#ifndef STAN_SERVICES_SAMPLE_HMC_NUTS_DENSE_E_HPP
#define STAN_SERVICES_SAMPLE_HMC_NUTS_DENSE_E_HPP

#include <stan/callbacks/interrupt.hpp>
#include <stan/callbacks/logger.hpp>
#include <stan/callbacks/writer.hpp>
#include <stan/io/var_context.hpp>
#include <stan/math/prim.hpp>
#include <stan/mcmc/hmc/nuts/dense_e_nuts.hpp>
#include <stan/services/error_codes.hpp>
#include <stan/services/util/run_sampler.hpp>
#include <stan/services/util/create_rng.hpp>
#include <stan/services/util/initialize.hpp>
#include <stan/services/util/inv_metric.hpp>
#include <vector>

namespace stan {
namespace services {
namespace sample {

/**
 * Runs HMC with NUTS without adaptation using dense Euclidean metric
 * with a pre-specified Euclidean metric.
 *
 * @tparam Model Model class
 * @param[in] model Input model to test (with data already instantiated)
 * @param[in] init var context for initialization
 * @param[in] init_inv_metric var context exposing an initial dense
              inverse Euclidean metric (must be positive definite)
 * @param[in] random_seed random seed for the random number generator
 * @param[in] chain chain id to advance the pseudo random number generator
 * @param[in] init_radius radius to initialize
 * @param[in] num_warmup Number of warmup samples
 * @param[in] num_samples Number of samples
 * @param[in] num_thin Number to thin the samples
 * @param[in] save_warmup Indicates whether to save the warmup iterations
 * @param[in] refresh Controls the output
 * @param[in] stepsize initial stepsize for discrete evolution
 * @param[in] stepsize_jitter uniform random jitter of stepsize
 * @param[in] max_depth Maximum tree depth
 * @param[in,out] interrupt Callback for interrupts
 * @param[in,out] logger Logger for messages
 * @param[in,out] init_writer Writer callback for unconstrained inits
 * @param[in,out] sample_writer Writer for draws
 * @param[in,out] diagnostic_writer Writer for diagnostic information
 * @return error_codes::OK if successful
 */
template <class Model>
int hmc_nuts_dense_e(Model& model, const stan::io::var_context& init,
                     const stan::io::var_context& init_inv_metric,
                     unsigned int random_seed, unsigned int chain,
                     double init_radius, int num_warmup, int num_samples,
                     int num_thin, bool save_warmup, int refresh,
                     double stepsize, double stepsize_jitter, int max_depth,
                     callbacks::interrupt& interrupt, callbacks::logger& logger,
                     callbacks::writer& init_writer,
                     callbacks::writer& sample_writer,
                     callbacks::writer& diagnostic_writer) {
  boost::ecuyer1988 rng = util::create_rng(random_seed, chain);

  std::vector<int> disc_vector;
  std::vector<double> cont_vector = util::initialize(
      model, init, rng, init_radius, true, logger, init_writer);

  Eigen::MatrixXd inv_metric;
  try {
    inv_metric = util::read_dense_inv_metric(init_inv_metric,
                                             model.num_params_r(), logger);
    util::validate_dense_inv_metric(inv_metric, logger);
  } catch (const std::domain_error& e) {
    return error_codes::CONFIG;
  }

  stan::mcmc::dense_e_nuts<Model, boost::ecuyer1988> sampler(model, rng);

  sampler.set_metric(inv_metric);

  sampler.set_nominal_stepsize(stepsize);
  sampler.set_stepsize_jitter(stepsize_jitter);
  sampler.set_max_depth(max_depth);

  util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples,
                    num_thin, refresh, save_warmup, rng, interrupt, logger,
                    sample_writer, diagnostic_writer);
  return error_codes::OK;
}

/**
 * Runs HMC with NUTS without adaptation using dense Euclidean metric,
 * with identity matrix as initial inv_metric.
 *
 * @tparam Model Model class
 * @param[in] model Input model to test (with data already instantiated)
 * @param[in] init var context for initialization
 * @param[in] random_seed random seed for the random number generator
 * @param[in] chain chain id to advance the pseudo random number generator
 * @param[in] init_radius radius to initialize
 * @param[in] num_warmup Number of warmup samples
 * @param[in] num_samples Number of samples
 * @param[in] num_thin Number to thin the samples
 * @param[in] save_warmup Indicates whether to save the warmup iterations
 * @param[in] refresh Controls the output
 * @param[in] stepsize initial stepsize for discrete evolution
 * @param[in] stepsize_jitter uniform random jitter of stepsize
 * @param[in] max_depth Maximum tree depth
 * @param[in,out] interrupt Callback for interrupts
 * @param[in,out] logger Logger for messages
 * @param[in,out] init_writer Writer callback for unconstrained inits
 * @param[in,out] sample_writer Writer for draws
 * @param[in,out] diagnostic_writer Writer for diagnostic information
 * @return error_codes::OK if successful
 *
 */
template <class Model>
int hmc_nuts_dense_e(Model& model, const stan::io::var_context& init,
                     unsigned int random_seed, unsigned int chain,
                     double init_radius, int num_warmup, int num_samples,
                     int num_thin, bool save_warmup, int refresh,
                     double stepsize, double stepsize_jitter, int max_depth,
                     callbacks::interrupt& interrupt, callbacks::logger& logger,
                     callbacks::writer& init_writer,
                     callbacks::writer& sample_writer,
                     callbacks::writer& diagnostic_writer) {
  stan::io::dump dmp
      = util::create_unit_e_dense_inv_metric(model.num_params_r());
  stan::io::var_context& unit_e_metric = dmp;

  return hmc_nuts_dense_e(model, init, unit_e_metric, random_seed, chain,
                          init_radius, num_warmup, num_samples, num_thin,
                          save_warmup, refresh, stepsize, stepsize_jitter,
                          max_depth, interrupt, logger, init_writer,
                          sample_writer, diagnostic_writer);
}

}  // namespace sample
}  // namespace services
}  // namespace stan
#endif
