#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import numpy as np
from scipy.stats import uniform, randint

from miplearn.problems.multiknapsack import (
    MultiKnapsackGenerator,
    MultiKnapsackData,
    build_multiknapsack_model_gurobipy,
)


def test_knapsack_generator() -> None:
    np.random.seed(42)
    gen = MultiKnapsackGenerator(
        n=randint(low=5, high=6),
        m=randint(low=3, high=4),
        w=randint(low=0, high=1000),
        K=randint(low=500, high=501),
        u=uniform(loc=0.0, scale=1.0),
        alpha=uniform(loc=0.25, scale=0.0),
    )
    data = gen.generate(1)
    assert data[0].prices.tolist() == [380.0, 521.0, 729.0, 476.0, 466.0]
    assert data[0].capacities.tolist() == [443.0, 382.0, 389.0]
    assert data[0].weights.tolist() == [
        [102, 435, 860, 270, 106],
        [71, 700, 20, 614, 121],
        [466, 214, 330, 458, 87],
    ]


def test_knapsack_generator_callable() -> None:
    np.random.seed(42)
    gen = MultiKnapsackGenerator(
        n=randint(low=10, high=11),
        m=lambda n: n // 3,
        w=randint(low=0, high=1000),
        K=randint(low=500, high=501),
        u=uniform(loc=0.0, scale=1.0),
        alpha=uniform(loc=0.25, scale=0.0),
    )
    data = gen.generate(1)[0]
    assert data.weights.shape[1] == 10
    assert data.weights.shape[0] == 3


def test_knapsack_model() -> None:
    data = MultiKnapsackData(
        prices=np.array([344.0, 527.0, 658.0, 519.0, 460.0]),
        capacities=np.array([449.0, 377.0, 380.0]),
        weights=np.array(
            [
                [92.0, 473.0, 871.0, 264.0, 96.0],
                [67.0, 664.0, 21.0, 628.0, 129.0],
                [436.0, 209.0, 309.0, 481.0, 86.0],
            ]
        ),
    )
    model = build_multiknapsack_model_gurobipy(data)
    model.optimize()
    assert model.inner.objVal == -460.0