// Arguments: Ints, Doubles
#include <stan/math/prim.hpp>

using stan::math::var;
using std::numeric_limits;
using std::vector;

class AgradDistributionsBernoulliLogistic : public AgradDistributionTest {
 public:
  void valid_values(vector<vector<double> >& parameters,
                    vector<double>& log_prob) {
    using stan::math::logit;
    using std::exp;
    vector<double> param(2);

    param[0] = 1;            // n
    param[1] = logit(0.25);  // theta
    parameters.push_back(param);
    log_prob.push_back(log(0.25));  // expected log_prob

    param[0] = 0;            // n
    param[1] = logit(0.25);  // theta
    parameters.push_back(param);
    log_prob.push_back(log(0.75));  // expected log_prob

    param[0] = 1;            // n
    param[1] = logit(0.01);  // theta
    parameters.push_back(param);
    log_prob.push_back(log(0.01));  // expected log_prob

    param[0] = 0;            // n
    param[1] = logit(0.01);  // theta
    parameters.push_back(param);
    log_prob.push_back(log(0.99));  // expected log_prob

    param[0] = 0;   // n
    param[1] = 25;  // theta
    parameters.push_back(param);
    log_prob.push_back(-25);  // expected log_prob

    param[0] = 1;    // n
    param[1] = -25;  // theta
    parameters.push_back(param);
    log_prob.push_back(-25);  // expected log_prob

    param[0] = 0;    // n
    param[1] = -25;  // theta
    parameters.push_back(param);
    log_prob.push_back(-exp(-25));  // expected log_prob
  }

  void invalid_values(vector<size_t>& index, vector<double>& value) {
    // y
    index.push_back(0U);
    value.push_back(-1);

    index.push_back(0U);
    value.push_back(2);

    // theta
  }

  template <class T_n, class T_prob, typename T2, typename T3, typename T4,
            typename T5>
  stan::return_type_t<T_n, T_prob> log_prob(const T_n& n, const T_prob& theta,
                                            const T2&, const T3&, const T4&,
                                            const T5&) {
    return stan::math::bernoulli_logit_log(n, theta);
  }

  template <bool propto, class T_n, class T_prob, typename T2, typename T3,
            typename T4, typename T5>
  stan::return_type_t<T_n, T_prob> log_prob(const T_n& n, const T_prob& theta,
                                            const T2&, const T3&, const T4&,
                                            const T5&) {
    return stan::math::bernoulli_logit_log<propto>(n, theta);
  }

  template <class T_n, class T_prob, typename T2, typename T3, typename T4,
            typename T5>
  stan::return_type_t<T_n, T_prob> log_prob_function(const T_n& n,
                                                     const T_prob& theta,
                                                     const T2&, const T3&,
                                                     const T4&, const T5&) {
    using stan::math::log1m;
    using std::log;
    T_prob ntheta = (2 * n - 1) * theta;
    const static double cutoff = 20.0;
    if (ntheta > cutoff)
      return -exp(-ntheta);
    else if (ntheta < -cutoff)
      return ntheta;
    else
      return -log(1 + exp(-ntheta));
    return 0.0;
  }
};
