#ifndef STAN_MCMC_HMC_STATIC_ADAPT_DENSE_E_STATIC_HMC_HPP
#define STAN_MCMC_HMC_STATIC_ADAPT_DENSE_E_STATIC_HMC_HPP

#include <stan/callbacks/logger.hpp>
#include <stan/mcmc/hmc/static/dense_e_static_hmc.hpp>
#include <stan/mcmc/stepsize_covar_adapter.hpp>

namespace stan {
namespace mcmc {
/**
 * Hamiltonian Monte Carlo implementation using the endpoint
 * of trajectories with a static integration time with a
 * Gaussian-Euclidean disintegration and adative dense metric and
 * adaptive step size
 */
template <class Model, class BaseRNG>
class adapt_dense_e_static_hmc : public dense_e_static_hmc<Model, BaseRNG>,
                                 public stepsize_covar_adapter {
 public:
  adapt_dense_e_static_hmc(const Model& model, BaseRNG& rng)
      : dense_e_static_hmc<Model, BaseRNG>(model, rng),
        stepsize_covar_adapter(model.num_params_r()) {}

  ~adapt_dense_e_static_hmc() {}

  sample transition(sample& init_sample, callbacks::logger& logger) {
    sample s
        = dense_e_static_hmc<Model, BaseRNG>::transition(init_sample, logger);

    if (this->adapt_flag_) {
      this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                s.accept_stat());
      this->update_L_();

      bool update = this->covar_adaptation_.learn_covariance(
          this->z_.inv_e_metric_, this->z_.q);

      if (update) {
        this->init_stepsize(logger);
        this->update_L_();

        this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
        this->stepsize_adaptation_.restart();
      }
    }
    return s;
  }

  void disengage_adaptation() {
    base_adapter::disengage_adaptation();
    this->stepsize_adaptation_.complete_adaptation(this->nom_epsilon_);
  }
};

}  // namespace mcmc
}  // namespace stan
#endif
