update python code, remove __future__ imports, remove six, use absl-py for flags, update examples

2020-11-18 10:50:14 +01:00
parent c1b23ffd92
commit 4ececbe448
99 changed files with 246 additions and 340 deletions
--- a/examples/python/appointments.py
+++ b/examples/python/appointments.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Generates possible daily schedules for workers."""

-from __future__ import print_function
-from __future__ import division

 import argparse
 from ortools.sat.python import cp_model
--- a/examples/python/arc_flow_cutting_stock_sat.py
+++ b/examples/python/arc_flow_cutting_stock_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Cutting stock problem with the objective to minimize wasted space."""

-from __future__ import print_function

 import argparse
 import collections
--- a/examples/python/assignment2_sat.py
+++ b/examples/python/assignment2_sat.py
@@ -11,7 +11,6 @@
 # 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


--- a/examples/python/assignment_with_constraints_sat.py
+++ b/examples/python/assignment_with_constraints_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Solve an assignment problem with combination constraints on workers."""

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/examples/python/balance_group_sat.py
+++ b/examples/python/balance_group_sat.py
@@ -18,8 +18,6 @@ Furthermore, if one color is an a group, at least k items with this color must
 be in that group.
 """

-from __future__ import print_function
-from __future__ import division

 from ortools.sat.python import cp_model

--- a/examples/python/bus_driver_scheduling_flow_sat.py
+++ b/examples/python/bus_driver_scheduling_flow_sat.py
@@ -21,7 +21,6 @@ Constraints:
 - 15 min cleaning time after the last shift
 - 2 min waiting time after each shift for passenger boarding and alighting
 """
-from __future__ import print_function

 import argparse
 import collections
--- a/examples/python/bus_driver_scheduling_sat.py
+++ b/examples/python/bus_driver_scheduling_sat.py
@@ -21,13 +21,23 @@ Constraints:
 - 15 min cleaning time after the last shift
 - 2 min waiting time after each shift for passenger boarding and alighting
 """
-from __future__ import print_function

 import collections
 import math

+from google.protobuf import text_format
+from absl import app
+from absl import flags
 from ortools.sat.python import cp_model

+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('output_proto', '',
+                    'Output file to write the cp_model proto to.')
+flags.DEFINE_string('params', 'num_search_workers:8,log_search_progress:true',
+                    'Sat solver parameters.')
+flags.DEFINE_integer('instance', 1, 'Instance to select (1, 2, 3).', 1, 3)
+
 SAMPLE_SHIFTS_SMALL = [
    #
    # column description:
@@ -1652,22 +1662,39 @@ SAMPLE_SHIFTS_LARGE = [
    [1355, '00:57', '01:07', 1497, 1507, 10]
 ]  # yapf:disable

-
-SAMPLE_SHIFTS = SAMPLE_SHIFTS_MEDIUM
+# pytype: disable=wrong-arg-types


 def bus_driver_scheduling(minimize_drivers, max_num_drivers):
    """Optimize the bus driver scheduling problem.

-    This model has two modes.
+  This model has two modes.

-    If minimize_drivers == True, the objective will be to find the minimal
-    number of drivers, independently of the working times of each drivers.
+  If minimize_drivers == True, the objective will be to find the minimal
+  number of drivers, independently of the working times of each drivers.

-    Otherwise, will will create max_num_drivers non optional drivers, and
-    minimize the sum of working times of these drivers.
-    """
-    num_shifts = len(SAMPLE_SHIFTS)
+  Otherwise, will will create max_num_drivers non optional drivers, and
+  minimize the sum of working times of these drivers.
+
+  Args:
+    minimize_drivers: A Boolean parameter specifying the objective of the
+      problem. If True, it tries to minimize the number of used drivers. If
+      false, it minimizes the sum of working times per workers.
+    max_num_drivers: This number specifies the exact number of non optional
+      drivers to use. This is only used if 'minimize_drivers' is False.
+
+  Returns:
+      The objective value of the model.
+  """
+    shifts = None
+    if FLAGS.instance == 1:
+        shifts = SAMPLE_SHIFTS_SMALL
+    elif FLAGS.instance == 2:
+        shifts = SAMPLE_SHIFTS_MEDIUM
+    elif FLAGS.instance == 3:
+        shifts = SAMPLE_SHIFTS_LARGE
+
+    num_shifts = len(shifts)

