, not both!\n";
    exit(1);
  }
  const bool output_training_vector = (write_gradient || feature_expectations);

  boost::shared_ptr<Translator> translator;
  const string formalism = LowercaseString(str("formalism",conf));
  const bool csplit_preserve_full_word = conf.count("csplit_preserve_full_word");
  if (csplit_preserve_full_word &&
      (formalism != "csplit" || !(conf.count("beam_prune")||conf.count("density_prune")||conf.count("prelm_beam_prune")||conf.count("prelm_density_prune")))) {
    cerr << "--csplit_preserve_full_word should only be "
         << "used with csplit AND --*_prune!\n";
    exit(1);
  }
  const bool csplit_output_plf = conf.count("csplit_output_plf");
  if (csplit_output_plf && formalism != "csplit") {
    cerr << "--csplit_output_plf should only be used with csplit!\n";
    exit(1);
  }

  // load feature weights (and possibly freeze feature set)
  vector<double> feature_weights,prelm_feature_weights;
  Weights w,prelm_w;
  bool has_prelm_models = false;
  if (conf.count("weights")) {
    w.InitFromFile(str("weights",conf));
    feature_weights.resize(FD::NumFeats());
    w.InitVector(&feature_weights);
    string plmw;
    if (prelm_weights_string(conf,plmw)) {
      has_prelm_models = true;
      prelm_w.InitFromFile(plmw);
      prelm_feature_weights.resize(FD::NumFeats());
      prelm_w.InitVector(&prelm_feature_weights);
//      cerr << "prelm_weights: " << FeatureVector(prelm_feature_weights)<<endl;
    }
//    cerr << "+LM weights: " << FeatureVector(feature_weights)<<endl;
  }
  bool warn0=conf.count("warn_0_weight");
  bool freeze=!conf.count("no_freeze_feature_set");
  bool early_freeze=freeze && !warn0;
  bool late_freeze=freeze && warn0;
  if (early_freeze) {
    cerr << "Freezing feature set (use --no_freeze_feature_set or --warn_0_weight to prevent)." << endl;
    FD::Freeze(); // this means we can't see the feature names of not-weighted features
  }

  // set up translation back end
  if (formalism == "scfg")
    translator.reset(new SCFGTranslator(conf));
  else if (formalism == "fst")
    translator.reset(new FSTTranslator(conf));
  else if (formalism == "pb")
    translator.reset(new PhraseBasedTranslator(conf));
  else if (formalism == "csplit")
    translator.reset(new CompoundSplit(conf));
  else if (formalism == "lextrans")
    translator.reset(new LexicalTrans(conf));
  else if (formalism == "lexalign")
    translator.reset(new LexicalAlign(conf));
  else if (formalism == "tagger")
    translator.reset(new Tagger(conf));
  else
    assert(!"error");

  // set up additional scoring features
  vector<shared_ptr<FeatureFunction> > pffs,prelm_only_ffs;

  vector<const FeatureFunction*> late_ffs,prelm_ffs;
  if (conf.count("feature_function") > 0) {
    const vector<string>& add_ffs = conf["feature_function"].as<vector<string> >();
    for (int i = 0; i < add_ffs.size(); ++i) {
      pffs.push_back(make_ff(add_ffs[i],verbose_feature_functions));
      FeatureFunction const* p=pffs.back().get();
      late_ffs.push_back(p);
      if (has_prelm_models) {
        if (p->NumBytesContext()==0)
          prelm_ffs.push_back(p);
        else
          cerr << "Excluding stateful feature from prelm pruning: "<<add_ffs[i]<<endl;
}
    }
  }
  if (conf.count("prelm_feature_function") > 0) {
    const vector<string>& add_ffs = conf["prelm_feature_function"].as<vector<string> >();
    for (int i = 0; i < add_ffs.size(); ++i) {
      prelm_only_ffs.push_back(make_ff(add_ffs[i],verbose_feature_functions,"prelm-only "));
      prelm_ffs.push_back(prelm_only_ffs.back().get());
    }
  }

  if (late_freeze) {
    cerr << "Late freezing feature set (use --no_freeze_feature_set to prevent)." << endl;
    FD::Freeze(); // this means we can't see the feature names of not-weighted features
  }

  if (has_prelm_models)
        cerr << "prelm rescoring with "<<prelm_ffs.size()<<" 0-state feature functions.  +LM pass will use "<<late_ffs.size()<<" features (not counting rule features)."<<endl;

