/*
Copyright (c) by respective owners including Yahoo!, Microsoft, and
individual contributors. All rights reserved.  Released under a BSD (revised)
license as described in the file LICENSE.
 */
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <sstream>

#include "reductions.h"
#include "constant.h"
#include "simple_label.h"
#include "rand48.h"
#include "gd.h"

using namespace std;
using namespace LEARNER;

namespace NN {
  const float hidden_min_activation = -3;
  const float hidden_max_activation = 3;
  const int nn_constant = 533357803;
  
  struct nn {
    uint32_t k;
    loss_function* squared_loss;
    example output_layer;
    float prediction;
    size_t increment;
    bool dropout;
    uint64_t xsubi;
    uint64_t save_xsubi;
    bool inpass;
    bool finished_setup;

    vw* all;
  };

#define cast_uint32_t static_cast<uint32_t>

  static inline float
  fastpow2 (float p)
  {
    float offset = (p < 0) ? 1.0f : 0.0f;
    float clipp = (p < -126) ? -126.0f : p;
    int w = (int)clipp;
    float z = clipp - w + offset;
    union { uint32_t i; float f; } v = { cast_uint32_t ( (1 << 23) * (clipp + 121.2740575f + 27.7280233f / (4.84252568f - z) - 1.49012907f * z) ) };

    return v.f;
  }

  static inline float
  fastexp (float p)
  {
    return fastpow2 (1.442695040f * p);
  }

  static inline float
  fasttanh (float p)
  {
    return -1.0f + 2.0f / (1.0f + fastexp (-2.0f * p));
  }

  void finish_setup (nn& n, vw& all)
  {
    // TODO: output_layer audit

    memset (&n.output_layer, 0, sizeof (n.output_layer));
    n.output_layer.indices.push_back(nn_output_namespace);
    feature output = {1., nn_constant << all.reg.stride_shift};

    for (unsigned int i = 0; i < n.k; ++i)
      {
        n.output_layer.atomics[nn_output_namespace].push_back(output);
        ++n.output_layer.num_features;
        output.weight_index += (uint32_t)n.increment;
      }

    if (! n.inpass) 
      {
        n.output_layer.atomics[nn_output_namespace].push_back(output);
        ++n.output_layer.num_features;
      }

    n.output_layer.in_use = true;

    n.finished_setup = true;
  }

  void end_pass(nn& n)
  {
    if (n.all->bfgs)
      n.xsubi = n.save_xsubi;
  }

  template <bool is_learn>
  void predict_or_learn(nn& n, learner& base, example& ec)
  {
    bool shouldOutput = n.all->raw_prediction > 0;

    if (! n.finished_setup)
      finish_setup (n, *(n.all));

    label_data* ld = (label_data*)ec.ld;
    float save_label = ld->label;
    void (*save_set_minmax) (shared_data*, float) = n.all->set_minmax;
    float save_min_label;
    float save_max_label;
    float dropscale = n.dropout ? 2.0f : 1.0f;
    loss_function* save_loss = n.all->loss;

    float* hidden_units = (float*) alloca (n.k * sizeof (float));
    bool* dropped_out = (bool*) alloca (n.k * sizeof (bool));
  
    string outputString;
    stringstream outputStringStream(outputString);

    n.all->set_minmax = noop_mm;
    n.all->loss = n.squared_loss;
    save_min_label = n.all->sd->min_label;
    n.all->sd->min_label = hidden_min_activation;
    save_max_label = n.all->sd->max_label;
    n.all->sd->max_label = hidden_max_activation;
    //ld->label = FLT_MAX;
    for (unsigned int i = 0; i < n.k; ++i)
      {
        uint32_t biasindex = (uint32_t) constant * (n.all->wpp << n.all->reg.stride_shift) + i * (uint32_t)n.increment + ec.ft_offset;
        weight* w = &n.all->reg.weight_vector[biasindex & n.all->reg.weight_mask];
        
        // avoid saddle point at 0
        if (*w == 0)
          {
            w[0] = (float) (frand48 () - 0.5);

            if (n.dropout && n.all->normalized_updates)
              w[n.all->normalized_idx] = 1e-4f;
          }

	base.predict(ec, i);

        hidden_units[i] = ld->prediction;

        dropped_out[i] = (n.dropout && merand48 (n.xsubi) < 0.5);

        if (shouldOutput) {
          if (i > 0) outputStringStream << ' ';
          outputStringStream << i << ':' << ec.partial_prediction << ',' << fasttanh (hidden_units[i]);
        }
      }
    //ld->label = save_label;
    n.all->loss = save_loss;
    n.all->set_minmax = save_set_minmax;
    n.all->sd->min_label = save_min_label;
    n.all->sd->max_label = save_max_label;

    bool converse = false;
    float save_partial_prediction = 0;
    float save_final_prediction = 0;
    float save_ec_loss = 0;

