#ifndef PYSTAN__STAN_FIT_HPP
#define PYSTAN__STAN_FIT_HPP

#include <iomanip>
#include <fstream>
#include <sstream>
#include <stdexcept>

#include <stan/version.hpp>
// #include <stan/io/cmd_line.hpp>
// #include <stan/io/dump.hpp>

#include <boost/date_time/posix_time/posix_time_types.hpp>
#include <boost/math/special_functions/fpclassify.hpp>
#include <boost/random/additive_combine.hpp> // L'Ecuyer RNG
#include <boost/random/uniform_real_distribution.hpp>

#include <stan/model/util.hpp>

#include <stan/mcmc/base_adaptation.hpp>
#include <stan/mcmc/base_adapter.hpp>
#include <stan/mcmc/base_mcmc.hpp>
#include <stan/mcmc/covar_adaptation.hpp>
#include <stan/mcmc/fixed_param_sampler.hpp>
#include <stan/mcmc/hmc/base_hmc.hpp>
#include <stan/mcmc/hmc/hamiltonians/base_hamiltonian.hpp>
#include <stan/mcmc/hmc/hamiltonians/dense_e_metric.hpp>
#include <stan/mcmc/hmc/hamiltonians/dense_e_point.hpp>
#include <stan/mcmc/hmc/hamiltonians/diag_e_metric.hpp>
#include <stan/mcmc/hmc/hamiltonians/diag_e_point.hpp>
#include <stan/mcmc/hmc/hamiltonians/ps_point.hpp>
#include <stan/mcmc/hmc/hamiltonians/unit_e_metric.hpp>
#include <stan/mcmc/hmc/hamiltonians/unit_e_point.hpp>
#include <stan/mcmc/hmc/integrators/base_integrator.hpp>
#include <stan/mcmc/hmc/integrators/base_leapfrog.hpp>
#include <stan/mcmc/hmc/integrators/expl_leapfrog.hpp>
#include <stan/mcmc/hmc/nuts/adapt_dense_e_nuts.hpp>
#include <stan/mcmc/hmc/nuts/adapt_diag_e_nuts.hpp>
#include <stan/mcmc/hmc/nuts/adapt_unit_e_nuts.hpp>
#include <stan/mcmc/hmc/nuts/base_nuts.hpp>
#include <stan/mcmc/hmc/nuts/dense_e_nuts.hpp>
#include <stan/mcmc/hmc/nuts/diag_e_nuts.hpp>
#include <stan/mcmc/hmc/nuts/unit_e_nuts.hpp>
#include <stan/mcmc/hmc/static/adapt_dense_e_static_hmc.hpp>
#include <stan/mcmc/hmc/static/adapt_diag_e_static_hmc.hpp>
#include <stan/mcmc/hmc/static/adapt_unit_e_static_hmc.hpp>
#include <stan/mcmc/hmc/static/base_static_hmc.hpp>
#include <stan/mcmc/hmc/static/dense_e_static_hmc.hpp>
#include <stan/mcmc/hmc/static/diag_e_static_hmc.hpp>
#include <stan/mcmc/hmc/static/unit_e_static_hmc.hpp>
#include <stan/mcmc/sample.hpp>
#include <stan/mcmc/stepsize_adaptation.hpp>
#include <stan/mcmc/stepsize_adapter.hpp>
#include <stan/mcmc/stepsize_covar_adapter.hpp>
#include <stan/mcmc/stepsize_var_adapter.hpp>
#include <stan/mcmc/var_adaptation.hpp>
#include <stan/mcmc/windowed_adaptation.hpp>

#include <stan/optimization/newton.hpp>
#include <stan/optimization/bfgs.hpp>

#include <stan/services/io/do_print.hpp>
#include <stan/services/io/write_error_msg.hpp>
#include <stan/services/io/write_iteration.hpp>
#include <stan/services/io/write_model.hpp>
#include <stan/services/io/write_stan.hpp>
#include <stan/services/init/initialize_state.hpp>
#include <stan/services/mcmc/sample.hpp>
#include <stan/services/mcmc/warmup.hpp>
#include <stan/services/optimize/do_bfgs_optimize.hpp>
#include <stan/services/sample/init_adapt.hpp>
#include <stan/services/sample/init_nuts.hpp>
#include <stan/services/sample/init_static_hmc.hpp>
#include <stan/services/sample/init_windowed_adapt.hpp>
#include <stan/services/sample/mcmc_writer.hpp>
#include <stan/services/sample/progress.hpp>

#include <stan/interface_callbacks/writer/noop_writer.hpp>
#include <stan/interface_callbacks/writer/base_writer.hpp>
#include <stan/interface_callbacks/writer/stream_writer.hpp>

#include <stan/variational/advi.hpp>

#include "py_var_context.hpp"
#include "py_var_context_factory.hpp"
#include "Python.h"
#ifndef Py_PYTHON_H
    #error Python headers needed to compile C extensions, please install development version of Python.
#endif


#include "pystan_writer.hpp"


typedef std::map<std::string, std::pair<std::vector<double>, std::vector<size_t> > > vars_r_t;
typedef std::map<std::string, std::pair<std::vector<int>, std::vector<size_t> > > vars_i_t;

namespace pystan {

  // rstan's stan_fit.hpp adds an anonymous namespace here; pystan needs direct access to items below

  enum sampling_algo_t { NUTS = 1, HMC = 2, Metropolis = 3, Fixed_param = 4};
  enum optim_algo_t { Newton = 1, BFGS = 3, LBFGS = 4};
  enum variational_algo_t { MEANFIELD = 1, FULLRANK = 2};
  enum sampling_metric_t { UNIT_E = 1, DIAG_E = 2, DENSE_E = 3};
  enum stan_args_method_t { SAMPLING = 1, OPTIM = 2, TEST_GRADIENT = 3, VARIATIONAL = 4};

  /* Simple class to store arguments provided by Python. Mirrors RStan's stan_args.
   *
   * Apart from the StanArgs class all the functionality found in RStan's
   * stan_args.hpp such as validate_args() and the constructor is handled in
   * Python for easier debugging.
   *
   */
  class StanArgs {
  public:
    unsigned int random_seed;
    unsigned int chain_id;
    std::string init;
    /* init_vars_r and init_vars_i == RStan's init_list */
    vars_r_t init_vars_r;
    vars_i_t init_vars_i;
    double init_radius;
    bool enable_random_init;
    std::string sample_file; // the file for outputting the samples
    bool append_samples;
    bool sample_file_flag; // true: write out to a file; false, do not
    stan_args_method_t method;
    std::string diagnostic_file;
    bool diagnostic_file_flag;
    union {
      struct {
        int iter;   // number of iterations
        int refresh;  //
        sampling_algo_t algorithm;
        int warmup; // number of warmup
        int thin;
        bool save_warmup; // weather to save warmup samples (always true now)
        int iter_save; // number of iterations saved
        int iter_save_wo_warmup; // number of iterations saved wo warmup
        bool adapt_engaged;
        double adapt_gamma;
        double adapt_delta;
        double adapt_kappa;
        unsigned int adapt_init_buffer;
        unsigned int adapt_term_buffer;
        unsigned int adapt_window;
        double adapt_t0;
        sampling_metric_t metric; // UNIT_E, DIAG_E, DENSE_E;
        double stepsize; // defaut to 1;
        double stepsize_jitter;
        int max_treedepth; // for NUTS, default to 10.
        double int_time; // for HMC, default to 2 * pi
      } sampling;
      struct {
        int iter; // default to 2000
        int refresh; // default to 100
        optim_algo_t algorithm; // Newton, (L)BFGS
        bool save_iterations; // default to false
        double init_alpha; // default to 0.0001, for (L)BFGS
        double tol_obj; // default to 1e-12, for (L)BFGS
        double tol_grad; // default to 1e-8, for (L)BFGS
        double tol_param; // default to 1e-8, for (L)BFGS
        double tol_rel_obj; // default to 1e4, for (L)BFGS
        double tol_rel_grad; // default to 1e7, for (L)BFGS
        int history_size; // default to 5, for LBFGS only
      } optim;
      struct {
        int iter; // default to 10000
        variational_algo_t algorithm;  // MEANFIELD or FULLRANK
        int grad_samples; // default to 1
        int elbo_samples; // default to 100
        int eval_elbo;    // default to 100
        int output_samples; // default to 1000
        double eta; // defaults to 1.0
        bool adapt_engaged; // defaults to 1
        int adapt_iter; // defaults to 50
        double tol_rel_obj; // default to 0.01
      } variational;
      struct {
        double epsilon; // default to 1e-6, for test_grad
        double error;  // default to 1e-6, for test_grad
      } test_grad;
    } ctrl;