  ModelSet late_models(feature_weights, late_ffs);
  show_models(conf,late_models,"late ");
  ModelSet prelm_models(prelm_feature_weights, prelm_ffs);
  if (has_prelm_models)
    show_models(conf,prelm_models,"prelm ");

  int palg = 1;
  if (LowercaseString(str("intersection_strategy",conf)) == "full") {
    palg = 0;
    cerr << "Using full intersection (no pruning).\n";
  }
  const IntersectionConfiguration inter_conf(palg, conf["cubepruning_pop_limit"].as<int>());

  const int sample_max_trans = conf.count("max_translation_sample") ?
    conf["max_translation_sample"].as<int>() : 0;
  if (sample_max_trans)
    rng.reset(new RandomNumberGenerator<boost::mt19937>);
  const bool aligner_mode = conf.count("aligner");
  const bool minimal_forests = conf.count("minimal_forests");
  const bool graphviz = conf.count("graphviz");
  const bool joshua_viz = conf.count("show_joshua_visualization");
  const bool encode_b64 = str("vector_format",conf) == "b64";
  const bool kbest = conf.count("k_best");
  const bool unique_kbest = conf.count("unique_k_best");
  const bool crf_uniform_empirical = conf.count("crf_uniform_empirical");
  const bool get_oracle_forest = conf.count("get_oracle_forest");

  /*Oracle Extraction Prep*/
  vector<const FeatureFunction*> oracle_model_ffs;
  vector<double> oracle_feature_weights;
  shared_ptr<FeatureFunction> oracle_pff;
  if(get_oracle_forest)      {
    
    /*Add feature for oracle rescoring */
    string ff, param;
    ff="BLEUModel";
    //pass the location of the references file via param to BLEUModel
    for(int kk=0;kk < conf["references"].as<vector<string> >().size();kk++)
      {
	param =  param + " " + conf["references"].as<vector<string> >()[kk];
      }       
    cerr << "Feature: " << ff << "->" << param << endl;
    oracle_pff = global_ff_registry->Create(ff,param);
    if (!oracle_pff) { exit(1); }
    oracle_model_ffs.push_back(oracle_pff.get());	
    oracle_feature_weights.push_back(1.0);

  }

  ModelSet oracle_models(oracle_feature_weights, oracle_model_ffs); 

  const string loss_function3 = "IBM_BLEU_3";
  ScoreType type3 = ScoreTypeFromString(loss_function3);
  const DocScorer ds(type3, conf["references"].as<vector<string> >(), "");
  cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function3 << endl;
  
    
  std::ostringstream kbest_string_stream;
  Score* doc_score=NULL;
  float doc_src_length=0;
  float tmp_src_length=0; 
  int oracle_doc_size= 10;   //used for scaling/weighting oracle doc
  float scale_oracle=  1-float(1)/oracle_doc_size;

  /*End Oracle Extraction Prep*/


  shared_ptr<WriteFile> extract_file;
  if (conf.count("extract_rules"))
    extract_file.reset(new WriteFile(str("extract_rules",conf)));

  int combine_size = conf["combine_size"].as<int>();
  if (combine_size < 1) combine_size = 1;
    const string input = str("input",conf);
  cerr << "Reading input from " << ((input == "-") ? "STDIN" : input.c_str()) << endl;
  ReadFile in_read(input);
  istream *in = in_read.stream();
  assert(*in);

  SparseVector<prob_t> acc_vec;  // accumulate gradient
  double acc_obj = 0; // accumulate objective
  int g_count = 0;    // number of gradient pieces computed
  int sent_id = -1;         // line counter

  while(*in) {
    NgramCache::Clear();   // clear ngram cache for remote LM (if used)
    Timer::Summarize();
    ++sent_id;
    string buf;
    getline(*in, buf);
    if (buf.empty()) continue;
    map<string, string> sgml;
    ProcessAndStripSGML(&buf, &sgml);
    if (sgml.find("id") != sgml.end())
      sent_id = atoi(sgml["id"].c_str());

