# Copyright 2010-2017 Google
# 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.
from __future__ import print_function
from ortools.sat.python import cp_model

def main():
  # Data.
  cost = [[90, 76, 75, 70, 50, 74],
          [35, 85, 55, 65, 48, 101],
          [125, 95, 90, 105, 59, 120],
          [45, 110, 95, 115, 104, 83],
          [60, 105, 80, 75, 59, 62],
          [45, 65, 110, 95, 47, 31],
          [38, 51, 107, 41, 69, 99],
          [47, 85, 57, 71, 92, 77],
          [39, 63, 97, 49, 118, 56],
          [47, 101, 71, 60, 88, 109],
          [17, 39, 103, 64, 61, 92],
          [101, 45, 83, 59, 92, 27]]

  group1 =  [[0, 0, 1, 1],      # Workers 2, 3
             [0, 1, 0, 1],      # Workers 1, 3
             [0, 1, 1, 0],      # Workers 1, 2
             [1, 1, 0, 0],      # Workers 0, 1
             [1, 0, 1, 0]]      # Workers 0, 2

  group2 =  [[0, 0, 1, 1],      # Workers 6, 7
             [0, 1, 0, 1],      # Workers 5, 7
             [0, 1, 1, 0],      # Workers 5, 6
             [1, 1, 0, 0],      # Workers 4, 5
             [1, 0, 0, 1]]      # Workers 4, 7

  group3 =  [[0, 0, 1, 1],      # Workers 10, 11
             [0, 1, 0, 1],      # Workers 9, 11
             [0, 1, 1, 0],      # Workers 9, 10
             [1, 0, 1, 0],      # Workers 8, 10
             [1, 0, 0, 1]]      # Workers 8, 11


  sizes = [10, 7, 3, 12, 15, 4, 11, 5]
  total_size_max = 15
  num_workers = len(cost)
  num_tasks = len(cost[1])
  all_workers = range(num_workers)
  all_tasks = range(num_tasks)

  # Model.

  model = cp_model.CpModel()
  # Variables
  total_cost = model.NewIntVar(0, 1000, 'total_cost')
  x = [[model.NewBoolVar('x[%i,%i]' % (i, j)) for j in all_tasks]
       for i in all_workers]
  works = [model.NewBoolVar('works[%i]' % i) for i in all_workers]

  # Constraints

  # Link x and workers.
  for i in range(num_workers):
    model.AddMaxEquality(works[i], x[i])

  # Each task is assigned to at least one worker.
  for j in all_tasks:
    model.Add(sum(x[i][j] for i in all_workers) >= 1)

  # Total task size for each worker is at most total_size_max
  for i in all_workers:
    model.Add(sum(sizes[j] * x[i][j] for j in all_tasks) <= total_size_max)

  # Group constraints.
  model.AddAllowedAssignments([works[0], works[1], works[2], works[3]], group1)
  model.AddAllowedAssignments([works[4], works[5], works[6], works[7]], group2)
  model.AddAllowedAssignments([works[8], works[9], works[10], works[11]], group3)

  # Total cost
  model.Add(total_cost ==
            sum(x[i][j] * cost[i][j] for j in all_tasks for i in all_workers))
  model.Minimize(total_cost)

  # Solve and output solution.
  solver = cp_model.CpSolver()
  status = solver.Solve(model)

  if status == cp_model.OPTIMAL:
    print('Total cost = %i' % solver.ObjectiveValue())
    print()
    for i in all_workers:
      for j in all_tasks:
        if solver.Value(x[i][j]) == 1:
          print('Worker ', i, ' assigned to task ', j, '  Cost = ', cost[i][j])

    print()

  print('Statistics')
  print('  - conflicts : %i' % solver.NumConflicts())
  print('  - branches  : %i' % solver.NumBranches())
  print('  - wall time : %f ms' % solver.WallTime())


if __name__ == '__main__':
  main()