#include "score.h"

namespace dtrain
{


/*
 * bleu
 *
 * as in "BLEU: a Method for Automatic Evaluation
 *        of Machine Translation"
 * (Papineni et al. '02)
 *
 * NOTE: 0 if for one n \in {1..N} count is 0
 */
score_t
BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len)
{
  if (hyp_len == 0 || ref_len == 0) return 0;
  unsigned M = N_;
  if (ref_len < N_) M = ref_len;
  score_t sum = 0;
  for (unsigned i = 0; i < M; i++) {
    if (counts.clipped[i] == 0 || counts.sum[i] == 0) return 0;
    sum += w_[i] * log((score_t)counts.clipped[i]/counts.sum[i]);
  }
  return brevity_penalty(hyp_len, ref_len) * exp(sum);
}

score_t
BleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
                  const unsigned /*rank*/)
{
  unsigned hyp_len = hyp.size(), ref_len = ref.size();
  if (hyp_len == 0 || ref_len == 0) return 0;
  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
  return Bleu(counts, hyp_len, ref_len);
}

/*
 * 'stupid' bleu
 *
 * as in "ORANGE: a Method for Evaluating
 *        Automatic Evaluation Metrics
 *        for Machine Translation"
 * (Lin & Och '04)
 *
 * NOTE: 0 iff no 1gram match
 */
score_t
StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
                        const unsigned /*rank*/)
{
  unsigned hyp_len = hyp.size(), ref_len = ref.size();
  if (hyp_len == 0 || ref_len == 0) return 0;
  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
  unsigned M = N_;
  if (ref_len < N_) M = ref_len;
  score_t sum = 0, add = 0;
  for (unsigned i = 0; i < M; i++) {
    if (i == 0 && (counts.clipped[i] == 0 || counts.sum[i] == 0)) return 0;
    if (i == 1) add = 1;
    sum += w_[i] * log(((score_t)counts.clipped[i] + add)/((counts.sum[i] + add)));
  }
  return  brevity_penalty(hyp_len, ref_len) * exp(sum);
}

/*
 * smooth bleu
 *
 * as in "An End-to-End Discriminative Approach
 *        to Machine Translation"
 * (Liang et al. '06)
 *
 * NOTE: max is 0.9375
 */
score_t
SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
                        const unsigned /*rank*/)
{
  unsigned hyp_len = hyp.size(), ref_len = ref.size();
  if (hyp_len == 0 || ref_len == 0) return 0;
  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
  unsigned M = N_;
  if (ref_len < N_) M = ref_len;
  score_t sum = 0.;
  vector<score_t> i_bleu;
  for (unsigned i = 0; i < M; i++) i_bleu.push_back(0.);
  for (unsigned i = 0; i < M; i++) {
    if (counts.clipped[i] == 0 || counts.sum[i] == 0) {
      break;
    } else {
      score_t i_ng = log((score_t)counts.clipped[i]/counts.sum[i]);
      for (unsigned j = i; j < M; j++) {
        i_bleu[j] += (1/((score_t)j+1)) * i_ng;
      }
    }
    sum += exp(i_bleu[i])/(pow(2, N_-i));
  }
  return brevity_penalty(hyp_len, ref_len) * sum;
}

/*
 * approx. bleu
 *
 * as in "Online Large-Margin Training of Syntactic
 *        and Structural Translation Features"
 * (Chiang et al. '08)
 *
 * NOTE: needs some code in dtrain.cc
 */
score_t
ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
                        const unsigned rank)
{
  unsigned hyp_len = hyp.size(), ref_len = ref.size();
  if (hyp_len == 0 || ref_len == 0) return 0;
  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
  NgramCounts tmp(N_);
  if (rank == 0) { // 'context of 1best translations'
    glob_onebest_counts += counts;
    glob_hyp_len += hyp_len;
    glob_ref_len += ref_len;
    hyp_len = glob_hyp_len;
    ref_len = glob_ref_len;
    tmp = glob_onebest_counts;
  } else {
    hyp_len = hyp.size();
    ref_len = ref.size();
    tmp = glob_onebest_counts + counts;
  }
  return 0.9 * Bleu(tmp, hyp_len, ref_len); // TODO param
}


} // namespace