    inline const std::string& get_sample_file() const {
      return sample_file;
    }
    inline bool get_sample_file_flag() const {
      return sample_file_flag;
    }
    inline bool get_diagnostic_file_flag() const {
      return diagnostic_file_flag;
    }
    inline const std::string& get_diagnostic_file() const {
      return diagnostic_file;
    }

    void set_random_seed(unsigned int seed) {
      random_seed = seed;
    }

    inline unsigned int get_random_seed() const {
      return random_seed;
    }

    inline int get_ctrl_variational_grad_samples() const {
      return ctrl.variational.grad_samples;
    }
    inline int get_ctrl_variational_elbo_samples() const {
      return ctrl.variational.elbo_samples;
    }
    inline int get_ctrl_variational_output_samples() const {
      return ctrl.variational.output_samples;
    }
    inline int get_ctrl_variational_eval_elbo() const {
      return ctrl.variational.eval_elbo;
    }
    inline double get_ctrl_variational_eta() const {
      return ctrl.variational.eta;
    }
    inline bool get_ctrl_variational_adapt_engaged() const {
      return ctrl.variational.adapt_engaged;
    }
    inline double get_ctrl_variational_tol_rel_obj() const {
      return ctrl.variational.tol_rel_obj;
    }
    inline variational_algo_t get_ctrl_variational_algorithm() const {
      return ctrl.variational.algorithm;
    }
    inline int get_ctrl_variational_adapt_iter() const {
      return ctrl.variational.adapt_iter;
    }
    inline int get_ctrl_sampling_refresh() const {
      return ctrl.sampling.refresh;
    }
    inline sampling_metric_t get_ctrl_sampling_metric() const {
      return ctrl.sampling.metric;
    }
    inline sampling_algo_t get_ctrl_sampling_algorithm() const {
      return ctrl.sampling.algorithm;
    }
    inline int get_ctrl_sampling_warmup() const {
      return ctrl.sampling.warmup;
    }
    void set_ctrl_sampling_warmup(int n) {
      ctrl.sampling.warmup = n;
    }
    inline int get_ctrl_sampling_thin() const {
      return ctrl.sampling.thin;
    }
    inline double get_ctrl_sampling_int_time() const {
      return ctrl.sampling.int_time;
    }
    inline bool get_append_samples() const {
      return append_samples;
    }
    inline stan_args_method_t get_method() const {
      return method;
    }
    inline int get_iter() const {
      switch (method) {
        case SAMPLING: return ctrl.sampling.iter;
        case OPTIM: return ctrl.optim.iter;
        case VARIATIONAL: return ctrl.variational.iter;
        case TEST_GRADIENT: return 0;
      }
      return 0;
    }
    inline bool get_ctrl_sampling_adapt_engaged() const {
      return ctrl.sampling.adapt_engaged;
    }
    inline double get_ctrl_sampling_adapt_gamma() const {
      return ctrl.sampling.adapt_gamma;
    }
    inline double get_ctrl_sampling_adapt_delta() const {
      return ctrl.sampling.adapt_delta;
    }
    inline double get_ctrl_sampling_adapt_kappa() const {
      return ctrl.sampling.adapt_kappa;
    }
    inline double get_ctrl_sampling_adapt_t0() const {
      return ctrl.sampling.adapt_t0;
    }
    inline unsigned int get_ctrl_sampling_adapt_init_buffer() const {
      return ctrl.sampling.adapt_init_buffer;
    }
    inline unsigned int get_ctrl_sampling_adapt_term_buffer() const {
      return ctrl.sampling.adapt_term_buffer;
    }
    inline unsigned int get_ctrl_sampling_adapt_window() const {
      return ctrl.sampling.adapt_window;
    }
    inline double get_ctrl_sampling_stepsize() const {
       return ctrl.sampling.stepsize;
    }
    inline double get_ctrl_sampling_stepsize_jitter() const {
       return ctrl.sampling.stepsize_jitter;
    }
    inline int get_ctrl_sampling_max_treedepth() const {
       return ctrl.sampling.max_treedepth;
    }
    inline int get_ctrl_sampling_iter_save_wo_warmup() const {
       return ctrl.sampling.iter_save_wo_warmup;
    }
    inline int get_ctrl_sampling_iter_save() const {
       return ctrl.sampling.iter_save;
    }
    inline bool get_ctrl_sampling_save_warmup() const {
       return true;
    }
    inline optim_algo_t get_ctrl_optim_algorithm() const {
      return ctrl.optim.algorithm;
    }
    inline int get_ctrl_optim_refresh() const {
      return ctrl.optim.refresh;
    }
    inline bool get_ctrl_optim_save_iterations() const {
      return ctrl.optim.save_iterations;
    }
    inline double get_ctrl_optim_init_alpha() const {
      return ctrl.optim.init_alpha;
    }
    inline double get_ctrl_optim_tol_obj() const {
      return ctrl.optim.tol_obj;
    }
    inline double get_ctrl_optim_tol_grad() const {
      return ctrl.optim.tol_grad;
    }
    inline double get_ctrl_optim_tol_param() const {
      return ctrl.optim.tol_param;
    }
    inline double get_ctrl_optim_tol_rel_obj() const {
      return ctrl.optim.tol_rel_obj;
    }
    inline double get_ctrl_optim_tol_rel_grad() const {
      return ctrl.optim.tol_rel_grad;
    }
    inline int get_ctrl_optim_history_size() const {
      return ctrl.optim.history_size;
    }
    inline double get_ctrl_test_grad_epsilon() const {
      return ctrl.test_grad.epsilon;
    }
    inline double get_ctrl_test_grad_error() const {
      return ctrl.test_grad.error;
    }
    inline unsigned int get_chain_id() const {
      return chain_id;
    }
    inline double get_init_radius() const {
      return init_radius;
    }
    inline bool get_enable_random_init() const {
      return enable_random_init;
    }
    const std::string& get_init() const {
      return init;
    }

  };

  /* simple class to store data for Python */
  class StanHolder {

      public:
          int num_failed;
          bool test_grad;
          std::vector<double> inits;
          std::vector<double> par;
          double value;
          std::vector<std::vector<double> > chains;
          std::vector<std::string> chain_names;
          StanArgs args;
          std::vector<double> mean_pars;
          double mean_lp__;
          std::string adaptation_info;
          std::vector<std::vector<double> > sampler_params;
          std::vector<std::string> sampler_param_names;
  };


    /* functions from RStan's stan_args.hpp */

    void write_comment(std::ostream& o) {
      o << "#" << std::endl;
    }

    template <typename M>
    void write_comment(std::ostream& o, const M& msg) {
      o << "# " << msg << std::endl;
    }

    template <typename K, typename V>
    void write_comment_property(std::ostream& o, const K& key, const V& val) {
      o << "# " << key << "=" << val << std::endl;
    }

    /**
     * Find the index of an element in a vector.
     * @param v the vector in which an element are searched.
     * @param e the element that we are looking for.
     * @return If e is in v, return the index (0 to size - 1);
     *  otherwise, return the size.
     */

    template <class T, class T2>
    size_t find_index(const std::vector<T>& v, const T2& e) {
      return std::distance(v.begin(), std::find(v.begin(), v.end(), T(e)));
    }

    /* the following mirrors RStan's stan_fit.hpp */
  namespace {
    /**
     *@tparam T The type by which we use for dimensions. T could be say size_t
     * or unsigned int. This whole business (not using size_t) is due to that
     * Rcpp::wrap/as does not support size_t on some platforms and R could not
     * deal with 64bits integers.
     *
     */
    template <class T>
    size_t calc_num_params(const std::vector<T>& dim) {
      T num_params = 1;
      for (size_t i = 0;  i < dim.size(); ++i)
        num_params *= dim[i];
      return num_params;
    }

    template <class T>
    void calc_starts(const std::vector<std::vector<T> >& dims,
                     std::vector<T>& starts) {
      starts.resize(0);
      starts.push_back(0);
      for (size_t i = 1; i < dims.size(); ++i)
        starts.push_back(starts[i - 1] + calc_num_params(dims[i - 1]));
    }

    template <class T>
    T calc_total_num_params(const std::vector<std::vector<T> >& dims) {
      T num_params = 0;
      for (size_t i = 0; i < dims.size(); ++i)
        num_params += calc_num_params(dims[i]);
      return num_params;
    }

