#include "MPC.h"
#include <cppad/cppad.hpp>
#include <cppad/ipopt/solve.hpp>
#include "Eigen-3.3/Eigen/Core"

using CppAD::AD;

#define CARWIDTH 3.5
#define CARLENGTH 8.5
#define GRANNYRADIUS 0.54
#define BABYRADIUS 0.43

// TODO: Set the timestep length and duration
size_t N = 15;
//double dt = 0.05;

// This value assumes the model presented in the classroom is used.
//
// It was obtained by measuring the radius formed by running the vehicle in the
// simulator around in a circle with a constant steering angle and velocity on a
// flat terrain.
//
// Lf was tuned until the the radius formed by the simulating the model
// presented in the classroom matched the previous radius.
//
// This is the length from front to CoG that has a similar radius.
const double Lf = 2.184738;

//double ref_v = 50;
size_t x_start = 0;
size_t y_start = x_start + N;
size_t psi_start = y_start + N;
size_t v_start = psi_start + N;
size_t cte_start = v_start + N;
size_t epsi_start = cte_start + N;
size_t delta_start = epsi_start + N;
size_t a_start = delta_start + N - 1;

class FG_eval {
 public:
  // Fitted polynomial coefficients
  Eigen::VectorXd coeffs;
  std::vector<double > constants;
  std::vector<double > grannies_x;
  std::vector<double > grannies_y;
  std::vector<double > babies_x;
  std::vector<double > babies_y;
  FG_eval(Eigen::VectorXd coeffs, std::vector<double> constants, std::vector<double> grannies_x, std::vector<double> grannies_y, std::vector<double> babies_x, std::vector<double> babies_y) { this->coeffs = coeffs; this->constants = constants; this->grannies_x = grannies_x; this->grannies_y = grannies_y; this->babies_x = babies_x; this->babies_y = babies_y;}

  typedef CPPAD_TESTVECTOR(AD<double>) ADvector;
  void operator()(ADvector& fg, const ADvector& vars) {

    double ref_v = constants[2];
    double dt = constants[0];

    // TODO: implement MPC
    // `fg` a vector of the cost constraints, `vars` is a vector of variable values (state & actuators)
    // NOTE: You'll probably go back and forth between this function and
    // the Solver function below.
    // The cost is stored is the first element of `fg`.
    // Any additions to the cost should be added to `fg[0]`.
    fg[0] = 0;

    // Reference State Cost
    // TODO: Define the cost related the reference state and
    // any anything you think may be beneficial.
    for (int i = 0; i < N; i++) {
      fg[0] += constants[3]*CppAD::pow(vars[cte_start + i], 2);  // Don't stray off the track
      fg[0] += constants[4]*CppAD::pow(vars[epsi_start + i], 2); // Stay in same direction as track
      fg[0] += constants[5]*CppAD::pow(vars[v_start + i] - ref_v, 2); // Don't drive away from the speed limit
    }

    for (int i = 0; i < N - 1; i++) {
      fg[0] += constants[6]*CppAD::pow(vars[delta_start + i], 2); // Don't steer too sharply
      fg[0] += constants[7]*CppAD::pow(vars[a_start + i], 2);     // Don't accelerate too much
      // try adding penalty for speed + steer
      fg[0] += constants[8]*CppAD::pow(vars[delta_start + i] * vars[v_start+i], 2); // Don't move quickly while steering
    }

    for (int i = 0; i < N - 2; i++) {
      fg[0] += constants[9]*CppAD::pow(vars[delta_start + i + 1] - vars[delta_start + i], 2); // Don't change steering direction between steps
      fg[0] += constants[10]*CppAD::pow(vars[a_start + i + 1] - vars[a_start + i], 2); // Don't change acceleration between steps
    }

    int numPoints = 4;
    for (int i = 0; i<N -2; i++) {
      CppAD::AD<double> Ax = vars[x_start + i] + 0.5*CARLENGTH;
      CppAD::AD<double> Ay = vars[y_start + i] + 0.5*CARWIDTH;
      CppAD::AD<double> Cx = vars[x_start + i] - 0.5*CARLENGTH;
      CppAD::AD<double> Cy = vars[y_start + i] - 0.5*CARWIDTH;
      vector<CppAD::AD<double>> border_x;
      vector<CppAD::AD<double>> border_y;

      for (int point = 0; point <= numPoints; point++){
        double distanceFraction = (double)point/(double)numPoints;
        border_x.push_back(Ax);
        border_y.push_back(Ay - distanceFraction * CARWIDTH);
        border_x.push_back(Ax - distanceFraction * CARLENGTH);
        border_y.push_back(Ay);

        border_x.push_back(Cx);
        border_y.push_back(Cy + distanceFraction * CARWIDTH);
        border_x.push_back(Cx + distanceFraction * CARLENGTH);
        border_y.push_back(Cy);

