#  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.

from miplearn import WarmStartComponent, LearningSolver
from miplearn.problems.knapsack import KnapsackInstance
import numpy as np
import tempfile


def _get_instances():
    return [
        KnapsackInstance(
            weights=[23., 26., 20., 18.],
            prices=[505., 352., 458., 220.],
            capacity=67.,
        ),
    ] * 2


# def test_warm_start_save_load():
#     state_file = tempfile.NamedTemporaryFile(mode="r")
#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
#     solver.parallel_solve(_get_instances(), n_jobs=2)
#     solver.fit()
#     comp = solver.components["warm-start"]
#     assert comp.x_train["default"].shape == (8, 6)
#     assert comp.y_train["default"].shape == (8, 2)
#     assert ("default", 0) in comp.predictors.keys()
#     assert ("default", 1) in comp.predictors.keys()
#     solver.save_state(state_file.name)
    
#     solver.solve(_get_instances()[0])
    
#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
#     solver.load_state(state_file.name)
#     comp = solver.components["warm-start"]
#     assert comp.x_train["default"].shape == (8, 6)
#     assert comp.y_train["default"].shape == (8, 2)
#     assert ("default", 0) in comp.predictors.keys()
#     assert ("default", 1) in comp.predictors.keys()