    /**
     *  Get the parameter indexes for a vector(array) parameter.
     *  For example, we have parameter beta, which has
     *  dimension [2,3]. Then this function gets
     *  the indexes as (if col_major = false)
     *  [0,0], [0,1], [0,2]
     *  [1,0], [1,1], [1,2]
     *  or (if col_major = true)
     *  [0,0], [1,0]
     *  [0,1], [1,1]
     *  [0,2], [121]
     *
     *  @param dim[in] the dimension of parameter
     *  @param idx[out] for keeping all the indexes
     *
     *  <p> when idx is empty (size = 0), idx
     *  would contains an empty vector.
     *
     *
     */

    template <class T>
    void expand_indices(std::vector<T> dim,
                        std::vector<std::vector<T> >& idx,
                        bool col_major = false) {
      size_t len = dim.size();
      idx.resize(0);
      size_t total = calc_num_params(dim);
      if (0 >= total) return;
      std::vector<size_t> loopj;
      for (size_t i = 1; i <= len; ++i)
        loopj.push_back(len - i);

      if (col_major)
        for (size_t i = 0; i < len; ++i)
          loopj[i] = len - 1 - loopj[i];

      idx.push_back(std::vector<T>(len, 0));
      for (size_t i = 1; i < total; i++) {
        std::vector<T>  v(idx.back());
        for (size_t j = 0; j < len; ++j) {
          size_t k = loopj[j];
          if (v[k] < dim[k] - 1) {
            v[k] += 1;
            break;
          }
          v[k] = 0;
        }
        idx.push_back(v);
      }
    }

    /**
     * Get the names for an array of given dimensions
     * in the way of column majored.
     * For example, if we know an array named `a`, with
     * dimensions of [2, 3, 4], the names then are (starting
     * from 0):
     * a[0,0,0]
     * a[1,0,0]
     * a[0,1,0]
     * a[1,1,0]
     * a[0,2,0]
     * a[1,2,0]
     * a[0,0,1]
     * a[1,0,1]
     * a[0,1,1]
     * a[1,1,1]
     * a[0,2,1]
     * a[1,2,1]
     * a[0,0,2]
     * a[1,0,2]
     * a[0,1,2]
     * a[1,1,2]
     * a[0,2,2]
     * a[1,2,2]
     * a[0,0,3]
     * a[1,0,3]
     * a[0,1,3]
     * a[1,1,3]
     * a[0,2,3]
     * a[1,2,3]
     *
     * @param name The name of the array variable
     * @param dim The dimensions of the array
     * @param fnames[out] Where the names would be pushed.
     * @param first_is_one[true] Where to start for the first index: 0 or 1.
     *
     */
    template <class T> void
    get_flatnames(const std::string& name,
                  const std::vector<T>& dim,
                  std::vector<std::string>& fnames,
                  bool col_major = true,
                  bool first_is_one = true) {

      fnames.clear();
      if (0 == dim.size()) {
        fnames.push_back(name);
        return;
      }

      std::vector<std::vector<T> > idx;
      expand_indices(dim, idx, col_major);
      size_t first = first_is_one ? 1 : 0;
      for (typename std::vector<std::vector<T> >::const_iterator it = idx.begin();
           it != idx.end();
           ++it) {
        std::stringstream stri;
        stri << name << "[";

        size_t lenm1 = it -> size() - 1;
        for (size_t i = 0; i < lenm1; i++)
          stri << ((*it)[i] + first) << ",";
        stri << ((*it)[lenm1] + first) << "]";
        fnames.push_back(stri.str());
      }
    }

    // vectorize get_flatnames
    template <class T>
    void get_all_flatnames(const std::vector<std::string>& names,
                           const std::vector<T>& dims,
                           std::vector<std::string>& fnames,
                           bool col_major = true) {
      fnames.clear();
      for (size_t i = 0; i < names.size(); ++i) {
        std::vector<std::string> i_names;
        // NOTE: col_major = true, first_is_one = false for PyStan (true for RStan)
        get_flatnames(names[i], dims[i], i_names, col_major, false);
        fnames.insert(fnames.end(), i_names.begin(), i_names.end());
      }
    }

    /* To facilitate transform an array variable ordered by col-major index
     * to row-major index order by providing the transforming indices.
     * For example, we have "x[2,3]", then if ordered by col-major, we have
     *
     * x[1,1], x[2,1], x[1,2], x[2,2], x[1,3], x[3,1]
     *
     * Then the indices for transforming to row-major order are
     * [0, 2, 4, 1, 3, 5] + start.
     *
     * @param dim[in] the dimension of the array variable, empty means a scalar
     * @param midx[out] store the indices for mapping col-major to row-major
     * @param start shifts the indices with a starting point
     *
     */
    template <typename T, typename T2>
    void get_indices_col2row(const std::vector<T>& dim, std::vector<T2>& midx,
                             T start = 0) {
      size_t len = dim.size();
      if (len < 1) {
        midx.push_back(start);
        return;
      }

      std::vector<T> z(len, 1);
      for (size_t i = 1; i < len; i++) {
        z[i] *= z[i - 1] * dim[i - 1];
      }

      T total = calc_num_params(dim);
      midx.resize(total);
      std::fill_n(midx.begin(), total, start);
      std::vector<T> v(len, 0);
      for (T i = 1; i < total; i++) {
        for (size_t j = 0; j < len; ++j) {
          size_t k = len - j - 1;
          if (v[k] < dim[k] - 1) {
            v[k] += 1;
            break;
          }
          v[k] = 0;
        }
        // v is the index of the ith element by row-major, for example v=[0,1,2].
        // obtain the position for v if it is col-major indexed.
        T pos = 0;
        for (size_t j = 0; j < len; j++)
          pos += z[j] * v[j];
        midx[i] += pos;
      }
    }

    template <class T>
    void get_all_indices_col2row(const std::vector<std::vector<T> >& dims,
                                 std::vector<size_t>& midx) {
      midx.clear();
      std::vector<T> starts;
      calc_starts(dims, starts);
      for (size_t i = 0; i < dims.size(); ++i) {
        std::vector<size_t> midxi;
        get_indices_col2row(dims[i], midxi, starts[i]);
        midx.insert(midx.end(), midxi.begin(), midxi.end());
      }
    }

    template <class Model>
    std::vector<std::string> get_param_names(Model& m) {
      std::vector<std::string> names;
      m.get_param_names(names);
      names.push_back("lp__");
      return names;
    }

    template <class T>
    void print_vector(const std::vector<T>& v, std::ostream& o,
                      const std::vector<size_t>& midx,
                      const std::string& sep = ",") {
      if (v.size() > 0)
        o << v[midx.at(0)];
      for (size_t i = 1; i < v.size(); i++)
        o << sep << v[midx.at(i)];
      o << std::endl;
    }

    template <class T>
    void print_vector(const std::vector<T>& v, std::ostream& o,
                      const std::string& sep = ",") {
      if (v.size() > 0)
        o << v[0];
      for (size_t i = 1; i < v.size(); i++)
        o << sep << v[i];
      o << std::endl;
    }

    void write_stan_version_as_comment(std::ostream& output) {
       write_comment_property(output,"stan_version_major",stan::MAJOR_VERSION);
       write_comment_property(output,"stan_version_minor",stan::MINOR_VERSION);
       write_comment_property(output,"stan_version_patch",stan::PATCH_VERSION);
    }

    /**
     * Cast a size_t vector to an unsigned int vector.
     * The reason is that first Rcpp::wrap/as does not
     * support size_t on some platforms; second R
     * could not deal with 64bits integers.
     */

    std::vector<unsigned int>
    sizet_to_uint(std::vector<size_t> v1) {
      std::vector<unsigned int> v2(v1.size());
      for (size_t i = 0; i < v1.size(); ++i)
        v2[i] = static_cast<unsigned int>(v1[i]);
      return v2;
    }

    template <class Model>
    std::vector<std::vector<unsigned int> > get_param_dims(Model& m) {
      std::vector<std::vector<size_t> > dims;
      m.get_dims(dims);

      std::vector<std::vector<unsigned int> > uintdims;
      for (std::vector<std::vector<size_t> >::const_iterator it = dims.begin();
           it != dims.end();
           ++it)
        uintdims.push_back(sizet_to_uint(*it));

      std::vector<unsigned int> scalar_dim; // for lp__
      uintdims.push_back(scalar_dim);
      return uintdims;
    }

    template<class Sampler>
    void init_static_hmc(stan::mcmc::base_mcmc* sampler_ptr, const StanArgs& args) {
      double epsilon = args.get_ctrl_sampling_stepsize();
      double epsilon_jitter = args.get_ctrl_sampling_stepsize_jitter();
      double int_time = args.get_ctrl_sampling_int_time();