      }

      vector<CppAD::AD<double>> transformed_border_x;
      vector<CppAD::AD<double>> transformed_border_y;

      // loop through all points and transform
      for (int j = 0; j < border_x.size(); j++){
        CppAD::AD<double> dx = border_x[j] - vars[x_start + i];
        CppAD::AD<double> dy = border_y[j] - vars[y_start + i];
        transformed_border_x.push_back(vars[x_start + i] + dx * cos(vars[psi_start + i]) - dy * sin(vars[psi_start + i]));
        transformed_border_y.push_back(vars[y_start + i] + dx * sin(vars[psi_start + i]) + dy * cos(vars[psi_start + i]));
      }



      for (int j = 0; j < grannies_x.size(); j++){
        CppAD::AD<double> min_granny_distance = 1000000000;
        for (int k = 0; k<border_x.size(); k++) {
          CppAD::AD<double> current_distance = CppAD::sqrt(CppAD::pow(transformed_border_x[k] - grannies_x[j], 2) + CppAD::pow(transformed_border_y[k] - grannies_y[j], 2));
          if (current_distance < min_granny_distance)
            min_granny_distance = current_distance;
        }
        min_granny_distance -= GRANNYRADIUS;
        if (min_granny_distance<=0.0)
        {
          min_granny_distance = 0.0000000001;
          cout << "I'M GONNA RUN OVER A GRANNY!" << endl;
        }
        fg[0] += constants[11]/min_granny_distance;
        fg[0] += constants[13]*vars[v_start+i]/min_granny_distance;
      }

      for (int j = 0; j < babies_x.size(); j++){
        CppAD::AD<double> min_baby_distance = 1000000000;
        for (int k = 0; k<border_x.size(); k++) {
          CppAD::AD<double> current_distance = CppAD::sqrt(CppAD::pow(transformed_border_x[k] - babies_x[j], 2) + CppAD::pow(transformed_border_y[k] - babies_y[j], 2));
          if (current_distance < min_baby_distance)
            min_baby_distance = current_distance;
        }
        min_baby_distance -= GRANNYRADIUS;
        if (min_baby_distance<=0.0)
        {
          min_baby_distance = 0.0000000001;
          cout << "I'M GONNA RUN OVER A BABY!" << endl;
        }
        fg[0] += constants[12]/min_baby_distance;
        fg[0] += constants[14]*vars[v_start+i]/min_baby_distance;
      }

    }


    // for (int i = 0; i < N - 2; i++) {
    //   for (int j = 0; j < grannies_x.size(); j++){
    //     fg[0] += constants[11]/CppAD::pow(CppAD::sqrt(CppAD::pow(vars[x_start + i] - grannies_x[j], 2) + CppAD::pow(vars[y_start + i] - grannies_y[j], 2)), 2);
    //   }
    // }

    // for (int i = 0; i < N - 2; i++) {
    //   for (int j = 0; j < babies_x.size(); j++){
    //     fg[0] += constants[12]/CppAD::pow(CppAD::sqrt(CppAD::pow(vars[x_start + i] - babies_x[j], 2) + CppAD::pow(vars[y_start + i] - babies_y[j], 2)), 2);
    //   }
    // }

    //
    // Setup Constraints
    //
    // NOTE: In this section you'll setup the model constraints.

    // Initial constraints
    //
    // We add 1 to each of the starting indices due to cost being located at
    // index 0 of `fg`.
    // This bumps up the position of all the other values.
    fg[1 + x_start] = vars[x_start];
    fg[1 + y_start] = vars[y_start];
    fg[1 + psi_start] = vars[psi_start];
    fg[1 + v_start] = vars[v_start];
    fg[1 + cte_start] = vars[cte_start];
    fg[1 + epsi_start] = vars[epsi_start];

