#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright (c) 2008-2022
#  National Technology and Engineering Solutions of Sandia, LLC
#  Under the terms of Contract DE-NA0003525 with National Technology and
#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
#  rights in this software.
#  This software is distributed under the 3-clause BSD License.
#  ___________________________________________________________________________

"""Tests for the MindtPy solver."""
from pyomo.core.expr.calculus.diff_with_sympy import differentiate_available
import pyomo.common.unittest as unittest
from pyomo.contrib.mindtpy.tests.eight_process_problem import \
    EightProcessFlowsheet
from pyomo.contrib.mindtpy.tests.MINLP_simple import SimpleMINLP as SimpleMINLP
from pyomo.contrib.mindtpy.tests.MINLP2_simple import SimpleMINLP as SimpleMINLP2
from pyomo.contrib.mindtpy.tests.MINLP3_simple import SimpleMINLP as SimpleMINLP3
from pyomo.contrib.mindtpy.tests.MINLP4_simple import SimpleMINLP4
from pyomo.contrib.mindtpy.tests.MINLP5_simple import SimpleMINLP5
from pyomo.contrib.mindtpy.tests.from_proposal import ProposalModel
from pyomo.contrib.mindtpy.tests.constraint_qualification_example import ConstraintQualificationExample
from pyomo.contrib.mindtpy.tests.online_doc_example import OnlineDocExample
from pyomo.environ import SolverFactory, value, maximize
from pyomo.solvers.tests.models.LP_unbounded import LP_unbounded
from pyomo.solvers.tests.models.QCP_simple import QCP_simple
from pyomo.opt import TerminationCondition


full_model_list = [EightProcessFlowsheet(convex=True),
                   ConstraintQualificationExample(),
                   SimpleMINLP(),
                   SimpleMINLP2(),
                   SimpleMINLP3(),
                   SimpleMINLP4(),
                   SimpleMINLP5(),
                   ProposalModel(),
                   OnlineDocExample()
                   ]
model_list = [EightProcessFlowsheet(convex=True),
              ConstraintQualificationExample(),
              SimpleMINLP2(),
              ]
nonconvex_model_list = [EightProcessFlowsheet(convex=False)]

obj_nonlinear_sum_model_list = [SimpleMINLP(), SimpleMINLP5()]

LP_model = LP_unbounded()
LP_model._generate_model()

QCP_model = QCP_simple()
QCP_model._generate_model()
extreme_model_list = [LP_model.model, QCP_model.model]

required_solvers = ('ipopt', 'glpk')
if all(SolverFactory(s).available() for s in required_solvers):
    subsolvers_available = True
else:
    subsolvers_available = False


@unittest.skipIf(not subsolvers_available,
                 'Required subsolvers %s are not available'
                 % (required_solvers,))
@unittest.skipIf(not differentiate_available,
                 'Symbolic differentiation is not available')
class TestMindtPy(unittest.TestCase):
    """Tests for the MindtPy solver plugin."""

    def check_optimal_solution(self, model, places=1):
        for var in model.optimal_solution:
            self.assertAlmostEqual(var.value, model.optimal_solution[var], places=places)

    def test_OA_rNLP(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='rNLP',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_extreme_model(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in extreme_model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='rNLP',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

    def test_OA_L2_norm(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='rNLP',
                                    feasibility_norm='L2',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_L_infinity_norm(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='rNLP',
                                    feasibility_norm='L_infinity',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_max_binary(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='max_binary',
                                    feasibility_norm='L1',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_sympy(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    differentiate_mode='sympy',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_initial_binary(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='initial_binary',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_no_good_cuts(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    add_no_good_cuts=True
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    @unittest.skipUnless(SolverFactory('cplex').available() or SolverFactory('gurobi').available(), 
                         "CPLEX or GUROBI not available.")
    def test_OA_quadratic_strategy(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            model = ProposalModel()
            if SolverFactory('cplex').available():
                mip_solver = 'cplex'
            elif SolverFactory('gurobi').available():
                mip_solver = 'gurobi'
            for quadratic_strategy in (0, 1, 2):
                results = opt.solve(model, strategy='OA',
                                    mip_solver=mip_solver,
                                    nlp_solver=required_solvers[0],
                                    quadratic_strategy=quadratic_strategy,
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)
    
    @unittest.skipUnless(SolverFactory('appsi_cplex').available(exception_flag=False), 
                         "APPSI_CPLEX not available.")
    def test_OA_APPSI_solver(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    mip_solver='appsi_cplex',
                                    nlp_solver=required_solvers[0]
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)

    @unittest.skipUnless(SolverFactory('appsi_ipopt').available(exception_flag=False), 
                         "APPSI_IPOPT not available.")
    def test_OA_APPSI_ipopt(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    mip_solver=required_solvers[1],
                                    nlp_solver='appsi_ipopt'
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)

    def test_OA_integer_to_binary(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    integer_to_binary=True
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_partition_obj_nonlinear_terms(self):
        """Test the outer approximation decomposition algorithm (partition_obj_nonlinear_terms)."""
        with SolverFactory('mindtpy') as opt:
            for model in obj_nonlinear_sum_model_list:
                results = opt.solve(model, strategy='OA',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    partition_obj_nonlinear_terms=True
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_add_slack(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in model_list:
                results = opt.solve(model, strategy='OA',
                                    init_strategy='initial_binary',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    add_slack=True
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

                results = opt.solve(model, strategy='OA',
                                    init_strategy='rNLP',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    add_slack=True
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_OA_nonconvex(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            for model in nonconvex_model_list:
                results = opt.solve(model, strategy='OA',
                                    mip_solver=required_solvers[1],
                                    nlp_solver=required_solvers[0],
                                    heuristic_nonconvex=True
                                    )

                self.assertIn(results.solver.termination_condition,
                              [TerminationCondition.optimal, TerminationCondition.feasible])
                self.assertAlmostEqual(
                    value(model.objective.expr), model.optimal_value, places=1)
                self.check_optimal_solution(model)

    def test_iteration_limit(self):
        with SolverFactory('mindtpy') as opt:
            model = ConstraintQualificationExample()
            opt.solve(model, strategy='OA',
                      iteration_limit=1,
                      mip_solver=required_solvers[1],
                      nlp_solver=required_solvers[0]
                      )
            # self.assertAlmostEqual(value(model.objective.expr), 3, places=2)

    def test_time_limit(self):
        with SolverFactory('mindtpy') as opt:
            model = ConstraintQualificationExample()
            opt.solve(model, strategy='OA',
                      time_limit=1,
                      mip_solver=required_solvers[1],
                      nlp_solver=required_solvers[0]
                      )

    def test_maximize_obj(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            model = ProposalModel()
            model.objective.sense = maximize
            opt.solve(model, strategy='OA',
                      mip_solver=required_solvers[1],
                      nlp_solver=required_solvers[0],
                      )
            self.assertAlmostEqual(
                value(model.objective.expr), 14.83, places=1)

    def test_infeasible_model(self):
        """Test the outer approximation decomposition algorithm."""
        with SolverFactory('mindtpy') as opt:
            model = SimpleMINLP()
            model.X[1].fix(0)
            model.Y[1].fix(0)
            results = opt.solve(model, strategy='OA',
                                mip_solver=required_solvers[1],
                                nlp_solver=required_solvers[0],
                                )
            self.assertIs(results.solver.termination_condition, TerminationCondition.infeasible)


if __name__ == '__main__':
    unittest.main()
