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# Copyright 2010 Hakan Kjellerstrand hakank@gmail.com
#
# 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.
"""

  Knapsack problem in Google CP Solver.

  Simple knapsack problem.

  This model was created by Hakan Kjellerstrand (hakank@gmail.com)
  Also see my other Google CP Solver models:
  http://www.hakank.org/google_or_tools/
"""
from __future__ import print_function
from ortools.constraint_solver import pywrapcp


def knapsack(solver, values, weights, n):
  z = solver.IntVar(0, 10000)
  x = [solver.IntVar(0, 1, "x(%i)" % i) for i in range(len(values))]
  solver.Add(z >= 0)
  solver.Add(z == solver.ScalProd(x, values))
  solver.Add(solver.ScalProd(x, weights) <= n)

  return [x, z]


# Create the solver.
solver = pywrapcp.Solver("knapsack_cp")

#
# data
#
print("values:", values)
print("weights:", weights)
print("n:", n)
print()

# declare variables

#
# constraints
#
[x, z] = knapsack(solver, values, weights, n)

# objective
objective = solver.Maximize(z, 1)

#
# solution and search
#
solution = solver.Assignment()
solution.Add(x)
solution.Add(z)

# db: DecisionBuilder
db = solver.Phase(x, solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MAX_VALUE)

solver.NewSearch(db, [objective])
num_solutions = 0
while solver.NextSolution():
  print("x:", [x[i].Value() for i in range(len(values))])
  print("z:", z.Value())
  print()
  num_solutions += 1
solver.EndSearch()

print()
print("num_solutions:", num_solutions)
print("failures:", solver.Failures())
print("branches:", solver.Branches())
print("WallTime:", solver.WallTime())

values = [15, 100, 90, 60, 40, 15, 10, 1, 12, 12, 100]
weights = [2, 20, 20, 30, 40, 30, 60, 10, 21, 12, 2]
n = 102