    # All durations are in minutes.
    max_driving_time = 540  # 8 hours.
@@ -1680,12 +1707,12 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
    cleanup_time = 15

    # Computed data.
-    total_driving_time = sum(shift[5] for shift in SAMPLE_SHIFTS)
-    min_num_drivers = int(
-        math.ceil(total_driving_time * 1.0 / max_driving_time))
+    total_driving_time = sum(shift[5] for shift in shifts)
+    min_num_drivers = int(math.ceil(total_driving_time * 1.0 /
+                                    max_driving_time))
    num_drivers = 2 * min_num_drivers if minimize_drivers else max_num_drivers
-    min_start_time = min(shift[3] for shift in SAMPLE_SHIFTS)
-    max_end_time = max(shift[4] for shift in SAMPLE_SHIFTS)
+    min_start_time = min(shift[3] for shift in shifts)
+    max_end_time = max(shift[4] for shift in shifts)

    print('Bus driver scheduling')
    print('  num shifts =', num_shifts)
@@ -1760,7 +1787,7 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
            performed[d, s] = model.NewBoolVar('performed_%i_%i' % (d, s))

        for s in range(num_shifts):
-            shift = SAMPLE_SHIFTS[s]
+            shift = shifts[s]
            duration = shift[5]

            # Arc from source to shift.
@@ -1772,10 +1799,9 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
            shared_incoming_literals[s].append(source_lit)
            model.Add(start_times[d] == shift[3] -
                      setup_time).OnlyEnforceIf(source_lit)
-            model.Add(
-                total_driving[d, s] == duration).OnlyEnforceIf(source_lit)
-            model.Add(
-                no_break_driving[d, s] == duration).OnlyEnforceIf(source_lit)
+            model.Add(total_driving[d, s] == duration).OnlyEnforceIf(source_lit)
+            model.Add(no_break_driving[d,
+                                       s] == duration).OnlyEnforceIf(source_lit)
            starting_shifts[d, s] = source_lit

            # Arc from shift to sink
@@ -1787,14 +1813,15 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
            incoming_sink_literals.append(sink_lit)
            model.Add(end_times[d] == shift[4] +
                      cleanup_time).OnlyEnforceIf(sink_lit)
-            model.Add(driving_times[d] == total_driving[d, s]).OnlyEnforceIf(
-                sink_lit)
+            model.Add(
+                driving_times[d] == total_driving[d, s]).OnlyEnforceIf(sink_lit)

            # Node not performed
            #    - set both driving times to 0
            #    - add a looping arc on the node
-            model.Add(total_driving[d, s] == 0).OnlyEnforceIf(
-                performed[d, s].Not())
+            model.Add(total_driving[d,
+                                    s] == 0).OnlyEnforceIf(performed[d,
+                                                                     s].Not())
            model.Add(no_break_driving[d, s] == 0).OnlyEnforceIf(
                performed[d, s].Not())
            incoming_literals[s].append(performed[d, s].Not())
@@ -1811,7 +1838,7 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
                performed[d, s])

            for o in range(num_shifts):
-                other = SAMPLE_SHIFTS[o]
+                other = shifts[o]
                delay = other[3] - shift[4]
                if delay < min_delay_between_shifts:
                    continue
@@ -1826,9 +1853,8 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
                    model.Add(
                        no_break_driving[d, o] == other[5]).OnlyEnforceIf(lit)
                else:
-                    model.Add(
-                        no_break_driving[d, o] == no_break_driving[d, s] +
-                        other[5]).OnlyEnforceIf(lit)
+                    model.Add(no_break_driving[d, o] == no_break_driving[d, s] +
+                              other[5]).OnlyEnforceIf(lit)