      Sampler* sampler_ptr2 = dynamic_cast<Sampler*>(sampler_ptr);
      sampler_ptr2->set_nominal_stepsize_and_T(epsilon, int_time);
      sampler_ptr2->set_stepsize_jitter(epsilon_jitter);
    }

    template<class Sampler>
    void init_nuts(stan::mcmc::base_mcmc* sampler_ptr, const StanArgs& args) {
      double epsilon = args.get_ctrl_sampling_stepsize();
      double epsilon_jitter = args.get_ctrl_sampling_stepsize_jitter();
      int max_depth = args.get_ctrl_sampling_max_treedepth();

      Sampler* sampler_ptr2 = dynamic_cast<Sampler*>(sampler_ptr);
      sampler_ptr2->set_nominal_stepsize(epsilon);
      sampler_ptr2->set_stepsize_jitter(epsilon_jitter);
      sampler_ptr2->set_max_depth(max_depth);
    }

    template<class Sampler>
    bool init_windowed_adapt(stan::mcmc::base_mcmc* sampler_ptr, const StanArgs& args,
                             const Eigen::VectorXd& cont_params,
                             stan::interface_callbacks::writer::base_writer& info,
                             stan::interface_callbacks::writer::base_writer& error) {
      double delta = args.get_ctrl_sampling_adapt_delta();
      double gamma = args.get_ctrl_sampling_adapt_gamma();
      double kappa = args.get_ctrl_sampling_adapt_kappa();
      double t0 = args.get_ctrl_sampling_adapt_t0();
      Sampler* sampler_ptr2 = dynamic_cast<Sampler*>(sampler_ptr);

      stan::services::sample::init_adapt<Sampler>(sampler_ptr2,
                                                  delta, gamma, kappa, t0, cont_params,
                                                  info, error);

      sampler_ptr2->set_window_params(args.get_ctrl_sampling_warmup(),
                                      args.get_ctrl_sampling_adapt_init_buffer(),
                                      args.get_ctrl_sampling_adapt_term_buffer(),
                                      args.get_ctrl_sampling_adapt_window(),
                                      info);
      return true;
    }

    struct PyErr_CheckSignals_Functor {
      void operator()() {
        // PyErr_CheckSignals is defined in Python.h
        PyErr_CheckSignals();
      }
    };

    // in:  model, s, sampler_ptr
    // out: sample_writer_size, diagnostic_writer_size
    template <class Model>
    void calculate_sizes(Model& model,
                         stan::mcmc::sample& s,
                         stan::mcmc::base_mcmc* sampler_ptr,
                         size_t& sample_writer_size,
                         size_t& sample_writer_offset,
                         std::vector<std::string>& sample_names,
                         std::vector<std::string>& sampler_names,
                         std::vector<std::string>& model_constrained_param_names,
                         std::vector<std::string>& model_unconstrained_param_names,
                         std::vector<std::string>& sampler_diagnostic_names) {
      s.get_sample_param_names(sample_names);

      sampler_ptr->get_sampler_param_names(sampler_names);

      model.constrained_param_names(model_constrained_param_names, true, true);

      model.unconstrained_param_names(model_unconstrained_param_names, false, false);

      sampler_ptr->get_sampler_diagnostic_names(model_unconstrained_param_names,
                                                sampler_diagnostic_names);

      sample_writer_size = sample_names.size() + sampler_names.size()
        + model_constrained_param_names.size();
      sample_writer_offset = sample_names.size() + sampler_names.size();
    }


    template <class Model, class RNG_t>
    void execute_sampling(StanArgs& args, Model& model, StanHolder& holder,
                          stan::mcmc::base_mcmc* sampler_ptr,
                          stan::mcmc::sample& s,
                          const std::vector<size_t>& qoi_idx,
                          std::vector<double>& initv,
                          std::fstream& sample_stream,
                          std::fstream& diagnostic_stream,
                          const std::vector<std::string>& fnames_oi, RNG_t& base_rng) {
      size_t sample_writer_size, sample_writer_offset;
      std::vector<std::string> sample_names;
      std::vector<std::string> sampler_names;
      std::vector<std::string> model_constrained_param_names;
      std::vector<std::string> model_unconstrained_param_names;
      std::vector<std::string> sampler_diagnostic_names;
      pystan::calculate_sizes(model, s, sampler_ptr,
                             sample_writer_size,
                             sample_writer_offset,
                             sample_names,
                             sampler_names,
                             model_constrained_param_names,
                             model_unconstrained_param_names,
                             sampler_diagnostic_names);


      pystan_sample_writer sample_writer
        = sample_writer_factory(&sample_stream, "# ",
                                  sample_writer_size,
                                  args.get_ctrl_sampling_iter_save(),
                                  args.get_ctrl_sampling_iter_save() - args.get_ctrl_sampling_iter_save_wo_warmup(),
                                  sample_writer_offset,
                                  qoi_idx);

      std::stringstream ss;
      stan::interface_callbacks::writer::stream_writer info(ss);
      stan::interface_callbacks::writer::stream_writer err(std::cerr);

      stan::interface_callbacks::writer::stream_writer diagnostic_writer
        = diagnostic_writer_factory(&diagnostic_stream, "# ");
      stan::interface_callbacks::writer::stream_writer message_writer(std::cout, "# ");
      stan::services::sample::mcmc_writer<Model,
                                          pystan_sample_writer,
                                          stan::interface_callbacks::writer::stream_writer,
                                          stan::interface_callbacks::writer::stream_writer>
        writer(sample_writer, diagnostic_writer, message_writer);

      if (!args.get_append_samples()) {
        writer.write_sample_names(s, sampler_ptr, model);
        writer.write_diagnostic_names(s, sampler_ptr, model);
      }

      // Warm-Up
      clock_t start = clock();

      std::string prefix = "";
      ss.str("");
      ss << " (Chain " << args.get_chain_id() << ")" << std::endl;
      std::string suffix = ss.str();
      PyErr_CheckSignals_Functor interruptCallback;

      stan::services::mcmc::warmup<Model, RNG_t,
                                   PyErr_CheckSignals_Functor>
        (sampler_ptr, args.get_ctrl_sampling_warmup(), args.get_iter() - args.get_ctrl_sampling_warmup(),
         args.get_ctrl_sampling_thin(),
         args.get_ctrl_sampling_refresh(), args.get_ctrl_sampling_save_warmup(),
         writer,
         s,
         model,
         base_rng,
         prefix,
         suffix,
         std::cout,
         interruptCallback,
         info,
         err);

      clock_t end = clock();
      double warmDeltaT = (double)(end - start) / CLOCKS_PER_SEC;
      std::string adaptation_info;
      if (args.get_ctrl_sampling_adapt_engaged()) {
        ss.str("");
        dynamic_cast<stan::mcmc::base_adapter*>(sampler_ptr)->disengage_adaptation();
        writer.write_adapt_finish(sampler_ptr);
        adaptation_info = ss.str();
        adaptation_info = adaptation_info.substr(0, adaptation_info.length()-1);
      }

