// nnet3/nnet-discriminative-example.h
  
  // Copyright 2012-2015  Johns Hopkins University (author: Daniel Povey)
  //           2014-2015  Vimal Manohar
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //  http://www.apache.org/licenses/LICENSE-2.0
  //
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  #ifndef KALDI_NNET3_NNET_DISCRIMINATIVE_EXAMPLE_H_
  #define KALDI_NNET3_NNET_DISCRIMINATIVE_EXAMPLE_H_
  
  #include "nnet3/nnet-nnet.h"
  #include "nnet3/nnet-computation.h"
  #include "util/table-types.h"
  #include "nnet3/discriminative-supervision.h"
  #include "nnet3/nnet-example.h"
  #include "nnet3/nnet-example-utils.h"
  #include "hmm/posterior.h"
  #include "hmm/transition-model.h"
  
  namespace kaldi {
  namespace nnet3 {
  
  // Glossary: mmi = Maximum Mutual Information,
  //          mpfe = Minimum Phone Frame Error
  //          smbr = State-level Minimum Bayes Risk
  
  // This file relates to the creation of examples for discriminative training
  
  struct NnetDiscriminativeSupervision {
    // the name of the output in the neural net; in simple setups it
    // will just be "output".
    std::string name;
  
    // The indexes that the output corresponds to.  The size of this vector will
    // be equal to supervision.num_sequences * supervision.frames_per_sequence.
    // Be careful about the order of these indexes-- it is a little confusing.
    // The indexes in the 'index' vector are ordered as: (frame 0 of each sequence);
    // (frame 1 of each sequence); and so on.  But in the 'supervision' object,
    // the lattice contains (sequence 0; sequence 1; ...).  So reordering is needed.
    // This is done to make the code similar that for the 'chain' model.
    std::vector<Index> indexes;
  
    // The supervision object, containing the numerator and denominator
    // lattices.
    discriminative::DiscriminativeSupervision supervision;
  
    // This is a vector of per-frame weights, required to be between 0 and 1,
    // that is applied to the derivative during training (but not during model
    // combination, where the derivatives need to agree with the computed objf
    // values for the optimization code to work).  The reason for this is to more
    // exactly handle edge effects and to ensure that no frames are
    // 'double-counted'.  The order of this vector corresponds to the order of
    // the 'indexes' (i.e. all the first frames, then all the second frames,
    // etc.)
    // If this vector is empty it means we're not applying per-frame weights,
    // so it's equivalent to a vector of all ones.  This vector is written
    // to disk compactly as unsigned char.
    Vector<BaseFloat> deriv_weights;
  
    // Use default assignment operator
    NnetDiscriminativeSupervision() { }
  
    // Initialize the object from an object of type discriminative::Supervision,
    // and some extra information.
    // Note: you probably want to set 'name' to "output".
    // 'first_frame' will often be zero but you can choose (just make it
    // consistent with how you numbered your inputs), and 'frame_skip' would be 1
    // in a vanilla setup, but 3 in the case of 'chain' models
    NnetDiscriminativeSupervision(const std::string &name,
                                  const discriminative::DiscriminativeSupervision &supervision,
                                  const VectorBase<BaseFloat> &deriv_weights,
                                  int32 first_frame,
                                  int32 frame_skip);
  
    NnetDiscriminativeSupervision(const NnetDiscriminativeSupervision &other);
  
    void Write(std::ostream &os, bool binary) const;
  
    void Read(std::istream &is, bool binary);
  
    void Swap(NnetDiscriminativeSupervision *other);
  
    void CheckDim() const;
  
    bool operator == (const NnetDiscriminativeSupervision &other) const;
  };
  
  /// NnetDiscriminativeExample is like NnetExample, but specialized for
  /// sequence training.
  struct NnetDiscriminativeExample {
  
    /// 'inputs' contains the input to the network-- normally just it has just one
    /// element called "input", but there may be others (e.g. one called
    /// "ivector")...  this depends on the setup.
    std::vector<NnetIo> inputs;
  
    /// 'outputs' contains the sequence output supervision.  There will normally
    /// be just one member with name == "output".
    std::vector<NnetDiscriminativeSupervision> outputs;
  
    void Write(std::ostream &os, bool binary) const;
  
    void Read(std::istream &is, bool binary);
  
    void Swap(NnetDiscriminativeExample *other);
  
    // Compresses the input features (if not compressed)
    void Compress();
  
    NnetDiscriminativeExample() { }
  
    NnetDiscriminativeExample(const NnetDiscriminativeExample &other);
  
    bool operator == (const NnetDiscriminativeExample &other) const {
      return inputs == other.inputs && outputs == other.outputs;
    }
  };
  
  
  /// This hashing object hashes just the structural aspects of the NnetExample
  /// without looking at the value of the features.  It will be used in combining
  /// egs into batches of all similar structure.
  struct NnetDiscriminativeExampleStructureHasher {
    size_t operator () (const NnetDiscriminativeExample &eg) const noexcept ;
    // We also provide a version of this that works from pointers.
    size_t operator () (const NnetDiscriminativeExample *eg) const noexcept {
      return (*this)(*eg);
    }
  };
  
  
  /// This comparator object compares just the structural aspects of the
  /// NnetDiscriminativeExample without looking at the value of the features.
  struct NnetDiscriminativeExampleStructureCompare {
    bool operator () (const NnetDiscriminativeExample &a,
                      const NnetDiscriminativeExample &b) const;
    // We also provide a version of this that works from pointers.
    bool operator () (const NnetDiscriminativeExample *a,
                      const NnetDiscriminativeExample *b) const {
      return (*this)(*a, *b);
    }
  };
  
  
  /**
    Appends the given vector of examples (which must be non-empty) into
    a single output example.
    Intended to be used when forming minibatches for neural net training. If
    'compress' it compresses the output features (recommended to save disk
    space).
  