                # Add arc
                outgoing_literals[s].append(lit)
@@ -1890,26 +1916,32 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
                                 working_drivers[d + 1].Not())

    # Redundant constraints: sum of driving times = sum of shift driving times
-    model.Add(sum(driving_times) == total_driving_time)
+    model.Add(cp_model.LinearExpr.Sum(driving_times) == total_driving_time)
    if not minimize_drivers:
        model.Add(
-            sum(working_times) == total_driving_time +
+            cp_model.LinearExpr.Sum(working_times) == total_driving_time +
            num_drivers * (setup_time + cleanup_time) +
            cp_model.LinearExpr.ScalProd(delay_literals, delay_weights))

    if minimize_drivers:
        # Minimize the number of working drivers
-        model.Minimize(sum(working_drivers))
+        model.Minimize(cp_model.LinearExpr.Sum(working_drivers))
    else:
        # Minimize the sum of delays between tasks, which in turns minimize the
        # sum of working times as the total driving time is fixed
        model.Minimize(
            cp_model.LinearExpr.ScalProd(delay_literals, delay_weights))

+    if not minimize_drivers and FLAGS.output_proto:
+        print('Writing proto to %s' % FLAGS.output_proto)
+        with open(FLAGS.output_proto, 'w') as text_file:
+            text_file.write(str(model))
+
    # Solve model.
    solver = cp_model.CpSolver()
-    solver.parameters.log_search_progress = True # not minimize_drivers
-    solver.parameters.num_search_workers = 8
+    if FLAGS.params:
+        text_format.Parse(FLAGS.params, solver.parameters)
+
    status = solver.Solve(model)

    if status != cp_model.OPTIMAL and status != cp_model.FEASIBLE:
@@ -1929,7 +1961,7 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):

        first = True
        for s in range(num_shifts):
-            shift = SAMPLE_SHIFTS[s]
+            shift = shifts[s]

            if not solver.BooleanValue(performed[d, s]):
                continue
@@ -1939,18 +1971,22 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
            # no_break_driving which was reinitialized in that case.
            if solver.Value(no_break_driving[d, s]) == shift[5] and not first:
                print('    **break**')
-            print('    shift ', shift[0], ':', shift[1], "-", shift[2])
+            print('    shift ', shift[0], ':', shift[1], '-', shift[2])
            first = False

    return int(solver.ObjectiveValue())


-def optimize_bus_driver_allocation():
+def main(_):
    """Optimize the bus driver allocation in two passes."""
    print('----------- first pass: minimize the number of drivers')
    num_drivers = bus_driver_scheduling(True, -1)
-    print('----------- second pass: minimize the sum of working times')
-    bus_driver_scheduling(False, num_drivers)
+    if num_drivers == -1:
+        print('no solution found, skipping the final step')
+    else:
+        print('----------- second pass: minimize the sum of working times')
+        bus_driver_scheduling(False, num_drivers)


-optimize_bus_driver_allocation()
+if __name__ == '__main__':
+    app.run(main)
--- a/examples/python/chemical_balance_lp.py
+++ b/examples/python/chemical_balance_lp.py
@@ -17,8 +17,6 @@
 # Furthermore, if one color is an a group, at least k items with this color must
 # be in that group.

-from __future__ import print_function
-from __future__ import division

 from ortools.linear_solver import pywraplp

--- a/examples/python/chemical_balance_sat.py
+++ b/examples/python/chemical_balance_sat.py
@@ -17,8 +17,6 @@
 # Furthermore, if one color is an a group, at least k items with this color must
 # be in that group.

-from __future__ import print_function
-from __future__ import division

 from ortools.sat.python import cp_model
 import math
--- a/examples/python/clustering_sat.py
+++ b/examples/python/clustering_sat.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Cluster 40 cities in 4 equal groups to minimize sum of crossed distances."""