      // Sampling
      start = clock();

      stan::services::mcmc::sample<Model, RNG_t,
                                   PyErr_CheckSignals_Functor>
        (sampler_ptr, args.get_ctrl_sampling_warmup(), args.get_iter() - args.get_ctrl_sampling_warmup(),
         args.get_ctrl_sampling_thin(),
         args.get_ctrl_sampling_refresh(), true,
         writer,
         s,
         model,
         base_rng,
         prefix,
         suffix,
         std::cout,
         interruptCallback,
         info,
         err);

      end = clock();
      double sampleDeltaT = (double)(end - start) / CLOCKS_PER_SEC;

      writer.write_timing(warmDeltaT, sampleDeltaT);

      double mean_lp(0);
      std::vector<double> mean_pars;
      mean_pars.resize(initv.size(), 0);

      if (args.get_ctrl_sampling_iter_save_wo_warmup() > 0) {
        double inverse_saved = 1.0 / args.get_ctrl_sampling_iter_save_wo_warmup();
        mean_lp = sample_writer.sum_.sum()[0] * inverse_saved;
        for (size_t n = 0; n < mean_pars.size(); n++) {
          mean_pars[n] = sample_writer.sum_.sum()[sample_writer_offset + n] * inverse_saved;
        }
      }

      if (args.get_sample_file_flag()) {
        std::cout << "Sample of chain "
                         << args.get_chain_id()
                         << " is written to file " << args.get_sample_file() << "."
                         << std::endl;
        sample_stream.close();
      }
      if (args.get_diagnostic_file_flag())
        diagnostic_stream.close();

      holder.chains = sample_writer.values_.x();
      holder.test_grad = false;
      holder.args = args;
      holder.inits = initv;
      holder.mean_pars = mean_pars;
      holder.mean_lp__ = mean_lp;
      holder.adaptation_info = adaptation_info;

      std::vector<std::vector<double> > slst(sample_writer.sampler_values_.x().begin()+1,
                      sample_writer.sampler_values_.x().end());
      std::vector<std::string> slst_names(sample_names.begin()+1, sample_names.end());
      slst_names.insert(slst_names.end(), sampler_names.begin(), sampler_names.end());

      holder.sampler_params = slst;
      holder.sampler_param_names = slst_names;

      holder.chain_names = fnames_oi;
    }

    /**
     * @tparam Model
     * @tparam RNG
     *
     * @param args: the instance that wraps the arguments passed for sampling.
     * @param model: the model instance.
     * @param holder[out]: the object to hold all the information returned to Python.
     * @param qoi_idx: the indexes for all parameters of interest.
     * @param fnames_oi: the parameter names of interest.
     * @param base_rng: the boost RNG instance.
     */
    template <class Model, class RNG_t>
    int sampler_command(StanArgs& args, Model& model, StanHolder& holder,
                        const std::vector<size_t>& qoi_idx,
                        const std::vector<std::string>& fnames_oi, RNG_t& base_rng) {
      std::stringstream ss;
      stan::interface_callbacks::writer::stream_writer info(std::cout);
      stan::interface_callbacks::writer::stream_writer error(std::cerr);

      base_rng.seed(args.get_random_seed());
      // (2**50 = 1T samples, 1000 chains)
      static boost::uintmax_t DISCARD_STRIDE =
        static_cast<boost::uintmax_t>(1) << 50;
      // std::cout << "DISCARD_STRIDE=" << DISCARD_STRIDE << std::endl;
      base_rng.discard(DISCARD_STRIDE * (args.get_chain_id() - 1));

      std::vector<double> cont_vector = std::vector<double>(model.num_params_r(), 0);
      Eigen::VectorXd cont_params = Eigen::VectorXd::Zero(model.num_params_r());

      std::vector<int> disc_vector = std::vector<int>(model.num_params_i(),0);
      std::vector<double> params_inr_etc; // cont, disc, and others
      std::vector<double> init_grad;
      std::string init_val = args.get_init();
      PyErr_CheckSignals_Functor interruptCallback;
      // parameter initialization
      {
        std::string init;
        pystan::io::py_var_context_factory context_factory(args.init_vars_r, args.init_vars_i);

        if (init_val == "0")
          init = "0";
        else if (init_val == "user")
          init = "user";
        else {
          std::stringstream R;
          R << args.get_init_radius();
          init = R.str();
        }
        stan::interface_callbacks::writer::stream_writer info(ss);
        if (!stan::services::init::initialize_state(init,
                                                    cont_params,
                                                    model,
                                                    base_rng,
                                                    info,
                                                    context_factory,
                                                    args.get_enable_random_init(),
                                                    args.get_init_radius())) {
          throw std::runtime_error(ss.str());
        }
        for (int n = 0; n < cont_params.size(); n++)
          cont_vector[n] = cont_params[n];
      }

      // keep a record of the initial values
      std::vector<double> initv;
      model.write_array(base_rng,cont_vector,disc_vector,initv);

      if (TEST_GRADIENT == args.get_method()) {
        std::cout << std::endl << "TEST GRADIENT MODE" << std::endl;
        double epsilon = args.get_ctrl_test_grad_epsilon();
        double error = args.get_ctrl_test_grad_error();

        stan::interface_callbacks::writer::stream_writer info(std::cout);
        int num_failed =
          stan::model::test_gradients<true,true>(model,cont_vector,disc_vector,
                                                 epsilon,error,info);
        std::cout << ss.str() << std::endl;
        holder.num_failed = num_failed;
        holder.test_grad = true;
        holder.inits = initv;
        return 0;
      }

      std::fstream sample_stream;
      std::fstream diagnostic_stream;
      bool append_samples(args.get_append_samples());
      if (args.get_sample_file_flag()) {
        std::ios_base::openmode samples_append_mode
          = append_samples ? (std::fstream::out | std::fstream::app)
                           : std::fstream::out;
        sample_stream.open(args.get_sample_file().c_str(), samples_append_mode);
      }

      stan::interface_callbacks::writer::stream_writer sample_writer(sample_stream, "# ");

      if (VARIATIONAL == args.get_method()) {
        int grad_samples = args.get_ctrl_variational_grad_samples();
        int elbo_samples = args.get_ctrl_variational_elbo_samples();
        int max_iterations = args.get_iter();
        double tol_rel_obj = args.get_ctrl_variational_tol_rel_obj();
        double eta = args.get_ctrl_variational_eta();
        bool adapt_engaged = args.get_ctrl_variational_adapt_engaged();
        int adapt_iterations = args.get_ctrl_variational_adapt_iter();
        int eval_elbo = args.get_ctrl_variational_eval_elbo();
        int output_samples = args.get_ctrl_variational_output_samples();
        if (args.get_sample_file_flag()) {
          write_comment(sample_stream,"Sample generated by Stan (Automatic Differentiation Variational Inference)");
          write_stan_version_as_comment(sample_stream);
          // FIXME PyStan: to implement args.write_args_as_comment(sample_stream);
        }
        if (args.get_diagnostic_file_flag()) {
          write_comment(diagnostic_stream,"Sample generated by Stan (Automatic Differentiation Variational Inference)");
          write_stan_version_as_comment(sample_stream);
          // FIXME PyStan: to implement args.write_args_as_comment(diagnostic_stream);
        }

        clock_t start_check = clock();

        double init_log_prob;
        Eigen::VectorXd init_grad
          = Eigen::VectorXd::Zero(model.num_params_r());

        stan::model::gradient(model, cont_params, init_log_prob,
                              init_grad, &std::cout);

        clock_t end_check = clock();
        double deltaT
          = static_cast<double>(end_check - start_check) / CLOCKS_PER_SEC;

        std::cout << std::endl;
        std::cout << "This is Automatic Differentiation Variational Inference.";
        std::cout << std::endl;

        std::cout << std::endl;
        std::cout << "(EXPERIMENTAL ALGORITHM: expect frequent updates to the"
                         << " procedure.)";
        std::cout << std::endl;

        std::cout << std::endl;
        std::cout << "Gradient evaluation took " << deltaT
                         << " seconds" << std::endl;
        std::cout << "1000 iterations under these settings should take "
                         << 1e3 * grad_samples * deltaT << " seconds." << std::endl;
        std::cout << "Adjust your expectations accordingly!";
        std::cout << std::endl;
        std::cout << std::endl;


        // FIXME(AR): WIP trying to get ADVI working with new API
        stan::interface_callbacks::writer::stream_writer message_writer(std::cout, "# ");
        stan::interface_callbacks::writer::stream_writer parameter_writer(sample_stream);
        stan::interface_callbacks::writer::stream_writer diagnostic_writer
          = diagnostic_writer_factory(&diagnostic_stream, "# ");

        if (args.get_ctrl_variational_algorithm() == FULLRANK) {
          if (args.get_sample_file_flag()) {
            std::vector<std::string> names;
            names.push_back("lp__");
            model.constrained_param_names(names, true, true);
            print_vector(names, sample_stream);
          }

          stan::variational::advi<Model,
                                  stan::variational::normal_fullrank,
                                  RNG_t>
            cmd_advi(model,
                     cont_params,
                     base_rng,
                     grad_samples,
                     elbo_samples,
                     eval_elbo,
                     output_samples);
            cmd_advi.run(eta, adapt_engaged, adapt_iterations, tol_rel_obj, max_iterations,
                         message_writer, parameter_writer, diagnostic_writer);
        }

        if (args.get_ctrl_variational_algorithm() == MEANFIELD) {
          if (args.get_sample_file_flag()) {
            std::vector<std::string> names;
            names.push_back("lp__");
            model.constrained_param_names(names, true, true);
            print_vector(names, sample_stream);
          }