    Note: the input is left as it was at the start, but it is temporarily
    changed inside the function; this is a trick to allow us to use the
    MergeExamples() routine while avoiding having to rewrite code.
  */
  void MergeDiscriminativeExamples(
      std::vector<NnetDiscriminativeExample> *input,
      bool compress,
      NnetDiscriminativeExample *output);
  
  // called from MergeDiscriminativeExamples, this function merges the Supervision
  // objects into one.  Requires (and checks) that they all have the same name.
  void MergeSupervision(
      const std::vector<const NnetDiscriminativeSupervision*> &inputs,
      NnetDiscriminativeSupervision *output);
  
  
  /** Shifts the time-index t of everything in the input of "eg" by adding
      "t_offset" to all "t" values-- but excluding those with names listed in
      "exclude_names", e.g.  "ivector".  This might be useful if you are doing
      subsampling of frames at the output, because shifted examples won't be quite
      equivalent to their non-shifted counterparts.  "exclude_names" is a vector
      of names of nnet inputs that we avoid shifting the "t" values of-- normally
      it will contain just the single string "ivector" because we always leave t=0
      for any ivector.
  
      Note: input features will be shifted by 'frame_shift', and indexes in the
      supervision in (eg->output) will be shifted by 'frame_shift' rounded to the
      closest multiple of the frame subsampling factor (e.g. 3).  The frame
      subsampling factor is worked out from the time spacing between the indexes
      in the output.  */
  void ShiftDiscriminativeExampleTimes(int32 frame_shift,
                                      const std::vector<std::string> &exclude_names,
                                      NnetDiscriminativeExample *eg);
  
  /**  This function takes a NnetDiscriminativeExample and produces a
       ComputationRequest.
       Assumes you don't want the derivatives w.r.t. the inputs; if you do, you
       can create the ComputationRequest manually.  Assumes that if
       need_model_derivative is true, you will be supplying derivatives w.r.t. all
       outputs.
  */
  void GetDiscriminativeComputationRequest(const Nnet &nnet,
                                           const NnetDiscriminativeExample &eg,
                                           bool need_model_derivative,
                                           bool store_component_stats,
                                           bool use_xent_regularization,
                                           bool use_xent_derivative,
                                           ComputationRequest *computation_request);
  
  typedef TableWriter<KaldiObjectHolder<NnetDiscriminativeExample > > NnetDiscriminativeExampleWriter;
  typedef SequentialTableReader<KaldiObjectHolder<NnetDiscriminativeExample > > SequentialNnetDiscriminativeExampleReader;
  typedef RandomAccessTableReader<KaldiObjectHolder<NnetDiscriminativeExample > > RandomAccessNnetDiscriminativeExampleReader;
  
  
  /// This function returns the 'size' of a discriminative example as defined for
  /// purposes of merging egs, which is defined as the largest number of Indexes
  /// in any of the inputs or outputs of the example.
  int32 GetDiscriminativeNnetExampleSize(const NnetDiscriminativeExample &a);
  
  
  /// This class is responsible for arranging examples in groups that have the
  /// same strucure (i.e. the same input and output indexes), and outputting them
  /// in suitable minibatches as defined by ExampleMergingConfig.
  class DiscriminativeExampleMerger {
   public:
    DiscriminativeExampleMerger(const ExampleMergingConfig &config,
                                NnetDiscriminativeExampleWriter *writer);
  
    // This function accepts an example, and if possible, writes a merged example
    // out.  The ownership of the pointer 'a' is transferred to this class when
    // you call this function.
    void AcceptExample(NnetDiscriminativeExample *a);
  
    // This function announces to the class that the input has finished, so it
    // should flush out any smaller-sized minibatches, as dictated by the config.
    // This will be called in the destructor, but you can call it explicitly when
    // all the input is done if you want to; it won't repeat anything if called
    // twice.  It also prints the stats.
    void Finish();
  
    // returns a suitable exit status for a program.
    int32 ExitStatus() { Finish(); return (num_egs_written_ > 0 ? 0 : 1); }
  
    ~DiscriminativeExampleMerger() { Finish(); };
   private:
    // called by Finish() and AcceptExample().  Merges, updates the stats, and
    // writes.  The 'egs' is non-const only because the egs are temporarily
    // changed inside MergeDiscriminativeEgs.  The pointer 'egs' is still owned
    // by the caller.
    void WriteMinibatch(std::vector<NnetDiscriminativeExample> *egs);
  
    bool finished_;
    int32 num_egs_written_;
    const ExampleMergingConfig &config_;
    NnetDiscriminativeExampleWriter *writer_;
    ExampleMergingStats stats_;
  
    // Note: the "key" into the egs is the first element of the vector.
    typedef unordered_map<NnetDiscriminativeExample*,
                          std::vector<NnetDiscriminativeExample*>,
                          NnetDiscriminativeExampleStructureHasher,
                          NnetDiscriminativeExampleStructureCompare> MapType;
     MapType eg_to_egs_;
  };
  
  
  } // namespace nnet3
  } // namespace kaldi
  
  #endif // KALDI_NNET3_NNET_DISCRIMINATIVE_EXAMPLE_H_