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

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

class AgradCdfExponential : public AgradCdfTest {
 public:
  void valid_values(vector<vector<double> >& parameters, vector<double>& cdf) {
    vector<double> param(2);

    param[0] = 2.0;  // y
    param[1] = 1.5;  // beta
    parameters.push_back(param);
    cdf.push_back(0.95021293163213605702);  // expected cdf

    param[0] = 15.0;  // y
    param[1] = 3.9;   // beta
    parameters.push_back(param);
    cdf.push_back(0.99999999999999999999999996);  // expected cdf
  }

  void invalid_values(vector<size_t>& index, vector<double>& value) {
    // y
    index.push_back(0U);
    value.push_back(-1.0);

    index.push_back(0U);
    value.push_back(-numeric_limits<double>::infinity());

    // beta
    index.push_back(1U);
    value.push_back(0.0);

    index.push_back(1U);
    value.push_back(-1.0);

    index.push_back(1U);
    value.push_back(numeric_limits<double>::infinity());

    index.push_back(1U);
    value.push_back(-numeric_limits<double>::infinity());
  }

  bool has_lower_bound() { return true; }

  double lower_bound() { return 0.0; }

  bool has_upper_bound() { return false; }

  template <typename T_y, typename T_inv_scale, typename T2, typename T3,
            typename T4, typename T5>
  stan::return_type_t<T_y, T_inv_scale> cdf(const T_y& y,
                                            const T_inv_scale& beta, const T2&,
                                            const T3&, const T4&, const T5&) {
    return stan::math::exponential_cdf(y, beta);
  }

  template <typename T_y, typename T_inv_scale, typename T2, typename T3,
            typename T4, typename T5>
  stan::return_type_t<T_y, T_inv_scale> cdf_function(const T_y& y,
                                                     const T_inv_scale& beta,
                                                     const T2&, const T3&,
                                                     const T4&, const T5&) {
    using std::exp;
    using std::log;

    return (1.0 - exp(-beta * y));
  }
};