          stan::variational::advi<Model,
                                  stan::variational::normal_meanfield,
                                  RNG_t>
            cmd_advi(model,
                     cont_params,
                     base_rng,
                     grad_samples,
                     elbo_samples,
                     eval_elbo,
                     output_samples);
              cmd_advi.run(eta, adapt_engaged, adapt_iterations, tol_rel_obj, max_iterations,
                           message_writer, parameter_writer, diagnostic_writer);
        }
        holder.args = args;
        return 0;
      }

      if (OPTIM == args.get_method()) { // point estimation
        if (LBFGS == args.get_ctrl_optim_algorithm()) {
          std::cout << "STAN OPTIMIZATION COMMAND (LBFGS)" << std::endl;
          std::cout << "init = " << init_val << std::endl;
          if (args.get_sample_file_flag())
            std::cout << "output = " << args.get_sample_file() << std::endl;
          std::cout << "save_iterations = " << args.get_ctrl_optim_save_iterations() << std::endl;
          std::cout << "init_alpha = " << args.get_ctrl_optim_init_alpha() << std::endl;
          std::cout << "tol_obj = " << args.get_ctrl_optim_tol_obj() << std::endl;
          std::cout << "tol_grad = " << args.get_ctrl_optim_tol_grad() << std::endl;
          std::cout << "tol_param = " << args.get_ctrl_optim_tol_param() << std::endl;
          std::cout << "tol_rel_obj = " << args.get_ctrl_optim_tol_rel_obj() << std::endl;
          std::cout << "tol_rel_grad = " << args.get_ctrl_optim_tol_rel_grad() << std::endl;
          std::cout << "history_size = " << args.get_ctrl_optim_history_size() << std::endl;
          std::cout << "seed = " << args.get_random_seed() << std::endl;

          if (args.get_sample_file_flag()) {
            write_comment(sample_stream,"Point Estimate Generated by Stan (LBFGS)");
            write_comment(sample_stream);
            write_comment_property(sample_stream,"stan_version_major",stan::MAJOR_VERSION);
            write_comment_property(sample_stream,"stan_version_minor",stan::MINOR_VERSION);
            write_comment_property(sample_stream,"stan_version_patch",stan::PATCH_VERSION);
            write_comment_property(sample_stream,"init",init_val);
            write_comment_property(sample_stream,"save_iterations",args.get_ctrl_optim_save_iterations());
            write_comment_property(sample_stream,"init_alpha",args.get_ctrl_optim_init_alpha());
            write_comment_property(sample_stream,"tol_obj",args.get_ctrl_optim_tol_obj());
            write_comment_property(sample_stream,"tol_grad",args.get_ctrl_optim_tol_grad());
            write_comment_property(sample_stream,"tol_param",args.get_ctrl_optim_tol_param());
            write_comment_property(sample_stream,"tol_rel_obj", args.get_ctrl_optim_tol_rel_obj());
            write_comment_property(sample_stream,"tol_rel_grad", args.get_ctrl_optim_tol_rel_grad());
            write_comment_property(sample_stream,"history_size", args.get_ctrl_optim_history_size());
            write_comment_property(sample_stream,"seed",args.get_random_seed());
            write_comment(sample_stream);

            std::vector<std::string> names;
            names.push_back("lp__");
            model.constrained_param_names(names);
            print_vector(names, sample_stream);
          }

          double lp(0);
          bool save_iterations = args.get_ctrl_optim_save_iterations();
          int refresh = args.get_ctrl_sampling_refresh();

          typedef stan::optimization::BFGSLineSearch<Model,stan::optimization::LBFGSUpdate<> > Optimizer;
          Optimizer lbfgs(model, cont_vector, disc_vector, &std::cout);

          lbfgs.get_qnupdate().set_history_size(args.get_ctrl_optim_history_size());

          lbfgs._ls_opts.alpha0       = args.get_ctrl_optim_init_alpha();
          lbfgs._conv_opts.tolAbsF    = args.get_ctrl_optim_tol_obj();
          lbfgs._conv_opts.tolRelF    = args.get_ctrl_optim_tol_rel_obj();
          lbfgs._conv_opts.tolAbsGrad = args.get_ctrl_optim_tol_grad();
          lbfgs._conv_opts.tolRelGrad = args.get_ctrl_optim_tol_rel_grad();
          lbfgs._conv_opts.tolAbsX    = args.get_ctrl_optim_tol_param();
          lbfgs._conv_opts.maxIts     = args.get_iter();

          stan::interface_callbacks::writer::stream_writer sampler_writer(sample_stream);

          stan::services::optimize::do_bfgs_optimize(model, lbfgs, base_rng,
                                                     lp, cont_vector, disc_vector,
                                                     sampler_writer, info,
                                                     save_iterations, refresh, interruptCallback);

          if (args.get_sample_file_flag()) {
            stan::services::io::write_iteration(model, base_rng,
                                                lp, cont_vector, disc_vector,
                                                info, sampler_writer);
            sample_stream.close();
          }
          model.write_array(base_rng,cont_vector,disc_vector, params_inr_etc);
          holder.par = params_inr_etc;
          holder.value = lp;
        } else if (BFGS == args.get_ctrl_optim_algorithm()) {
          std::cout << "STAN OPTIMIZATION COMMAND (BFGS)" << std::endl;
          std::cout << "init = " << init_val << std::endl;
          if (args.get_sample_file_flag())
            std::cout << "output = " << args.get_sample_file() << std::endl;
          std::cout << "save_iterations = " << args.get_ctrl_optim_save_iterations() << std::endl;
          std::cout << "init_alpha = " << args.get_ctrl_optim_init_alpha() << std::endl;
          std::cout << "tol_obj = " << args.get_ctrl_optim_tol_obj() << std::endl;
          std::cout << "tol_grad = " << args.get_ctrl_optim_tol_grad() << std::endl;
          std::cout << "tol_param = " << args.get_ctrl_optim_tol_param() << std::endl;
          std::cout << "tol_rel_obj = " << args.get_ctrl_optim_tol_rel_obj() << std::endl;
          std::cout << "tol_rel_grad = " << args.get_ctrl_optim_tol_rel_grad() << std::endl;
          std::cout << "seed = " << args.get_random_seed() << std::endl;

          if (args.get_sample_file_flag()) {
            write_comment(sample_stream,"Point Estimate Generated by Stan (BFGS)");
            write_comment(sample_stream);
            write_comment_property(sample_stream,"stan_version_major",stan::MAJOR_VERSION);
            write_comment_property(sample_stream,"stan_version_minor",stan::MINOR_VERSION);
            write_comment_property(sample_stream,"stan_version_patch",stan::PATCH_VERSION);
            write_comment_property(sample_stream,"init",init_val);
            write_comment_property(sample_stream,"save_iterations",args.get_ctrl_optim_save_iterations());
            write_comment_property(sample_stream,"init_alpha",args.get_ctrl_optim_init_alpha());
            write_comment_property(sample_stream,"tol_obj",args.get_ctrl_optim_tol_obj());
            write_comment_property(sample_stream,"tol_grad",args.get_ctrl_optim_tol_grad());
            write_comment_property(sample_stream,"tol_param",args.get_ctrl_optim_tol_param());
            write_comment_property(sample_stream,"tol_rel_obj", args.get_ctrl_optim_tol_rel_obj());
            write_comment_property(sample_stream,"tol_rel_grad", args.get_ctrl_optim_tol_rel_grad());
            write_comment_property(sample_stream,"seed",args.get_random_seed());
            write_comment(sample_stream);

            std::vector<std::string> names;
            names.push_back("lp__");
            model.constrained_param_names(names);
            print_vector(names, sample_stream);
          }
          double lp(0);
          bool save_iterations = args.get_ctrl_optim_save_iterations();
          int refresh = args.get_ctrl_sampling_refresh();

          typedef stan::optimization::BFGSLineSearch<Model,stan::optimization::BFGSUpdate_HInv<> > Optimizer;
          Optimizer bfgs(model, cont_vector, disc_vector, &std::cout);

          bfgs._ls_opts.alpha0       = args.get_ctrl_optim_init_alpha();
          bfgs._conv_opts.tolAbsF    = args.get_ctrl_optim_tol_obj();
          bfgs._conv_opts.tolRelF    = args.get_ctrl_optim_tol_rel_obj();
          bfgs._conv_opts.tolAbsGrad = args.get_ctrl_optim_tol_grad();
          bfgs._conv_opts.tolRelGrad = args.get_ctrl_optim_tol_rel_grad();
          bfgs._conv_opts.tolAbsX    = args.get_ctrl_optim_tol_param();
          bfgs._conv_opts.maxIts     = args.get_iter();

