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# Copyright 2010-2018 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.
"""Code sample to demonstrate how to build a NoOverlap constraint."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from ortools.sat.python import cp_model


def NoOverlapSampleSat():
    """No overlap sample with fixed activities."""
    model = cp_model.CpModel()
    horizon = 21  # 3 weeks.

    # Task 0, duration 2.
    start_0 = model.NewIntVar(0, horizon, 'start_0')
    duration_0 = 2  # Python cp/sat code accepts integer variables or constants.
    end_0 = model.NewIntVar(0, horizon, 'end_0')
    task_0 = model.NewIntervalVar(start_0, duration_0, end_0, 'task_0')
    # Task 1, duration 4.
    start_1 = model.NewIntVar(0, horizon, 'start_1')
    duration_1 = 4  # Python cp/sat code accepts integer variables or constants.
    end_1 = model.NewIntVar(0, horizon, 'end_1')
    task_1 = model.NewIntervalVar(start_1, duration_1, end_1, 'task_1')

    # Task 2, duration 3.
    start_2 = model.NewIntVar(0, horizon, 'start_2')
    duration_2 = 3  # Python cp/sat code accepts integer variables or constants.
    end_2 = model.NewIntVar(0, horizon, 'end_2')
    task_2 = model.NewIntervalVar(start_2, duration_2, end_2, 'task_2')

    # Weekends.
    weekend_0 = model.NewIntervalVar(5, 2, 7, 'weekend_0')
    weekend_1 = model.NewIntervalVar(12, 2, 14, 'weekend_1')
    weekend_2 = model.NewIntervalVar(19, 2, 21, 'weekend_2')

    # No Overlap constraint.
    model.AddNoOverlap(
        [task_0, task_1, task_2, weekend_0, weekend_1, weekend_2])

    # Makespan objective.
    obj = model.NewIntVar(0, horizon, 'makespan')
    model.AddMaxEquality(obj, [end_0, end_1, end_2])
    model.Minimize(obj)

    # Solve model.
    solver = cp_model.CpSolver()
    status = solver.Solve(model)

    if status == cp_model.OPTIMAL:
        # Print out makespan and the start times for all tasks.
        print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
        print('Task 0 starts at %i' % solver.Value(start_0))
        print('Task 1 starts at %i' % solver.Value(start_1))
        print('Task 2 starts at %i' % solver.Value(start_2))
    else:
        print('Solver exited with nonoptimal status: %i' % status)


NoOverlapSampleSat()