    cerr << "\nINPUT: ";
    if (buf.size() < 100)
      cerr << buf << endl;
    else {
     size_t x = buf.rfind(" ", 100);
     if (x == string::npos) x = 100;
     cerr << buf.substr(0, x) << " ..." << endl;
    }
    cerr << "  id = " << sent_id << endl;
    string to_translate;
    Lattice ref;
    ParseTranslatorInputLattice(buf, &to_translate, &ref);
    const unsigned srclen=NTokens(to_translate,' ');
//FIXME: should get the avg. or max source length of the input lattice (like Lattice::dist_(start,end)); but this is only used to scale beam parameters (optionally) anyway so fidelity isn't important.
    const bool has_ref = ref.size() > 0;
    SentenceMetadata smeta(sent_id, ref);
    const bool hadoop_counters = (write_gradient);
    Hypergraph forest;          // -LM forest
    translator->ProcessMarkupHints(sgml);
    Timer t("Translation");
    const bool translation_successful =
      translator->Translate(to_translate, &smeta, feature_weights, &forest);
    //TODO: modify translator to incorporate all 0-state model scores immediately?
    translator->SentenceComplete();
    if (!translation_successful) {
      cerr << "  NO PARSE FOUND.\n";
      if (hadoop_counters)
        cerr << "reporter:counter:UserCounters,FParseFailed,1" << endl;
      cout << endl << flush;
      continue;
    }
    const bool show_tree_structure=conf.count("show_tree_structure");
    const bool show_features=conf.count("show_features");
    forest_stats(forest,"  -LM forest",show_tree_structure,show_features,&feature_weights);
    if (conf.count("show_expected_length")) {
      const PRPair<double, double> res =
        Inside<PRPair<double, double>,
               PRWeightFunction<double, EdgeProb, double, ELengthWeightFunction> >(forest);
      cerr << "  Expected length  (words): " << res.r / res.p << "\t" << res << endl;
    }
    if (conf.count("show_partition")) {
      const prob_t z = Inside<prob_t, EdgeProb>(forest);
      cerr << "  -LM partition     log(Z): " << log(z) << endl;
    }
    if (extract_file)
      ExtractRulesDedupe(forest, extract_file->stream());

    if (has_prelm_models) {
      Timer t("prelm rescoring");
      forest.Reweight(prelm_feature_weights);
      forest.SortInEdgesByEdgeWeights();
      Hypergraph prelm_forest;
      ApplyModelSet(forest,
                    smeta,
                    prelm_models,
                    inter_conf, // this is now reduced to exhaustive if all are stateless
                    &prelm_forest);
      forest.swap(prelm_forest);
      forest.Reweight(prelm_feature_weights);
      forest_stats(forest," prelm forest",show_tree_structure,show_features,&prelm_feature_weights);
    }

    maybe_prune(forest,conf,"prelm_beam_prune","prelm_density_prune","-LM",srclen);

    bool has_late_models = !late_models.empty();
    if (has_late_models) {
      Timer t("Forest rescoring:");
      forest.Reweight(feature_weights);
      if (!has_prelm_models || conf.count("keep_prelm_cube_order"))
        forest.SortInEdgesByEdgeWeights();
      Hypergraph lm_forest;
      ApplyModelSet(forest,
                    smeta,
                    late_models,
                    inter_conf,
                    &lm_forest);
      forest.swap(lm_forest);
      forest.Reweight(feature_weights);
      forest_stats(forest,"  +LM forest",show_tree_structure,show_features,&feature_weights);
    }

    maybe_prune(forest,conf,"beam_prune","density_prune","+LM",srclen);

    vector<WordID> trans;
    ViterbiESentence(forest, &trans);
    
    /*Oracle Rescoring*/
    if(get_oracle_forest)
      {
	Timer t("Forest Oracle rescoring:");
	vector<WordID> model_trans;
	model_trans = trans;

	trans=model_trans;
	Score* sentscore = ds[sent_id]->ScoreCandidate(model_trans);
	//initilize psuedo-doc vector to 1 counts
	if (!doc_score) { doc_score = sentscore->GetOne(); }
	double bleu_scale_ = doc_src_length * doc_score->ComputeScore();
	tmp_src_length = smeta.GetSourceLength();
	smeta.SetScore(doc_score);
	smeta.SetDocLen(doc_src_length);
	smeta.SetDocScorer(&ds); 
	
	feature_weights[0]=1.0;	

	kbest_string_stream << conf["forest_output"].as<string>() << "/kbest_model" << "." << sent_id;
	DumpKBest(sent_id, forest, 10, true, kbest_string_stream.str().c_str(), doc_src_length, tmp_src_length, ds, doc_score);
	kbest_string_stream.str("");