    // The rest of the constraints
    for (int t = 1; t < N; t++) {
      AD<double> x1 = vars[x_start + t];
      AD<double> x0 = vars[x_start + t - 1];
      AD<double> y1 = vars[y_start + t];
      AD<double> y0 = vars[y_start + t - 1];
      AD<double> psi1 = vars[psi_start + t];
      AD<double> psi0 = vars[psi_start + t - 1];
      AD<double> v1 = vars[v_start + t];
      AD<double> v0 = vars[v_start + t - 1];
      AD<double> cte1 = vars[cte_start + t];
      AD<double> cte0 = vars[cte_start + t - 1];
      AD<double> epsi1 = vars[epsi_start + t];
      AD<double> epsi0 = vars[epsi_start + t - 1];
      AD<double> a = vars[a_start + t - 1];
      AD<double> delta = vars[delta_start + t - 1];
      if (t > 1) {   // use previous actuations (to account for latency)
        a = vars[a_start + t - 2];
        delta = vars[delta_start + t - 2];
      }
      // AD<double> f0 = coeffs[0] + coeffs[1] * x0 + coeffs[2] * CppAD::pow(x0, 2) + coeffs[3] * CppAD::pow(x0, 3)+ coeffs[4] * CppAD::pow(x0, 4);
      // AD<double> psides0 = CppAD::atan(coeffs[1] + 2 * coeffs[2] * x0 + 3 * coeffs[3] * CppAD::pow(x0, 2)+ 4 * coeffs[4] * CppAD::pow(x0, 3));

      AD<double> f0 = coeffs[0] + coeffs[1] * x0 + coeffs[2] * CppAD::pow(x0, 2) + coeffs[3] * CppAD::pow(x0, 3);
      AD<double> psides0 = CppAD::atan(coeffs[1] + 2 * coeffs[2] * x0 + 3 * coeffs[3] * CppAD::pow(x0, 2));

      // Here's `x` to get you started.
      // The idea here is to constraint this value to be 0.
      //
      // NOTE: The use of `AD<double>` and use of `CppAD`!
      // This is also CppAD can compute derivatives and pass
      // these to the solver.

      // TODO: Setup the rest of the model constraints
      fg[1 + x_start + t] = x1 - (x0 + v0 * CppAD::cos(psi0) * dt);
      fg[1 + y_start + t] = y1 - (y0 + v0 * CppAD::sin(psi0) * dt);
      fg[1 + psi_start + t] = psi1 - (psi0 - v0/Lf * delta * dt);
      fg[1 + v_start + t] = v1 - (v0 + a * dt);
      fg[1 + cte_start + t] = cte1 - ((f0 - y0) + (v0 * CppAD::sin(epsi0) * dt));
      fg[1 + epsi_start + t] = epsi1 - ((psi0 - psides0) - v0/Lf * delta * dt);
    }
  }
};

//
// MPC class definition implementation.
//
MPC::MPC() {}
MPC::~MPC() {}

vector<double> MPC::Solve(Eigen::VectorXd state, Eigen::VectorXd coeffs, std::vector< double > constants, std::vector< double > grannies_x, std::vector< double > grannies_y, std::vector< double > babies_x, std::vector< double > babies_y) {
  bool ok = true;
  size_t i;
  typedef CPPAD_TESTVECTOR(double) Dvector;

  double x = state[0];
  double y = state[1];
  double psi = state[2];
  double v = state[3];
  double cte = state[4];
  double epsi = state[5];

  // TODO: Set the number of model variables (includes both states and inputs).
  // For example: If the state is a 4 element vector, the actuators is a 2
  // element vector and there are 10 timesteps. The number of variables is:
  //
  // 4 * 10 + 2 * 9
  size_t n_vars = N * 6 + (N - 1) * 2;
  // TODO: Set the number of constraints
  size_t n_constraints = N * 6;

  // Initial value of the independent variables.
  // SHOULD BE 0 besides initial state.
  Dvector vars(n_vars);
  for (int i = 0; i < n_vars; i++) {
    vars[i] = 0;
  }

  Dvector vars_lowerbound(n_vars);
  Dvector vars_upperbound(n_vars);
  // TODO: Set lower and upper limits for variables.

  // Set the initial variable values
  vars[x_start] = x;
  vars[y_start] = y;
  vars[psi_start] = psi;
  vars[v_start] = v;
  vars[cte_start] = cte;
  vars[epsi_start] = epsi;

  // Set all non-actuators upper and lowerlimits
  // to the max negative and positive values.
  for (int i = 0; i < delta_start; i++) {
    vars_lowerbound[i] = -1.0e19;
    vars_upperbound[i] = 1.0e19;
  }

  // The upper and lower limits of delta are set to -25 and 25
  // degrees (values in radians).
  // NOTE: Feel free to change this to something else.
  for (int i = delta_start; i < a_start; i++) {
    vars_lowerbound[i] = -0.436332;
    vars_upperbound[i] = 0.436332;
  }

  // Acceleration/decceleration upper and lower limits.
  // NOTE: Feel free to change this to something else.
  for (int i = a_start; i < n_vars; i++) {
    vars_lowerbound[i] = -4.0;
    vars_upperbound[i] = 3.0;
  }

