#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

from typing import Any, List

from miplearn.features import VariableFeatures, ConstraintFeatures
from miplearn.solvers.internal import InternalSolver

inf = float("inf")

# NOTE:
# This file is in the main source folder, so that it can be called from Julia.


def _round(obj: Any) -> Any:
    if obj is None:
        return None
    if isinstance(obj, float):
        return round(obj, 6)
    if isinstance(obj, tuple):
        return tuple([_round(v) for v in obj])
    if isinstance(obj, list):
        return [_round(v) for v in obj]
    if isinstance(obj, dict):
        return {key: _round(value) for (key, value) in obj.items()}
    if isinstance(obj, VariableFeatures):
        obj.__dict__ = _round(obj.__dict__)
    if isinstance(obj, ConstraintFeatures):
        obj.__dict__ = _round(obj.__dict__)
    return obj


def _filter_attrs(allowed_keys: List[str], obj: Any) -> Any:
    for key in obj.__dict__.keys():
        if key not in allowed_keys:
            setattr(obj, key, None)
    return obj


def run_internal_solver_tests(solver: InternalSolver) -> None:
    run_basic_usage_tests(solver.clone())
    run_warm_start_tests(solver.clone())
    run_infeasibility_tests(solver.clone())
    run_iteration_cb_tests(solver.clone())
    if solver.are_callbacks_supported():
        run_lazy_cb_tests(solver.clone())


def run_basic_usage_tests(solver: InternalSolver) -> None:
    # Create and set instance
    instance = solver.build_test_instance_knapsack()
    model = instance.to_model()
    solver.set_instance(instance, model)

    # Fetch variables (after-load)
    assert_equals(
        solver.get_variables(),
        VariableFeatures(
            names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
            lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
            upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
            types=["B", "B", "B", "B", "C"],
            obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
        ),
    )

    # Fetch constraints (after-load)
    assert_equals(
        solver.get_constraints(),
        ConstraintFeatures(
            names=("eq_capacity",),
            rhs=(0.0,),
            lhs=(
                (
                    ("x[0]", 23.0),
                    ("x[1]", 26.0),
                    ("x[2]", 20.0),
                    ("x[3]", 18.0),
                    ("z", -1.0),
                ),
            ),
            senses=("=",),
        ),
    )

    # Solve linear programming relaxation
    lp_stats = solver.solve_lp()
    assert not solver.is_infeasible()
    assert lp_stats.lp_value is not None
    assert_equals(round(lp_stats.lp_value, 3), 1287.923)
    assert lp_stats.lp_log is not None
    assert len(lp_stats.lp_log) > 100
    assert lp_stats.lp_wallclock_time is not None
    assert lp_stats.lp_wallclock_time > 0

    # Fetch variables (after-lp)
    assert_equals(
        _round(solver.get_variables(with_static=False)),
        _filter_attrs(
            solver.get_variable_attrs(),
            VariableFeatures(
                names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
                basis_status=["U", "B", "U", "L", "U"],
                reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
                sa_lb_down=[-inf, -inf, -inf, -0.111111, -inf],
                sa_lb_up=[1.0, 0.923077, 1.0, 1.0, 67.0],
                sa_obj_down=[311.384615, 317.777778, 270.769231, -inf, -13.538462],
                sa_obj_up=[inf, 570.869565, inf, 243.692308, inf],
                sa_ub_down=[0.913043, 0.923077, 0.9, 0.0, 43.0],
                sa_ub_up=[2.043478, inf, 2.2, inf, 69.0],
                values=[1.0, 0.923077, 1.0, 0.0, 67.0],
            ),
        ),
    )

    # Fetch constraints (after-lp)
    assert_equals(
        _round(solver.get_constraints(with_static=False)),
        _filter_attrs(
            solver.get_constraint_attrs(),
            ConstraintFeatures(
                basis_status=("N",),
                dual_values=(13.538462,),
                names=("eq_capacity",),
                sa_rhs_down=(-24.0,),
                sa_rhs_up=(2.0,),
                slacks=(0.0,),
            ),
        ),
    )

    # Solve MIP
    mip_stats = solver.solve(
        tee=True,
    )
    assert not solver.is_infeasible()
    assert mip_stats.mip_log is not None
    assert len(mip_stats.mip_log) > 100
    assert mip_stats.mip_lower_bound is not None
    assert_equals(mip_stats.mip_lower_bound, 1183.0)
    assert mip_stats.mip_upper_bound is not None
    assert_equals(mip_stats.mip_upper_bound, 1183.0)
    assert mip_stats.mip_sense is not None
    assert_equals(mip_stats.mip_sense, "max")
    assert mip_stats.mip_wallclock_time is not None
    assert isinstance(mip_stats.mip_wallclock_time, float)
    assert mip_stats.mip_wallclock_time > 0

