#ifndef STAN_MATH_PRIM_SCAL_PROB_BERNOULLI_LPMF_HPP
#define STAN_MATH_PRIM_SCAL_PROB_BERNOULLI_LPMF_HPP

#include <stan/math/prim/scal/meta/is_constant_struct.hpp>
#include <stan/math/prim/scal/meta/partials_return_type.hpp>
#include <stan/math/prim/scal/meta/OperandsAndPartials.hpp>
#include <stan/math/prim/scal/err/check_consistent_sizes.hpp>
#include <stan/math/prim/scal/err/check_bounded.hpp>
#include <stan/math/prim/scal/err/check_finite.hpp>
#include <stan/math/prim/scal/err/check_not_nan.hpp>
#include <stan/math/prim/scal/fun/constants.hpp>
#include <stan/math/prim/scal/fun/inv_logit.hpp>
#include <stan/math/prim/scal/fun/log1m.hpp>
#include <stan/math/prim/scal/fun/value_of.hpp>
#include <stan/math/prim/scal/meta/include_summand.hpp>
#include <stan/math/prim/scal/meta/scalar_seq_view.hpp>
#include <boost/random/bernoulli_distribution.hpp>
#include <boost/random/variate_generator.hpp>
#include <cmath>

namespace stan {
  namespace math {

    /**
     * Returns the log PMF of the Bernoulli distribution. If containers are 
     * supplied, returns the log sum of the probabilities.
     *
     * @tparam T_n type of integer parameters
     * @tparam T_prob type of chance of success parameters
     * @param n integer parameter
     * @param theta chance of success parameter
     * @return log probability or log sum of probabilities
     * @throw std::domain_error if theta is not a valid probability
     * @throw std::invalid_argument if container sizes mismatch.
     */
    template <bool propto, typename T_n, typename T_prob>
    typename return_type<T_prob>::type
    bernoulli_lpmf(const T_n& n,
                  const T_prob& theta) {
      static const char* function("bernoulli_lpmf");
      typedef typename stan::partials_return_type<T_n, T_prob>::type
        T_partials_return;

      using std::log;

      if (!(stan::length(n)
            && stan::length(theta)))
        return 0.0;

      T_partials_return logp(0.0);

      check_bounded(function, "n", n, 0, 1);
      check_finite(function, "Probability parameter", theta);
      check_bounded(function, "Probability parameter", theta, 0.0, 1.0);
      check_consistent_sizes(function,
                             "Random variable", n,
                             "Probability parameter", theta);

      if (!include_summand<propto, T_prob>::value)
        return 0.0;

      scalar_seq_view<const T_n> n_vec(n);
      scalar_seq_view<const T_prob> theta_vec(theta);
      size_t N = max_size(n, theta);
      OperandsAndPartials<T_prob> operands_and_partials(theta);

      if (length(theta) == 1) {
        size_t sum = 0;
        for (size_t n = 0; n < N; n++) {
          sum += value_of(n_vec[n]);
        }
        const T_partials_return theta_dbl = value_of(theta_vec[0]);
        // avoid nans when sum == N or sum == 0
        if (sum == N) {
          logp += N * log(theta_dbl);
          if (!is_constant_struct<T_prob>::value)
            operands_and_partials.d_x1[0] += N / theta_dbl;
        } else if (sum == 0) {
          logp += N * log1m(theta_dbl);
          if (!is_constant_struct<T_prob>::value)
            operands_and_partials.d_x1[0] += N / (theta_dbl - 1);
        } else {
          const T_partials_return log_theta = log(theta_dbl);
          const T_partials_return log1m_theta = log1m(theta_dbl);

          logp += sum * log_theta;
          logp += (N - sum) * log1m_theta;

          if (!is_constant_struct<T_prob>::value) {
            operands_and_partials.d_x1[0] += sum / theta_dbl;
            operands_and_partials.d_x1[0] += (N - sum) / (theta_dbl - 1);
          }
        }
      } else {
        for (size_t n = 0; n < N; n++) {
          const int n_int = value_of(n_vec[n]);
          const T_partials_return theta_dbl = value_of(theta_vec[n]);

          if (n_int == 1)
            logp += log(theta_dbl);
          else
            logp += log1m(theta_dbl);

          if (!is_constant_struct<T_prob>::value) {
            if (n_int == 1)
              operands_and_partials.d_x1[n] += 1.0 / theta_dbl;
            else
              operands_and_partials.d_x1[n] += 1.0 / (theta_dbl - 1);
          }
        }
      }
      return operands_and_partials.value(logp);
    }

    template <typename T_y, typename T_prob>
    inline
    typename return_type<T_prob>::type
    bernoulli_lpmf(const T_y& n,
                  const T_prob& theta) {
      return bernoulli_lpmf<false>(n, theta);
    }

  }
}
#endif