          stan::interface_callbacks::writer::stream_writer sampler_writer(sample_stream);

          stan::services::optimize::do_bfgs_optimize(model, bfgs, base_rng,
                                                     lp, cont_vector, disc_vector,
                                                     sampler_writer, info,
                                                     save_iterations, refresh, interruptCallback);

          if (args.get_sample_file_flag()) {
            stan::services::io::write_iteration(model, base_rng,
                                                lp, cont_vector, disc_vector,
                                                info, sampler_writer);
            sample_stream.close();
          }
          model.write_array(base_rng,cont_vector,disc_vector,params_inr_etc);
          holder.par = params_inr_etc;
          holder.value = lp;
        } else if (Newton == args.get_ctrl_optim_algorithm()) {
          std::cout << "STAN OPTIMIZATION COMMAND (Newton)" << std::endl;
          if (args.get_sample_file_flag()) {
            write_comment(sample_stream,"Point Estimate Generated by Stan (Newton)");
            write_comment(sample_stream);
            write_comment_property(sample_stream,"stan_version_major",stan::MAJOR_VERSION);
            write_comment_property(sample_stream,"stan_version_minor",stan::MINOR_VERSION);
            write_comment_property(sample_stream,"stan_version_patch",stan::PATCH_VERSION);
            write_comment_property(sample_stream,"init",init_val);
            write_comment_property(sample_stream,"seed",args.get_random_seed());
            write_comment(sample_stream);

            std::vector<std::string> names;
            names.push_back("lp__");
            model.constrained_param_names(names);
            print_vector(names, sample_stream);
          }
          std::vector<double> gradient;
          double lp = stan::model::log_prob_grad<true,true>(model, cont_vector, disc_vector, gradient);

          double lastlp = lp - 1;
          std::cout << "initial log joint probability = " << lp << std::endl;
          int m = 0;
          while ((lp - lastlp) / fabs(lp) > 1e-8) {
            PyErr_CheckSignals();
            lastlp = lp;
            lp = stan::optimization::newton_step(model, cont_vector, disc_vector);
            if (args.get_ctrl_optim_refresh() > 0) {
                std::cout << "Iteration ";
                std::cout << std::setw(2) << (m + 1) << ". ";
                std::cout << "Log joint probability = " << std::setw(10) << lp;
                std::cout << ". Improved by " << (lp - lastlp) << ".";
                std::cout << std::endl;
                std::cout.flush();
            }
            m++;
            if (args.get_sample_file_flag()) {
              sample_stream << lp << ',';
              model.write_array(base_rng, cont_vector, disc_vector, params_inr_etc);
              print_vector(params_inr_etc, sample_stream);
            }
          }
          model.write_array(base_rng, cont_vector, disc_vector, params_inr_etc);
          holder.par = params_inr_etc;
          holder.value = lp;

          if (args.get_sample_file_flag()) {
            sample_stream << lp << ',';
            print_vector(params_inr_etc, sample_stream);
            sample_stream.close();
          }
        }
        return 0;
      }

      for (size_t i = 0; i < cont_vector.size(); i++) cont_params(i) = cont_vector[i];

      // method = 3 //sampling
      if (args.get_diagnostic_file_flag())
        diagnostic_stream.open(args.get_diagnostic_file().c_str(), std::fstream::out);

      if (args.get_sample_file_flag()) {
        write_comment(sample_stream,"Sample generated by Stan");
        write_stan_version_as_comment(sample_stream);
        // FIXME PyStan: to implement: args.write_args_as_comment(sample_stream);
      }
      if (args.get_diagnostic_file_flag()) {
        write_comment(diagnostic_stream,"Sample generated by Stan");
        write_stan_version_as_comment(sample_stream);
        // FIXME PyStan: to implmenet: args.write_args_as_comment(diagnostic_stream);
      }

      int engine_index = 0;

      sampling_metric_t metric = args.get_ctrl_sampling_metric(); // unit_e, diag_e, dense_e;
      int metric_index = 0;
      if (UNIT_E == metric) metric_index = 0;
      else if (DIAG_E == metric) metric_index = 1;
      else if(DENSE_E == metric) metric_index = 2;
      sampling_algo_t algorithm = args.get_ctrl_sampling_algorithm();
      if (model.num_params_r() == 0 && algorithm != Fixed_param) {
        throw std::runtime_error("Must use algorithm=\"Fixed_param\" for model that has no parameters.");
      }

      stan::mcmc::sample s(cont_params, 0, 0);

      if (algorithm == Fixed_param) {
        stan::mcmc::fixed_param_sampler sampler;
        if (args.get_ctrl_sampling_warmup() != 0) {
          std::cout << "Warning: warmup will be skipped for the fixed parameter sampler!" << std::endl;
          args.set_ctrl_sampling_warmup(0);
        }
        execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                         sample_stream, diagnostic_stream, fnames_oi,
                         base_rng);
        return 0;

      }

      switch (algorithm) {
         case Metropolis: engine_index = 3; break;
         case HMC: engine_index = 0; break;
         case NUTS: engine_index = 1; break;
         default: engine_index = 10000; // make it fail in the end
      }
      int sampler_select = engine_index + 10 * metric_index;
      if (args.get_ctrl_sampling_adapt_engaged())  sampler_select += 100;
      switch (sampler_select) {
        case 0: {
          typedef stan::mcmc::unit_e_static_hmc<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_static_hmc<sampler_t>(&sampler, args);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 1: {
          typedef stan::mcmc::unit_e_nuts<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_nuts<sampler_t>(&sampler, args);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 10: {
          typedef stan::mcmc::diag_e_static_hmc<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_static_hmc<sampler_t>(&sampler, args);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 11: {
          typedef stan::mcmc::diag_e_nuts<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_nuts<sampler_t>(&sampler, args);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 20: {
          typedef stan::mcmc::dense_e_static_hmc<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_static_hmc<sampler_t>(&sampler, args);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 21: {
          typedef stan::mcmc::dense_e_nuts<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_nuts<sampler_t>(&sampler, args);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 100: {
          typedef stan::mcmc::adapt_unit_e_static_hmc<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_static_hmc<sampler_t>(&sampler, args);

          double delta = args.get_ctrl_sampling_adapt_delta();
          double gamma = args.get_ctrl_sampling_adapt_gamma();
          double kappa = args.get_ctrl_sampling_adapt_kappa();
          double t0 = args.get_ctrl_sampling_adapt_t0();
          stan::services::sample::init_adapt<sampler_t>(&sampler,
                                                        delta, gamma, kappa, t0, cont_params,
                                                        info, error);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 101: {
          typedef stan::mcmc::adapt_unit_e_nuts<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_nuts<sampler_t>(&sampler, args);
          double delta = args.get_ctrl_sampling_adapt_delta();
          double gamma = args.get_ctrl_sampling_adapt_gamma();
          double kappa = args.get_ctrl_sampling_adapt_kappa();
          double t0 = args.get_ctrl_sampling_adapt_t0();
          stan::services::sample::init_adapt<sampler_t>(&sampler,
                                                        delta, gamma, kappa, t0, cont_params,
                                                        info, error);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 110: {
          typedef stan::mcmc::adapt_diag_e_static_hmc<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_static_hmc<sampler_t>(&sampler, args);
          init_windowed_adapt<sampler_t>(&sampler, args, cont_params, info, error);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 111: {
          typedef stan::mcmc::adapt_diag_e_nuts<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_nuts<sampler_t>(&sampler, args);
          init_windowed_adapt<sampler_t>(&sampler, args, cont_params, info, error);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 120: {
          typedef stan::mcmc::adapt_dense_e_static_hmc<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_static_hmc<sampler_t>(&sampler, args);
          init_windowed_adapt<sampler_t>(&sampler, args, cont_params, info, error);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        case 121: {
          typedef stan::mcmc::adapt_dense_e_nuts<Model, RNG_t> sampler_t;
          sampler_t sampler(model, base_rng);
          init_nuts<sampler_t>(&sampler, args);
          init_windowed_adapt<sampler_t>(&sampler, args, cont_params, info, error);
          execute_sampling(args, model, holder, &sampler, s, qoi_idx, initv,
                           sample_stream, diagnostic_stream, fnames_oi,
                           base_rng);
          break;
        }
        default:
          throw std::invalid_argument("No sampler matching HMC specification!");
      }
      return 0;
    }
  }



  template <class Model, class RNG_t>
  class stan_fit {

  private:
    pystan::io::py_var_context data_;
    Model model_;
    RNG_t base_rng;
    const std::vector<std::string> names_;
    const std::vector<std::vector<unsigned int> > dims_;
    const unsigned int num_params_;

    std::vector<std::string> names_oi_; // parameters of interest
    std::vector<std::vector<unsigned int> > dims_oi_;
    std::vector<size_t> names_oi_tidx_;  // the total indexes of names2.
    // std::vector<size_t> midx_for_col2row; // indices for mapping col-major to row-major
    std::vector<unsigned int> starts_oi_;
    unsigned int num_params2_;  // total number of POI's.
    std::vector<std::string> fnames_oi_;

  private:
    /**
     * Tell if a parameter name is an element of an array parameter.
     * Note that it only supports full specified name; slicing
     * is not supported. The test only tries to see if there
     * are brackets.
     */
    bool is_flatname(const std::string& name) {
      return name.find('[') != name.npos && name.find(']') != name.npos;
    }

