# Copyright 2010-2018 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MIP example that uses a variable array."""
# [START program]
# [START import]
from __future__ import print_function
from ortools.linear_solver import pywraplp

# [END import]


# [START program_part1]
# [START data_model]
def create_data_model():
    """Stores the data for the problem."""
    data = {}
    data['constraint_coeffs'] = [
        [5, 7, 9, 2, 1],
        [18, 4, -9, 10, 12],
        [4, 7, 3, 8, 5],
        [5, 13, 16, 3, -7],
    ]
    data['bounds'] = [250, 285, 211, 315]
    data['obj_coeffs'] = [7, 8, 2, 9, 6]
    data['num_vars'] = 5
    data['num_constraints'] = 4
    return data


# [END data_model]


def main():
    # [START data]
    data = create_data_model()
    # [END data]
    # [END program_part1]
    # [START solver]
    # Create the mip solver with the SCIP backend.
    solver = pywraplp.Solver.CreateSolver('SCIP')

    # [START program_part2]
    # [START variables]
    infinity = solver.infinity()
    x = {}
    for j in range(data['num_vars']):
        x[j] = solver.IntVar(0, infinity, 'x[%i]' % j)
    print('Number of variables =', solver.NumVariables())
    # [END variables]

    # [START constraints]
    for i in range(data['num_constraints']):
        constraint = solver.RowConstraint(0, data['bounds'][i], '')
        for j in range(data['num_vars']):
            constraint.SetCoefficient(x[j], data['constraint_coeffs'][i][j])
    print('Number of constraints =', solver.NumConstraints())
    # In Python, you can also set the constraints as follows.
    # for i in range(data['num_constraints']):
    #  constraint_expr = \
    # [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
    #  solver.Add(sum(constraint_expr) <= data['bounds'][i])
    # [END constraints]

    # [START objective]
    objective = solver.Objective()
    for j in range(data['num_vars']):
        objective.SetCoefficient(x[j], data['obj_coeffs'][j])
    objective.SetMaximization()
    # In Python, you can also set the objective as follows.
    # obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]
    # solver.Maximize(solver.Sum(obj_expr))
    # [END objective]

    # [START solve]
    status = solver.Solve()
    # [END solve]

    # [START print_solution]
    if status == pywraplp.Solver.OPTIMAL:
        print('Objective value =', solver.Objective().Value())
        for j in range(data['num_vars']):
            print(x[j].name(), ' = ', x[j].solution_value())
        print()
        print('Problem solved in %f milliseconds' % solver.wall_time())
        print('Problem solved in %d iterations' % solver.iterations())
        print('Problem solved in %d branch-and-bound nodes' % solver.nodes())
    else:
        print('The problem does not have an optimal solution.')
    # [END print_solution]


if __name__ == '__main__':
    main()
# [END program_part2]
# [END program]