// $Id$

/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
***********************************************************************/

#ifndef moses_HypothesisStackNormal_h
#define moses_HypothesisStackNormal_h

#include <limits>
#include <set>
#include "Hypothesis.h"
#include "HypothesisStack.h"
#include "WordsBitmap.h"

namespace Moses
{
// class WordsBitmap;
// typedef size_t WordsBitmapID;

/** A stack for standard phrase-based decoding. */
class HypothesisStackNormal: public HypothesisStack
{
public:
  friend std::ostream& operator<<(std::ostream&, const HypothesisStackNormal&);

protected:
  float m_bestScore; /**< score of the best hypothesis in collection */
  float m_worstScore; /**< score of the worse hypothesis in collection */
  std::map< WordsBitmapID, float > m_diversityWorstScore; /**< score of worst hypothesis for particular source word coverage */
  float m_beamWidth; /**< minimum score due to threashold pruning */
  size_t m_maxHypoStackSize; /**< maximum number of hypothesis allowed in this stack */
  size_t m_minHypoStackDiversity; /**< minimum number of hypothesis with different source word coverage */
  bool m_nBestIsEnabled; /**< flag to determine whether to keep track of old arcs */

  /** add hypothesis to stack. Prune if necessary.
   * Returns false if equiv hypo exists in collection, otherwise returns true
   */
  std::pair<HypothesisStackNormal::iterator, bool> Add(Hypothesis *hypothesis);

  /** destroy all instances of Hypothesis in this collection */
  void RemoveAll();

  void SetWorstScoreForBitmap( WordsBitmapID id, float worstScore ) {
    m_diversityWorstScore[ id ] = worstScore;
  }

public:
  float GetWorstScoreForBitmap( WordsBitmapID id ) {
    if (m_diversityWorstScore.find( id ) == m_diversityWorstScore.end())
      return -std::numeric_limits<float>::infinity();
    return m_diversityWorstScore[ id ];
  }
  virtual float GetWorstScoreForBitmap( const WordsBitmap &coverage ) {
    return GetWorstScoreForBitmap( coverage.GetID() );
  }

  HypothesisStackNormal(Manager& manager);

  /** adds the hypo, but only if within thresholds (beamThr, stackSize).
  *	This function will recombine hypotheses silently!  There is no record
  * (could affect n-best list generation...TODO)
  * Call stack for adding hypothesis is
  		AddPrune()
  			Add()
  				AddNoPrune()
  */
  std::pair<AddStatus, const Hypothesis*> AddPrune(Hypothesis *hypothesis);

  std::pair<AddStatus, const Hypothesis*> AddNoPrune(Hypothesis *hypothesis);

  /** set maximum number of hypotheses in the collection
   * \param maxHypoStackSize maximum number (typical number: 100)
   * \param maxHypoStackSize maximum number (defauly: 0)
   */
  inline void SetMaxHypoStackSize(size_t maxHypoStackSize, size_t minHypoStackDiversity) {
    m_maxHypoStackSize = maxHypoStackSize;
    m_minHypoStackDiversity = minHypoStackDiversity;
  }

  /** set beam threshold, hypotheses in the stack must not be worse than
   * this factor times the best score to be allowed in the stack
   * \param beamThreshold minimum factor (typical number: 0.03)
   */
  inline void SetBeamWidth(float beamWidth) {
    m_beamWidth = beamWidth;
  }
  /** return score of the best hypothesis in the stack */
  inline float GetBestScore() const {
    return m_bestScore;
  }
  /** return worst allowable score */
  inline float GetWorstScore() const {
    return m_worstScore;
  }

  /** pruning, if too large.
   * Pruning algorithm: find a threshold and delete all hypothesis below it.
   * The threshold is chosen so that exactly newSize top items remain on the
   * stack in fact, in situations where some of the hypothesis fell below
   * m_beamWidth, the stack will contain less items.
   * \param newSize maximum size */
  void PruneToSize(size_t newSize);

  //! return the hypothesis with best score. Used to get the translated at end of decoding
  const Hypothesis *GetBestHypothesis() const;
  //! return all hypothesis, sorted by descending score. Used in creation of N best list
  std::vector<const Hypothesis*> GetSortedList() const;
  std::vector<Hypothesis*> GetSortedListNOTCONST();

  /** make all arcs in point to the equiv hypothesis that contains them.
  * Ie update doubly linked list be hypo & arcs
  */
  void CleanupArcList();

  TO_STRING();
};

}
#endif