  // Lower and upper limits for the constraints
  // Should be 0 besides initial state.
  Dvector constraints_lowerbound(n_constraints);
  Dvector constraints_upperbound(n_constraints);
  for (int i = 0; i < n_constraints; i++) {
    constraints_lowerbound[i] = 0;
    constraints_upperbound[i] = 0;
  }
  constraints_lowerbound[x_start] = x;
  constraints_lowerbound[y_start] = y;
  constraints_lowerbound[psi_start] = psi;
  constraints_lowerbound[v_start] = v;
  constraints_lowerbound[cte_start] = cte;
  constraints_lowerbound[epsi_start] = epsi;

  constraints_upperbound[x_start] = x;
  constraints_upperbound[y_start] = y;
  constraints_upperbound[psi_start] = psi;
  constraints_upperbound[v_start] = v;
  constraints_upperbound[cte_start] = cte;
  constraints_upperbound[epsi_start] = epsi;

  // object that computes objective and constraints
  FG_eval fg_eval(coeffs, constants, grannies_x, grannies_y, babies_x, babies_y);

  //
  // NOTE: You don't have to worry about these options
  //
  // options for IPOPT solver
  std::string options;
  // Uncomment this if you'd like more print information
  options += "Integer print_level  0\n";
  // NOTE: Setting sparse to true allows the solver to take advantage
  // of sparse routines, this makes the computation MUCH FASTER. If you
  // can uncomment 1 of these and see if it makes a difference or not but
  // if you uncomment both the computation time should go up in orders of
  // magnitude.
  options += "Sparse  true        forward\n";
  options += "Sparse  true        reverse\n";
  // NOTE: Currently the solver has a maximum time limit of 0.5 seconds.
  // Change this as you see fit.
  options += "Numeric max_cpu_time         " + std::to_string(constants[0]*0.8f) + "\n";

  // place to return solution
  CppAD::ipopt::solve_result<Dvector> solution;

  // solve the problem
  CppAD::ipopt::solve<Dvector, FG_eval>(
      options, vars, vars_lowerbound, vars_upperbound, constraints_lowerbound,
      constraints_upperbound, fg_eval, solution);

  // Check some of the solution values
  ok &= solution.status == CppAD::ipopt::solve_result<Dvector>::success;

  // Cost
  auto cost = solution.obj_value;
  //std::cout << "Cost " << cost << std::endl;

  // TODO: Return the first actuator values. The variables can be accessed with
  // `solution.x[i]`.
  //
  // {...} is shorthand for creating a vector, so auto x1 = {1.0,2.0}
  // creates a 2 element double vector.
  vector<double> result;

  result.push_back(solution.x[delta_start]);
  result.push_back(solution.x[a_start]);

  // for (int i = 0; i < N-1; i++) {
  //   result.push_back(solution.x[x_start + i + 1]);
  //   result.push_back(solution.x[y_start + i + 1]);
  // }

  for (int i = 0; i < N-1; i++) {
      int numPoints = 4;
      CppAD::AD<double> Ax = solution.x[x_start + i] + 0.5*CARLENGTH;
      CppAD::AD<double> Ay = solution.x[y_start + i] + 0.5*CARWIDTH;
      CppAD::AD<double> Cx = solution.x[x_start + i] - 0.5*CARLENGTH;
      CppAD::AD<double> Cy = solution.x[y_start + i] - 0.5*CARWIDTH;
      vector<CppAD::AD<double>> border_x;
      vector<CppAD::AD<double>> border_y;

      for (int point = 0; point <= numPoints; point++){
        double distanceFraction = (double)point/(double)numPoints;
        border_x.push_back(Ax);
        border_y.push_back(Ay - distanceFraction * CARWIDTH);
        border_x.push_back(Ax - distanceFraction * CARLENGTH);
        border_y.push_back(Ay);

        border_x.push_back(Cx);
        border_y.push_back(Cy + distanceFraction * CARWIDTH);
        border_x.push_back(Cx + distanceFraction * CARLENGTH);
        border_y.push_back(Cy);

      }

      vector<CppAD::AD<double>> transformed_border_x;
      vector<CppAD::AD<double>> transformed_border_y;

      // loop through all points and transform
      for (int j = 0; j < border_x.size(); j++){
        CppAD::AD<double> dx = border_x[j] - solution.x[x_start + i];
        CppAD::AD<double> dy = border_y[j] - solution.x[y_start + i];
        result.push_back(CppAD::Value(solution.x[x_start + i] + dx * cos(solution.x[psi_start + i]) - dy * sin(solution.x[psi_start + i])));
        result.push_back(CppAD::Value(solution.x[y_start + i] + dx * sin(solution.x[psi_start + i]) + dy * cos(solution.x[psi_start + i])));
      }
  }
  return result;
}