#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import logging
from abc import ABC, abstractmethod

logger = logging.getLogger(__name__)


class ExtractedConstraint(ABC):
    pass


class InternalSolver(ABC):
    """
    Abstract class representing the MIP solver used internally by LearningSolver.
    """

    @abstractmethod
    def solve_lp(self, tee=False):
        """
        Solves the LP relaxation of the currently loaded instance. After this
        method finishes, the solution can be retrieved by calling `get_solution`.

        Parameters
        ----------
        tee: bool
            If true, prints the solver log to the screen.

        Returns
        -------
        dict
            A dictionary of solver statistics containing the following keys:
            "Optimal value".
        """
        pass

    @abstractmethod
    def get_solution(self):
        """
        Returns current solution found by the solver.

        If called after `solve`, returns the best primal solution found during
        the search. If called after `solve_lp`, returns the optimal solution
        to the LP relaxation.

        The solution is a dictionary `sol`, where the optimal value of `var[idx]`
        is given by `sol[var][idx]`.
        """
        pass

    @abstractmethod
    def set_warm_start(self, solution):
        """
        Sets the warm start to be used by the solver.

        The solution should be a dictionary following the same format as the
        one produced by `get_solution`. Only one warm start is supported.
        Calling this function when a warm start already exists will
        remove the previous warm start.
        """
        pass

    @abstractmethod
    def clear_warm_start(self):
        """
        Removes any existing warm start from the solver.
        """
        pass

    @abstractmethod
    def set_instance(self, instance, model=None):
        """
        Loads the given instance into the solver.

        Parameters
        ----------
        instance: miplearn.Instance
            The instance to be loaded.
        model:
            The concrete optimization model corresponding to this instance
            (e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
            it will be generated by calling `instance.to_model()`.
        """
        pass

    @abstractmethod
    def fix(self, solution):
        """
        Fixes the values of a subset of decision variables.

        The values should be provided in the dictionary format generated by
        `get_solution`. Missing values in the solution indicate variables
        that should be left free.
        """
        pass

    @abstractmethod
    def set_branching_priorities(self, priorities):
        """
        Sets the branching priorities for the given decision variables.

        When the MIP solver needs to decide on which variable to branch, variables
        with higher priority are picked first, given that they are fractional.
        Ties are solved arbitrarily. By default, all variables have priority zero.

        The priorities should be provided in the dictionary format generated by
        `get_solution`. Missing values indicate variables whose priorities
        should not be modified.
        """
        pass

    @abstractmethod
    def add_constraint(self, constraint):
        """
        Adds a single constraint to the model.
        """
        pass

    @abstractmethod
    def solve(self, tee=False):
        """
        Solves the currently loaded instance. After this method finishes,
        the best solution found can be retrieved by calling `get_solution`.

        Parameters
        ----------
        tee: bool
            If true, prints the solver log to the screen.

        Returns
        -------
        dict
            A dictionary of solver statistics containing the following keys:
            "Lower bound", "Upper bound", "Wallclock time", "Nodes", "Sense",
             "Log" and "Warm start value".
        """
        pass

    # @abstractmethod
    def get_constraint_names(self):
        """
        Returns a list of strings, containing the name of each constraint in the
        model.
        """
        pass

    # @abstractmethod
    def extract_constraint(self, cname):
        """
        Removes a given constraint from the model and returns an object `c` which
        can be used to verify if the removed constraint is still satisfied by
        the current solution, using `is_constraint_satisfied(c)`, and can potentially
        be re-added to the model using `add_constraint(c)`.
        """
        pass

    def is_constraint_satisfied(self, c):
        pass

    @abstractmethod
    def set_threads(self, threads):
        pass

    @abstractmethod
    def set_time_limit(self, time_limit):
        pass

    @abstractmethod
    def set_node_limit(self, node_limit):
        pass

    @abstractmethod
    def set_gap_tolerance(self, gap_tolerance):
        pass

    @abstractmethod
    def get_variables(self):
        pass

    def get_empty_solution(self):
        solution = {}
        for (var, indices) in self.get_variables().items():
            solution[var] = {}
            for idx in indices:
                solution[var][idx] = 0.0
        return solution