	forest.SortInEdgesByEdgeWeights();
	Hypergraph lm_forest;
	const IntersectionConfiguration inter_conf_oracle(0, 0);
	cerr << "Going to call Apply Model " << endl;
	ApplyModelSet(forest,
		      smeta,
		      oracle_models,
		      inter_conf_oracle,
		      &lm_forest);
	
	forest.swap(lm_forest);
	forest.Reweight(feature_weights);
	forest.SortInEdgesByEdgeWeights();
	vector<WordID> oracle_trans;

	ViterbiESentence(forest, &oracle_trans);
	cerr << "  +Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
	cerr << "  +Oracle BLEU (paths): " << forest.NumberOfPaths() << endl;
	cerr << "  +Oracle BLEU Viterbi: " << TD::GetString(oracle_trans) << endl;  
      
	//compute kbest for oracle
	kbest_string_stream << conf["forest_output"].as<string>() <<"/kbest_oracle" << "." << sent_id;
	DumpKBest(sent_id, forest, 10, true, kbest_string_stream.str().c_str(), doc_src_length, tmp_src_length, ds, doc_score);
	kbest_string_stream.str("");
	
	
	//reweight the model with -1 for the BLEU feature to compute k-best list for negative examples
	feature_weights[0]=-1.0;
	forest.Reweight(feature_weights);
	forest.SortInEdgesByEdgeWeights();
	vector<WordID> neg_trans;
	ViterbiESentence(forest, &neg_trans);
	cerr << "  -Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
	cerr << "  -Oracle BLEU (paths): " << forest.NumberOfPaths() << endl;
	cerr << "  -Oracle BLEU Viterbi: " << TD::GetString(neg_trans) << endl;  
	
	//compute kbest for negative
	kbest_string_stream << conf["forest_output"].as<string>() << "/kbest_negative" << "." << sent_id;
	DumpKBest(sent_id, forest, 10, true, kbest_string_stream.str().c_str(), doc_src_length, tmp_src_length,ds,  doc_score);
	kbest_string_stream.str("");
		
	//Add 1-best translation (trans) to psuedo-doc vectors
	doc_score->PlusEquals(*sentscore, scale_oracle);
	delete sentscore;
	
	doc_src_length = (doc_src_length + tmp_src_length) * scale_oracle;
	
	
	string details;
	doc_score->ScoreDetails(&details);
	cerr << "SCALED SCORE: " << bleu_scale_ << "DOC BLEU " << doc_score->ComputeScore() << " " <<details << endl;
      }


    if (conf.count("forest_output") && !has_ref) {
      ForestWriter writer(str("forest_output",conf), sent_id);
      if (FileExists(writer.fname_)) {
        cerr << "  Unioning...\n";
        Hypergraph new_hg;
        {
          ReadFile rf(writer.fname_);
          bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
          assert(succeeded);
        }
        new_hg.Union(forest);
        bool succeeded = writer.Write(new_hg, minimal_forests);
        assert(succeeded);
      } else {
        bool succeeded = writer.Write(forest, minimal_forests);
        assert(succeeded);
      }
    }

    if (sample_max_trans) {
      MaxTranslationSample(&forest, sample_max_trans, conf.count("k_best") ? conf["k_best"].as<int>() : 0);
    } else {
      
      if (kbest) {
        DumpKBest(sent_id, forest, conf["k_best"].as<int>(), unique_kbest,"", doc_src_length, tmp_src_length, ds,  doc_score);
      } else if (csplit_output_plf) {
        cout << HypergraphIO::AsPLF(forest, false) << endl;
      } else {
        if (!graphviz && !has_ref && !joshua_viz) {
          cout << TD::GetString(trans) << endl << flush;
        }
        if (joshua_viz) {
          cout << sent_id << " ||| " << JoshuaVisualizationString(forest) << " ||| 1.0 ||| " << -1.0 << endl << flush;
        }
      }
    }

    const int max_trans_beam_size = conf.count("max_translation_beam") ?
      conf["max_translation_beam"].as<int>() : 0;
    if (max_trans_beam_size) {
      Hack::MaxTrans(forest, max_trans_beam_size);
      continue;
    }

    if (graphviz && !has_ref) forest.PrintGraphviz();