CONVERSE: // That's right, I'm using goto.  So sue me.

    n.output_layer.total_sum_feat_sq = 1;
    n.output_layer.sum_feat_sq[nn_output_namespace] = 1;

    for (unsigned int i = 0; i < n.k; ++i)
      {
        float sigmah = 
          (dropped_out[i]) ? 0.0f : dropscale * fasttanh (hidden_units[i]);
        n.output_layer.atomics[nn_output_namespace][i].x = sigmah;

        n.output_layer.total_sum_feat_sq += sigmah * sigmah;
        n.output_layer.sum_feat_sq[nn_output_namespace] += sigmah * sigmah;

        uint32_t nuindex = n.output_layer.atomics[nn_output_namespace][i].weight_index + (n.k * (uint32_t)n.increment) + ec.ft_offset;
        weight* w = &n.all->reg.weight_vector[nuindex & n.all->reg.weight_mask];
        
        // avoid saddle point at 0
        if (*w == 0)
          {
            float sqrtk = sqrt ((float)n.k);
            w[0] = (float) (frand48 () - 0.5) / sqrtk;

            if (n.dropout && n.all->normalized_updates)
              w[n.all->normalized_idx] = 1e-4f;
          }
      }

    if (n.inpass) {
      // TODO: this is not correct if there is something in the 
      // nn_output_namespace but at least it will not leak memory
      // in that case

      ec.indices.push_back (nn_output_namespace);
      v_array<feature> save_nn_output_namespace = ec.atomics[nn_output_namespace];
      ec.atomics[nn_output_namespace] = n.output_layer.atomics[nn_output_namespace];
      ec.sum_feat_sq[nn_output_namespace] = n.output_layer.sum_feat_sq[nn_output_namespace];
      ec.total_sum_feat_sq += n.output_layer.sum_feat_sq[nn_output_namespace];
      if (is_learn)
	base.learn(ec, n.k);
      else
	base.predict(ec, n.k);
      n.output_layer.partial_prediction = ec.partial_prediction;
      n.output_layer.loss = ec.loss;
      ec.total_sum_feat_sq -= n.output_layer.sum_feat_sq[nn_output_namespace];
      ec.sum_feat_sq[nn_output_namespace] = 0;
      ec.atomics[nn_output_namespace] = save_nn_output_namespace;
      ec.indices.pop ();
    }
    else {
      n.output_layer.ft_offset = ec.ft_offset;
      n.output_layer.ld = ec.ld;
      n.output_layer.partial_prediction = 0;
      n.output_layer.eta_round = ec.eta_round;
      n.output_layer.eta_global = ec.eta_global;
      n.output_layer.example_t = ec.example_t;
      if (is_learn)
	base.learn(n.output_layer, n.k);
      else
	base.predict(n.output_layer, n.k);
      n.output_layer.ld = 0;
    }

    n.prediction = GD::finalize_prediction (*(n.all), n.output_layer.partial_prediction);

    if (shouldOutput) {
      outputStringStream << ' ' << n.output_layer.partial_prediction;
      n.all->print_text(n.all->raw_prediction, outputStringStream.str(), ec.tag);
    }

    if (is_learn && n.all->training && ld->label != FLT_MAX) {
      float gradient = n.all->loss->first_derivative(n.all->sd, 
                                                  n.prediction,
                                                  ld->label);

      if (fabs (gradient) > 0) {
        n.all->loss = n.squared_loss;
        n.all->set_minmax = noop_mm;
        save_min_label = n.all->sd->min_label;
        n.all->sd->min_label = hidden_min_activation;
        save_max_label = n.all->sd->max_label;
        n.all->sd->max_label = hidden_max_activation;

        for (unsigned int i = 0; i < n.k; ++i) {
          if (! dropped_out[i]) {
            float sigmah = 
              n.output_layer.atomics[nn_output_namespace][i].x / dropscale;
            float sigmahprime = dropscale * (1.0f - sigmah * sigmah);
            uint32_t nuindex = n.output_layer.atomics[nn_output_namespace][i].weight_index + (n.k * (uint32_t)n.increment) + ec.ft_offset;
            float nu = n.all->reg.weight_vector[nuindex & n.all->reg.weight_mask];
            float gradhw = 0.5f * nu * gradient * sigmahprime;

            ld->label = GD::finalize_prediction (*(n.all), hidden_units[i] - gradhw);
            if (ld->label != hidden_units[i]) 
              base.learn(ec, i);
          }
        }