    /*
     * Update the parameters we are interested for the model.
     * As well, the dimensions vector for the parameters are
     * updated.
     */
    void update_param_oi0(const std::vector<std::string>& pnames) {
      names_oi_.clear();
      dims_oi_.clear();
      names_oi_tidx_.clear();

      std::vector<unsigned int> starts;
      calc_starts(dims_, starts);
      for (std::vector<std::string>::const_iterator it = pnames.begin();
           it != pnames.end();
           ++it) {
        size_t p = find_index(names_, *it);
        if (p != names_.size()) {
          names_oi_.push_back(*it);
          dims_oi_.push_back(dims_[p]);
          if (*it == "lp__") {
            names_oi_tidx_.push_back(-1); // -1 for lp__ as it is not really a parameter
            continue;
          }
          size_t i_num = calc_num_params(dims_[p]);
          size_t i_start = starts[p];
          for (size_t j = i_start; j < i_start + i_num; j++)
            names_oi_tidx_.push_back(j);
        }
      }
      calc_starts(dims_oi_, starts_oi_);
      num_params2_ = names_oi_tidx_.size();
    }

  public:
    bool update_param_oi(std::vector<std::string> pars) {
      std::vector<std::string> pnames = pars;
      if (std::find(pnames.begin(), pnames.end(), "lp__") == pnames.end())
        pnames.push_back("lp__");
      update_param_oi0(pnames);
      get_all_flatnames(names_oi_, dims_oi_, fnames_oi_, true);
      return true;
    }

    stan_fit(vars_r_t& vars_r, vars_i_t& vars_i) :
      data_(vars_r, vars_i),
      model_(data_, &std::cout),
      base_rng(static_cast<boost::uint32_t>(std::time(0))),
      names_(get_param_names(model_)),
      dims_(get_param_dims(model_)),
      num_params_(calc_total_num_params(dims_)),
      names_oi_(names_),
      dims_oi_(dims_),
      num_params2_(num_params_)
    {
      for (size_t j = 0; j < num_params2_ - 1; j++)
        names_oi_tidx_.push_back(j);
      names_oi_tidx_.push_back(-1); // lp__
      calc_starts(dims_oi_, starts_oi_);
      get_all_flatnames(names_oi_, dims_oi_, fnames_oi_, true);
      // get_all_indices_col2row(dims_, midx_for_col2row);
    }

    /**
     * Transform the parameters from its defined support
     * to unconstrained space
     *
     * @param vars_r initial values for a chain
     * @param vars_i initial values for a chain
     */
    std::vector<double> unconstrain_pars(vars_r_t& vars_r, vars_i_t& vars_i) {
      pystan::io::py_var_context par_context(vars_r, vars_i);
      std::vector<int> params_i;
      std::vector<double> params_r;
      model_.transform_inits(par_context, params_i, params_r, &std::cout);
      return params_r;
    }

    std::vector<std::string> unconstrained_param_names(bool include_tparams, bool include_gqs) {
      std::vector<std::string> names;
      model_.unconstrained_param_names(names, include_tparams, include_gqs);
      return names;
    }

    std::vector<std::string> constrained_param_names(bool include_tparams, bool include_gqs) {
      std::vector<std::string> names;
      model_.constrained_param_names(names, include_tparams, include_gqs);
      return names;
    }

    /**
     * Contrary to unconstrain_pars, transform parameters
     * from unconstrained support to the constrained.
     *
     * @param params_r The real parameters on the unconstrained
     *  space.
     *
     */
    std::vector<double> constrain_pars(std::vector<double>& params_r) {
      std::vector<double> par;
      if (params_r.size() != model_.num_params_r()) {
        std::stringstream msg;
        msg << "Number of unconstrained parameters does not match "
               "that of the model ("
            << params_r.size() << " vs "
            << model_.num_params_r()
            << ").";
        throw std::domain_error(msg.str());
      }
      std::vector<int> params_i(model_.num_params_i());
      model_.write_array(base_rng, params_r, params_i, par);
      return par;
    }

    /**
     * Expose the log_prob of the model to stan_fit so R user
     * can call this function.
     *
     * @param upar The real parameters on the unconstrained
     *  space.
     */
    double log_prob(std::vector<double> upar, bool jacobian_adjust_transform, bool gradient) {
      using std::vector;
      vector<double> par_r = upar;
      if (par_r.size() != model_.num_params_r()) {
        std::stringstream msg;
        msg << "Number of unconstrained parameters does not match "
               "that of the model ("
            << par_r.size() << " vs "
            << model_.num_params_r()
            << ").";
        throw std::domain_error(msg.str());
      }
      vector<int> par_i(model_.num_params_i(), 0);
      if (!gradient) {
        if (jacobian_adjust_transform) {
          return stan::model::log_prob_propto<true>(model_, par_r, par_i, &std::cout);
        } else {
          return stan::model::log_prob_propto<false>(model_, par_r, par_i, &std::cout);
        }
      }

      std::vector<double> grad;
      double lp;
      if (jacobian_adjust_transform)
        lp = stan::model::log_prob_grad<true,true>(model_, par_r, par_i, grad, &std::cout);
      else
        lp = stan::model::log_prob_grad<true,false>(model_, par_r, par_i, grad, &std::cout);
      // RStan returns the gradient as an attribute. Python numbers don't have attributes.
      return lp;
    }

    /**
     * Expose the grad_log_prob of the model to stan_fit so R user
     * can call this function.
     *
     * @param upar The real parameters on the unconstrained
     *  space.
     * @param jacobian_adjust_transform TRUE/FALSE, whether
     *  we add the term due to the transform from constrained
     *  space to unconstrained space implicitly done in Stan.
     */
    std::vector<double> grad_log_prob(std::vector<double> upar, bool jacobian_adjust_transform) {
      std::vector<double> par_r = upar;
      if (par_r.size() != model_.num_params_r()) {
        std::stringstream msg;
        msg << "Number of unconstrained parameters does not match "
               "that of the model ("
            << par_r.size() << " vs "
            << model_.num_params_r()
            << ").";
        throw std::domain_error(msg.str());
      }
      std::vector<int> par_i(model_.num_params_i(), 0);
      std::vector<double> gradient;
      // RStan returns the lp as an attribute. Python numbers don't have attributes.
      if (jacobian_adjust_transform)
        stan::model::log_prob_grad<true,true>(model_, par_r, par_i, gradient, &std::cout);
      else
        stan::model::log_prob_grad<true,false>(model_, par_r, par_i, gradient, &std::cout);
      return gradient;
    }

    /**
     * Return the number of unconstrained parameters
     */
    int num_pars_unconstrained() {
      int n = model_.num_params_r();
      return n;
    }

    int call_sampler(StanArgs& args, StanHolder& holder) {
      int ret;
      ret = sampler_command(args, model_, holder, names_oi_tidx_,
                            fnames_oi_, base_rng);
      if (ret != 0) {
        throw std::runtime_error("Something went wrong after call_sampler.");
      }
      return ret; // FIXME: rstan returns holder
    }

    std::vector<std::string> param_names() const {
       return names_;
    }

    std::vector<std::string> param_names_oi() const {
       return names_oi_;
    }

    std::vector<std::vector<unsigned int> > param_dims() const {
        return dims_;
    }

    std::vector<std::vector<unsigned int> > param_dims_oi() const {
        return dims_oi_;
    }

    std::vector<std::string> param_fnames_oi() const {
       std::vector<std::string> fnames;
       get_all_flatnames(names_oi_, dims_oi_, fnames, true);
       return fnames;
    }

  };
}
#endif
