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#include <iostream>
#include <vector>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "weights.h"
#include "rule_lexer.h"
#include "trule.h"
#include "filelib.h"
#include "tdict.h"
#include "lm/model.hh"
#include "lm/enumerate_vocab.hh"
#include "wordid.h"
namespace po = boost::program_options;
using namespace std;
vector<lm::WordIndex> word_map;
lm::ngram::ProbingModel* ngram;
struct VMapper : public lm::ngram::EnumerateVocab {
VMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
void Add(lm::WordIndex index, const StringPiece &str) {
const WordID cdec_id = TD::Convert(str.as_string());
if (cdec_id >= out_->size())
out_->resize(cdec_id + 1, kLM_UNKNOWN_TOKEN);
(*out_)[cdec_id] = index;
}
vector<lm::WordIndex>* out_;
const lm::WordIndex kLM_UNKNOWN_TOKEN;
};
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("source_lm,l",po::value<string>(),"Source language LM (KLM)")
("collapse_weights,w",po::value<string>(), "Collapse weights into a single feature X using the coefficients from this weights file")
("clear_features_after_collapse,c", "After collapse_weights, clear the features except for X")
("add_shape_types,s", "Add rule shape types")
("extra_lex_feature,x", "Experimental nonlinear lexical weighting feature")
("replace_files,r", "Replace files with transformed variants (requires loading full grammar into memory)")
("grammar,g", po::value<vector<string> >(), "Input (also output) grammar file(s)");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
("help,h", "Print this help message and exit");
po::options_description dconfig_options, dcmdline_options;
po::positional_options_description p;
p.add("grammar", -1);
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
po::store(po::command_line_parser(argc, argv).options(dcmdline_options).positional(p).run(), *conf);
if (conf->count("config")) {
ifstream config((*conf)["config"].as<string>().c_str());
po::store(po::parse_config_file(config, dconfig_options), *conf);
}
po::notify(*conf);
if (conf->count("help") || conf->count("grammar")==0) {
cerr << "Usage " << argv[0] << " [OPTIONS] file.scfg [file2.scfg...]\n";
cerr << dcmdline_options << endl;
return false;
}
return true;
}
lm::WordIndex kSOS;
template <class Model> float Score(const vector<WordID>& str, const Model &model) {
typename Model::State state, out;
lm::FullScoreReturn ret;
float total = 0.0f;
state = model.NullContextState();
for (int i = 0; i < str.size(); ++i) {
lm::WordIndex vocab = ((str[i] < word_map.size() && str[i] > 0) ? word_map[str[i]] : 0);
if (vocab == kSOS) {
state = model.BeginSentenceState();
} else {
ret = model.FullScore(state, vocab, out);
total += ret.prob;
state = out;
}
}
return total;
}
bool extra_feature;
int kSrcLM;
vector<double> col_weights;
bool gather_rules;
bool clear_features = false;
vector<TRulePtr> rules;
static void RuleHelper(const TRulePtr& new_rule, const unsigned int ctf_level, const TRulePtr& coarse_rule, void* extra) {
static const int kSrcLM = FD::Convert("SrcLM");
static const int kPC = FD::Convert("PC");
static const int kX = FD::Convert("X");
static const int kPhraseModel2 = FD::Convert("PhraseModel_1");
static const int kNewLex = FD::Convert("NewLex");
TRulePtr r; r.reset(new TRule(*new_rule));
if (ngram) r->scores_.set_value(kSrcLM, Score(r->f_, *ngram));
r->scores_.set_value(kPC, 1.0);
if (extra_feature) {
float v = r->scores_.value(kPhraseModel2);
r->scores_.set_value(kNewLex, v*(v+1));
}
if (col_weights.size()) {
double score = r->scores_.dot(col_weights);
if (clear_features) r->scores_.clear();
r->scores_.set_value(kX, score);
}
if (gather_rules) {
rules.push_back(r);
} else {
cout << *r << endl;
}
}
int main(int argc, char** argv) {
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
if (conf.count("source_lm")) {
lm::ngram::Config kconf;
VMapper vm(&word_map);
kconf.enumerate_vocab = &vm;
ngram = new lm::ngram::ProbingModel(conf["source_lm"].as<string>().c_str(), kconf);
kSOS = word_map[TD::Convert("<s>")];
cerr << "Loaded " << (int)ngram->Order() << "-gram KenLM (MapSize=" << word_map.size() << ")\n";
cerr << " <s> = " << kSOS << endl;
} else { ngram = NULL; }
extra_feature = conf.count("extra_lex_feature") > 0;
if (conf.count("collapse_weights")) {
Weights w;
w.InitFromFile(conf["collapse_weights"].as<string>());
w.InitVector(&col_weights);
}
clear_features = conf.count("clear_features_after_collapse") > 0;
gather_rules = false;
bool replace_files = conf.count("replace_files");
if (replace_files) gather_rules = true;
vector<string> files = conf["grammar"].as<vector<string> >();
for (int i=0; i < files.size(); ++i) {
cerr << "Processing " << files[i] << " ..." << endl;
if (true) {
ReadFile rf(files[i]);
rules.clear();
RuleLexer::ReadRules(rf.stream(), &RuleHelper, NULL);
}
if (replace_files) {
WriteFile wf(files[i]);
for (int i = 0; i < rules.size(); ++i) { (*wf.stream()) << *rules[i] << endl; }
}
}
return 0;
}
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