"""
Copyright 2017 Robin Verschueren, 2017 Akshay Agrawal

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 abc

from cvxpy.reductions.reduction import Reduction
from typing import List, Tuple

import numpy as np


def extract_mip_idx(variables) -> Tuple[List[int], List[int]]:
    """Coalesces bool, int indices for variables.

       The indexing scheme assumes that the variables will be coalesced into
       a single one-dimensional variable, with each variable being reshaped
       in Fortran order.
    """
    def ravel_multi_index(multi_index, x, vert_offset):
        """Ravel a multi-index and add a vertical offset to it.
        """
        ravel_idx = np.ravel_multi_index(multi_index, max(x.shape, (1,)), order='F')
        return [(vert_offset + idx,) for idx in ravel_idx]
    boolean_idx = []
    integer_idx = []
    vert_offset = 0
    for x in variables:
        if x.boolean_idx:
            multi_index = list(zip(*x.boolean_idx))
            boolean_idx += ravel_multi_index(multi_index, x, vert_offset)
        if x.integer_idx:
            multi_index = list(zip(*x.integer_idx))
            integer_idx += ravel_multi_index(multi_index, x, vert_offset)
        vert_offset += x.size
    return boolean_idx, integer_idx


class MatrixStuffing(Reduction):
    """Stuffs a problem into a standard form for a family of solvers."""

    __metaclass__ = abc.ABCMeta

    def apply(self, problem) -> None:
        """Returns a stuffed problem.

        The returned problem is a minimization problem in which every
        constraint in the problem has affine arguments that are expressed in
        the form A @ x + b.


        Parameters
        ----------
        problem: The problem to stuff; the arguments of every constraint
            must be affine

        Returns
        -------
        Problem
            The stuffed problem
        InverseData
            Data for solution retrieval
        """
    def invert(self, solution, inverse_data):
        raise NotImplementedError()

    def stuffed_objective(self, problem, inverse_data):
        raise NotImplementedError()
