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#include <iostream>
#include <vector>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#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)")
("add_shape_types,s", "Add rule shape types");
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;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
po::store(parse_command_line(argc, argv, dcmdline_options), *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")) {
cerr << "Usage " << argv[0] << " [OPTIONS]\n";
cerr << dcmdline_options << endl;
return false;
}
return true;
}
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);
ret = model.FullScore(state, vocab, out);
total += ret.prob;
state = out;
}
return total;
}
static void RuleHelper(const TRulePtr& new_rule, const unsigned int ctf_level, const TRulePtr& coarse_rule, void* extra) {
cout << *new_rule << " SrcLM=" << Score(new_rule->f_, *ngram) << 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);
cerr << "Loaded " << (int)ngram->Order() << "-gram KenLM (MapSize=" << word_map.size() << ")\n";
} else { ngram = NULL; }
assert(ngram);
RuleLexer::ReadRules(&cin, &RuleHelper, NULL);
return 0;
}
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