# Copyright 2010 Hakan Kjellerstrand hakank@bonetmail.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.

"""

  Combinatorial auction in Google CP Solver.

  This is a more general model for the combinatorial example
  in the Numberjack Tutorial, pages 9 and 24 (slides  19/175 and
  51/175).

  The original and more talkative model is here:
  http://www.hakank.org/numberjack/combinatorial_auction.py

  Compare with the following models:
  * MiniZinc: http://hakank.org/minizinc/combinatorial_auction.mzn
  * Gecode: http://hakank.org/gecode/combinatorial_auction.cpp

  This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
  Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/
"""
import sys,string
from collections import *
from constraint_solver import pywrapcp


def main():

    # Create the solver.
    solver = pywrapcp.Solver('Problem')

    #
    # data
    #
    N = 5

    # the items for each bid
    items = [
           [0,1],   # A,B
           [0,2],   # A, C
           [1,3],   # B,D
           [1,2,3], # B,C,D
           [0]      # A
           ]
    # collect the bids for each item
    items_t = defaultdict(list)

    # [items_t.setdefault(j,[]).append(i) for i in range(N) for j in items[i] ]
    # nicer:
    [items_t[j].append(i) for i in range(N) for j in items[i] ]

    bid_amount = [10,20,30,40,14]

    #
    # declare variables
    #
    X = [solver.BoolVar("x%i"%i) for i in range(N)]
    obj = solver.IntVar(0,100,'obj')

    #
    # constraints
    #
    solver.Add(obj == solver.ScalProd(X,bid_amount))
    for item in items_t:
        solver.Add(solver.Sum([X[bid] for bid in items_t[item]]) <= 1)


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

    #
    # solution and search
    #
    solution = solver.Assignment()
    solution.Add(X)
    solution.Add(obj)

    # db: DecisionBuilder
    db = solver.Phase(X,
                 solver.CHOOSE_FIRST_UNBOUND,
                 solver.ASSIGN_MIN_VALUE)

    solver.NewSearch(db,[objective])
    num_solutions = 0
    while solver.NextSolution():
        print "X:", [X[i].Value() for i in range(N)]
        print "obj:", obj.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()

if __name__ == '__main__':
    main()