        n.all->loss = save_loss;
        n.all->set_minmax = save_set_minmax;
        n.all->sd->min_label = save_min_label;
        n.all->sd->max_label = save_max_label;
      }
    }

    ld->label = save_label;

    if (! converse) {
      save_partial_prediction = n.output_layer.partial_prediction;
      save_final_prediction = n.prediction;
      save_ec_loss = n.output_layer.loss;
    }

    if (n.dropout && ! converse)
      {
        for (unsigned int i = 0; i < n.k; ++i)
          {
            dropped_out[i] = ! dropped_out[i];
          }

        converse = true;
        goto CONVERSE;
      }

    ec.partial_prediction = save_partial_prediction;
    ld->prediction = save_final_prediction;
    ec.loss = save_ec_loss;
  }

  void finish_example(vw& all, nn&, example& ec)
  {
    int save_raw_prediction = all.raw_prediction;
    all.raw_prediction = -1;
    return_simple_example(all, NULL, ec);
    all.raw_prediction = save_raw_prediction;
  }

  void finish(nn& n)
  {
    delete n.squared_loss;
    free (n.output_layer.indices.begin);
    free (n.output_layer.atomics[nn_output_namespace].begin);
  }

  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
  {
    nn* n = (nn*)calloc_or_die(1,sizeof(nn));
    n->all = &all;

    po::options_description desc("NN options");
    desc.add_options()
      ("inpass", "Train or test sigmoidal feedforward network with input passthrough.")
      ("dropout", "Train or test sigmoidal feedforward network using dropout.")
      ("meanfield", "Train or test sigmoidal feedforward network using mean field.");

    po::parsed_options parsed = po::command_line_parser(opts).
      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
      options(desc).allow_unregistered().run();
    opts = po::collect_unrecognized(parsed.options, po::include_positional);
    po::store(parsed, vm);
    po::notify(vm);

    po::parsed_options parsed_file = po::command_line_parser(all.options_from_file_argc,all.options_from_file_argv).
      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
      options(desc).allow_unregistered().run();
    po::store(parsed_file, vm_file);
    po::notify(vm_file);

    //first parse for number of hidden units
    n->k = 0;
    if( vm_file.count("nn") ) {
      n->k = (uint32_t)vm_file["nn"].as<size_t>();
      if( vm.count("nn") && (uint32_t)vm["nn"].as<size_t>() != n->k )
        std::cerr << "warning: you specified a different number of hidden units through --nn than the one loaded from predictor. Pursuing with loaded value of: " << n->k << endl;
    }
    else {
      n->k = (uint32_t)vm["nn"].as<size_t>();

      std::stringstream ss;
      ss << " --nn " << n->k;
      all.options_from_file.append(ss.str());
    }

    if( vm_file.count("dropout") ) {
      n->dropout = all.training || vm.count("dropout");

      if (! n->dropout && ! vm.count("meanfield") && ! all.quiet) 
        std::cerr << "using mean field for testing, specify --dropout explicitly to override" << std::endl;
    }
    else if ( vm.count("dropout") ) {
      n->dropout = true;

      std::stringstream ss;
      ss << " --dropout ";
      all.options_from_file.append(ss.str());
    }

    if ( vm.count("meanfield") ) {
      n->dropout = false;
      if (! all.quiet) 
        std::cerr << "using mean field for neural network " 
                  << (all.training ? "training" : "testing") 
                  << std::endl;
    }

    if (n->dropout) 
      if (! all.quiet)
        std::cerr << "using dropout for neural network "
                  << (all.training ? "training" : "testing") 
                  << std::endl;

    if( vm_file.count("inpass") ) {
      n->inpass = true;
    }
    else if (vm.count ("inpass")) {
      n->inpass = true;

      std::stringstream ss;
      ss << " --inpass";
      all.options_from_file.append(ss.str());
    }

    if (n->inpass && ! all.quiet)
      std::cerr << "using input passthrough for neural network "
                << (all.training ? "training" : "testing") 
                << std::endl;

    n->finished_setup = false;
    n->squared_loss = getLossFunction (0, "squared", 0);

    n->xsubi = 0;

    if (vm.count("random_seed"))
      n->xsubi = vm["random_seed"].as<size_t>();

    n->save_xsubi = n->xsubi;
    n->increment = all.l->increment;//Indexing of output layer is odd.
    learner* l = new learner(n,  all.l, n->k+1);
    l->set_learn<nn, predict_or_learn<true> >();
    l->set_predict<nn, predict_or_learn<false> >();
    l->set_finish<nn, finish>();
    l->set_finish_example<nn, finish_example>();
    l->set_end_pass<nn,end_pass>();

    return l;
  }
}