    // the following are only used if write_gradient is true!
    SparseVector<prob_t> full_exp, ref_exp, gradient;
    double log_z = 0, log_ref_z = 0;
    if (write_gradient) {
      const prob_t z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &full_exp);
      log_z = log(z);
      full_exp /= z;
    }
    if (conf.count("show_cfg_search_space"))
      HypergraphIO::WriteAsCFG(forest);
    if (has_ref) {
      if (HG::Intersect(ref, &forest)) {
        cerr << "  Constr. forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
        cerr << "  Constr. forest       (paths): " << forest.NumberOfPaths() << endl;
        if (crf_uniform_empirical) {
          cerr << "  USING UNIFORM WEIGHTS\n";
          for (int i = 0; i < forest.edges_.size(); ++i)
            forest.edges_[i].edge_prob_=prob_t::One();
        } else {
          forest.Reweight(feature_weights);
          cerr << "  Constr. VitTree: " << ViterbiFTree(forest) << endl;
        }
	if (hadoop_counters)
          cerr << "reporter:counter:UserCounters,SentencePairsParsed,1" << endl;
        if (conf.count("show_partition")) {
           const prob_t z = Inside<prob_t, EdgeProb>(forest);
           cerr << "  Contst. partition  log(Z): " << log(z) << endl;
        }
        //DumpKBest(sent_id, forest, 1000);
        if (conf.count("forest_output")) {
          ForestWriter writer(str("forest_output",conf), sent_id);
          if (FileExists(writer.fname_)) {
            cerr << "  Unioning...\n";
            Hypergraph new_hg;
            {
              ReadFile rf(writer.fname_);
              bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
              assert(succeeded);
            }
            new_hg.Union(forest);
            bool succeeded = writer.Write(new_hg, minimal_forests);
            assert(succeeded);
          } else {
            bool succeeded = writer.Write(forest, minimal_forests);
            assert(succeeded);
          }
        }
        if (aligner_mode && !output_training_vector)
          AlignerTools::WriteAlignment(smeta.GetSourceLattice(), smeta.GetReference(), forest, &cout);
        if (write_gradient) {
          const prob_t ref_z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
          ref_exp /= ref_z;
          if (crf_uniform_empirical) {
            log_ref_z = ref_exp.dot(feature_weights);
          } else {
            log_ref_z = log(ref_z);
          }
          //cerr << "      MODEL LOG Z: " << log_z << endl;
          //cerr << "  EMPIRICAL LOG Z: " << log_ref_z << endl;
          if ((log_z - log_ref_z) < kMINUS_EPSILON) {
            cerr << "DIFF. ERR! log_z < log_ref_z: " << log_z << " " << log_ref_z << endl;
            exit(1);
          }
          assert(!isnan(log_ref_z));
          ref_exp -= full_exp;
          acc_vec += ref_exp;
          acc_obj += (log_z - log_ref_z);
        }
        if (feature_expectations) {
          const prob_t z =
            InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
          ref_exp /= z;
          acc_obj += log(z);
          acc_vec += ref_exp;
        }

        if (output_training_vector) {
          acc_vec.clear_value(0);
          ++g_count;
          if (g_count % combine_size == 0) {
            if (encode_b64) {
              cout << "0\t";
              SparseVector<double> dav; ConvertSV(acc_vec, &dav);
              B64::Encode(acc_obj, dav, &cout);
              cout << endl << flush;
            } else {
              cout << "0\t**OBJ**=" << acc_obj << ';' <<  acc_vec << endl << flush;
            }
            acc_vec.clear();
            acc_obj = 0;
          }
        }
        if (conf.count("graphviz")) forest.PrintGraphviz();
      } else {
        cerr << "  REFERENCE UNREACHABLE.\n";
        if (write_gradient) {
	  if (hadoop_counters)
            cerr << "reporter:counter:UserCounters,EFParseFailed,1" << endl;
          cout << endl << flush;
	}
      }
    }
  }
  if (output_training_vector && !acc_vec.empty()) {
    if (encode_b64) {
      cout << "0\t";
      SparseVector<double> dav; ConvertSV(acc_vec, &dav);
      B64::Encode(acc_obj, dav, &cout);
      cout << endl << flush;
    } else {
      cout << "0\t**OBJ**=" << acc_obj << ';' << acc_vec << endl << flush;
    }
  }
}