    # Fetch variables (after-mip)
    assert_equals(
        _round(solver.get_variables(with_static=False)),
        _filter_attrs(
            solver.get_variable_attrs(),
            VariableFeatures(
                names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
                values=[1.0, 0.0, 1.0, 1.0, 61.0],
            ),
        ),
    )

    # Fetch constraints (after-mip)
    assert_equals(
        _round(solver.get_constraints(with_static=False)),
        _filter_attrs(
            solver.get_constraint_attrs(),
            ConstraintFeatures(
                names=("eq_capacity",),
                slacks=(0.0,),
            ),
        ),
    )

    # Build new constraint and verify that it is violated
    cf = ConstraintFeatures(
        names=("cut",),
        lhs=((("x[0]", 1.0),),),
        rhs=(0.0,),
        senses=("<",),
    )
    assert_equals(solver.are_constraints_satisfied(cf), (False,))

    # Add constraint and verify it affects solution
    solver.add_constraints(cf)
    assert_equals(
        _round(solver.get_constraints(with_static=True)),
        _filter_attrs(
            solver.get_constraint_attrs(),
            ConstraintFeatures(
                names=("eq_capacity", "cut"),
                rhs=(0.0, 0.0),
                lhs=(
                    (
                        ("x[0]", 23.0),
                        ("x[1]", 26.0),
                        ("x[2]", 20.0),
                        ("x[3]", 18.0),
                        ("z", -1.0),
                    ),
                    (("x[0]", 1.0),),
                ),
                senses=("=", "<"),
            ),
        ),
    )
    stats = solver.solve()
    assert_equals(stats.mip_lower_bound, 1030.0)
    assert_equals(solver.are_constraints_satisfied(cf), (True,))

    # Remove the new constraint
    solver.remove_constraints(("cut",))

    # New constraint should no longer affect solution
    stats = solver.solve()
    assert_equals(stats.mip_lower_bound, 1183.0)


def run_warm_start_tests(solver: InternalSolver) -> None:
    instance = solver.build_test_instance_knapsack()
    model = instance.to_model()
    solver.set_instance(instance, model)
    solver.set_warm_start({"x[0]": 1.0, "x[1]": 0.0, "x[2]": 0.0, "x[3]": 1.0})
    stats = solver.solve(tee=True)
    if stats.mip_warm_start_value is not None:
        assert_equals(stats.mip_warm_start_value, 725.0)

    solver.set_warm_start({"x[0]": 1.0, "x[1]": 1.0, "x[2]": 1.0, "x[3]": 1.0})
    stats = solver.solve(tee=True)
    assert stats.mip_warm_start_value is None

    solver.fix({"x[0]": 1.0, "x[1]": 0.0, "x[2]": 0.0, "x[3]": 1.0})
    stats = solver.solve(tee=True)
    assert_equals(stats.mip_lower_bound, 725.0)
    assert_equals(stats.mip_upper_bound, 725.0)


def run_infeasibility_tests(solver: InternalSolver) -> None:
    instance = solver.build_test_instance_infeasible()
    solver.set_instance(instance)
    mip_stats = solver.solve()
    assert solver.is_infeasible()
    assert solver.get_solution() is None
    assert mip_stats.mip_upper_bound is None
    assert mip_stats.mip_lower_bound is None
    lp_stats = solver.solve_lp()
    assert solver.get_solution() is None
    assert lp_stats.lp_value is None


def run_iteration_cb_tests(solver: InternalSolver) -> None:
    instance = solver.build_test_instance_knapsack()
    solver.set_instance(instance)
    count = 0

    def custom_iteration_cb() -> bool:
        nonlocal count
        count += 1
        return count < 5

    solver.solve(iteration_cb=custom_iteration_cb)
    assert_equals(count, 5)


def run_lazy_cb_tests(solver: InternalSolver) -> None:
    instance = solver.build_test_instance_knapsack()
    model = instance.to_model()

    def lazy_cb(cb_solver: InternalSolver, cb_model: Any) -> None:
        relsol = cb_solver.get_solution()
        assert relsol is not None
        assert relsol["x[0]"] is not None
        if relsol["x[0]"] > 0:
            instance.enforce_lazy_constraint(cb_solver, cb_model, "cut")

    solver.set_instance(instance, model)
    solver.solve(lazy_cb=lazy_cb)
    solution = solver.get_solution()
    assert solution is not None
    assert_equals(solution["x[0]"], 0.0)


def assert_equals(left: Any, right: Any) -> None:
    assert left == right, f"left:\n{left}\nright:\n{right}"