#!/usr/bin/env python3
# Copyright 2010-2022 Google LLC
# 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.

# [START program]
"""Solve a multiple knapsack problem using a MIP solver."""
# [START import]
from ortools.linear_solver import pywraplp
# [END import]


def main():
    # [START data]
    data = {}
    data["weights"] = [48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36]
    data["values"] = [10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25]
    assert len(data["weights"]) == len(data["values"])
    data["num_items"] = len(data["weights"])
    data["all_items"] = range(data["num_items"])

    data["bin_capacities"] = [100, 100, 100, 100, 100]
    data["num_bins"] = len(data["bin_capacities"])
    data["all_bins"] = range(data["num_bins"])
    # [END data]

    # Create the mip solver with the SCIP backend.
    # [START solver]
    solver = pywraplp.Solver.CreateSolver("SCIP")
    if solver is None:
        print("SCIP solver unavailable.")
        return
    # [END solver]

    # Variables.
    # [START variables]
    # x[i, b] = 1 if item i is packed in bin b.
    x = {}
    for i in data["all_items"]:
        for b in data["all_bins"]:
            x[i, b] = solver.BoolVar(f"x_{i}_{b}")
    # [END variables]

    # Constraints.
    # [START constraints]
    # Each item is assigned to at most one bin.
    for i in data["all_items"]:
        solver.Add(sum(x[i, b] for b in data["all_bins"]) <= 1)

    # The amount packed in each bin cannot exceed its capacity.
    for b in data["all_bins"]:
        solver.Add(
            sum(x[i, b] * data["weights"][i] for i in data["all_items"])
            <= data["bin_capacities"][b]
        )
    # [END constraints]

    # Objective.
    # [START objective]
    # Maximize total value of packed items.
    objective = solver.Objective()
    for i in data["all_items"]:
        for b in data["all_bins"]:
            objective.SetCoefficient(x[i, b], data["values"][i])
    objective.SetMaximization()
    # [END objective]

    # [START solve]
    status = solver.Solve()
    # [END solve]

    # [START print_solution]
    if status == pywraplp.Solver.OPTIMAL:
        print(f"Total packed value: {objective.Value()}")
        total_weight = 0
        for b in data["all_bins"]:
            print(f"Bin {b}")
            bin_weight = 0
            bin_value = 0
            for i in data["all_items"]:
                if x[i, b].solution_value() > 0:
                    print(
                        f"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}"
                    )
                    bin_weight += data["weights"][i]
                    bin_value += data["values"][i]
            print(f"Packed bin weight: {bin_weight}")
            print(f"Packed bin value: {bin_value}\n")
            total_weight += bin_weight
        print(f"Total packed weight: {total_weight}")
    else:
        print("The problem does not have an optimal solution.")
    # [END print_solution]


if __name__ == "__main__":
    main()
# [END program]
