"""
Copyright 2013 Steven Diamond

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.
"""

import cvxpy.interface as intf
import cvxpy.settings as s
from cvxpy.reductions.solvers.conic_solvers import GLPK
from cvxpy.reductions.solvers.conic_solvers.conic_solver import ConicSolver


class GLPK_MI(GLPK):
    """An interface for the GLPK MI solver.
    """

    # Solver capabilities.
    MIP_CAPABLE = True
    SUPPORTED_CONSTRAINTS = ConicSolver.SUPPORTED_CONSTRAINTS

    def name(self):
        """The name of the solver.
        """
        return s.GLPK_MI

    def apply(self, problem):
        """Returns a new problem and data for inverting the new solution.

        Returns
        -------
        tuple
            (dict of arguments needed for the solver, inverse data)
        """
        data, inv_data = super(GLPK_MI, self).apply(problem)
        var = problem.x
        data[s.BOOL_IDX] = [int(t[0]) for t in var.boolean_idx]
        data[s.INT_IDX] = [int(t[0]) for t in var.integer_idx]
        return data, inv_data

    def solve_via_data(self, data, warm_start, verbose, solver_opts, solver_cache=None):
        import cvxopt
        import cvxopt.solvers
        # Save original cvxopt solver options.
        old_options = cvxopt.glpk.options.copy()
        # Silence cvxopt if verbose is False.
        if verbose:
            cvxopt.glpk.options["msg_lev"] = "GLP_MSG_ON"
        else:
            cvxopt.glpk.options["msg_lev"] = "GLP_MSG_OFF"

        # Apply any user-specific options.
        # Rename max_iters to maxiters.
        if "max_iters" in solver_opts:
            solver_opts["maxiters"] = solver_opts["max_iters"]
        for key, value in solver_opts.items():
            cvxopt.glpk.options[key] = value

        try:
            results_tup = cvxopt.glpk.ilp(data[s.C],
                                          data[s.G],
                                          data[s.H],
                                          data[s.A],
                                          data[s.B],
                                          set(int(i) for i in data[s.INT_IDX]),
                                          set(int(i) for i in data[s.BOOL_IDX]))
            results_dict = {}
            results_dict["status"] = results_tup[0]
            results_dict["x"] = results_tup[1]
        # Catch exceptions in CVXOPT and convert them to solver errors.
        except ValueError:
            results_dict = {"status": "unknown"}

        # Restore original cvxopt solver options.
        self._restore_solver_options(old_options)

        # Convert CVXOPT results to solution format.
        solution = {}
        status = self.STATUS_MAP[results_dict['status']]
        solution[s.STATUS] = status
        if solution[s.STATUS] in s.SOLUTION_PRESENT:
            # No dual variables.
            solution[s.PRIMAL] = intf.cvxopt2dense(results_dict['x'])
            primal_val = (data[s.C].T*results_dict['x'])[0]
            solution[s.VALUE] = primal_val

        return solution