-from __future__ import print_function
-from __future__ import division

 from ortools.sat.python import cp_model

--- a/examples/python/cover_rectangle_sat.py
+++ b/examples/python/cover_rectangle_sat.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Fill a 72x37 rectangle by a minimum number of non-overlapping squares."""

-from __future__ import print_function
-from __future__ import division

 from ortools.sat.python import cp_model

--- a/examples/python/flexible_job_shop_sat.py
+++ b/examples/python/flexible_job_shop_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Solves a flexible jobshop problems with the CP-SAT solver."""

-from __future__ import print_function

 import collections
 from ortools.sat.python import cp_model
--- a/examples/python/golomb8.py
+++ b/examples/python/golomb8.py
@@ -20,14 +20,18 @@ between all marks are all different. The objective is to minimize the length
 of the rule.
 """

+from absl import app
+from absl import flags
 from ortools.constraint_solver import pywrapcp

+FLAGS = flags.FLAGS
+
 # We disable the following warning because it is a false positive on constraints
 # like: solver.Add(x == 0)
 # pylint: disable=g-explicit-bool-comparison


-def main():
+def main(_):
    # Create the solver.
    solver = pywrapcp.Solver('golomb ruler')

@@ -40,11 +44,13 @@ def main():
    objective = solver.Minimize(marks[size - 1], 1)

    solver.Add(marks[0] == 0)
-    solver.Add(
-        solver.AllDifferent([
-            marks[j] - marks[i]
-            for i in range(0, size - 1) for j in range(i + 1, size)
-        ]))
+
+    # We expand the creation of the diff array to avoid a pylint warning.
+    diffs = []
+    for i in range(0, size - 1):
+        for j in range(i + 1, size):
+            diffs.append(marks[j] - marks[i])
+    solver.Add(solver.AllDifferent(diffs))

    solver.Add(marks[size - 1] - marks[size - 2] > marks[1] - marks[0])
    for i in range(0, size - 2):
@@ -72,4 +78,4 @@ def main():


 if __name__ == '__main__':
-    main()
+    app.run(main)
--- a/examples/python/hidato_sat.py
+++ b/examples/python/hidato_sat.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Solves the Hidato problem with the CP-SAT solver."""

-from __future__ import print_function
-
 from ortools.sat.python import visualization
 from ortools.sat.python import cp_model

@@ -31,13 +29,16 @@ def build_pairs(rows, cols):
    rows: the number of rows in the grid
    cols: the number of columns in the grid
  """
-    return [
-        (x * cols + y, (x + dx) * cols + (y + dy)) for x in range(rows)
-        for y in range(cols) for dx in (-1, 0, 1)
-        for dy in (-1, 0, 1)
-        if (x + dx >= 0 and x + dx < rows and y + dy >= 0 and y + dy < cols and
-            (dx != 0 or dy != 0))
-    ]
+    result = []
+    for x in range(rows):
+        for y in range(cols):
+            for dx in (-1, 0, 1):
+                for dy in (-1, 0, 1):
+                    if (x + dx >= 0 and x + dx < rows and y + dy >= 0 and
+                            y + dy < cols and (dx != 0 or dy != 0)):
+                        result.append(
+                            (x * cols + y, (x + dx) * cols + (y + dy)))
+    return result


 def print_solution(positions, rows, cols):
@@ -83,8 +84,8 @@ def build_puzzle(problem):
    elif problem == 2:
        puzzle = [[0, 44, 41, 0, 0, 0, 0], [0, 43, 0, 28, 29, 0, 0],
                  [0, 1, 0, 0, 0, 33, 0], [0, 2, 25, 4, 34, 0, 36],
-                  [49, 16, 0, 23, 0, 0, 0], [0, 19, 0, 0, 12, 7,
-                                             0], [0, 0, 0, 14, 0, 0, 0]]
+                  [49, 16, 0, 23, 0, 0, 0], [0, 19, 0, 0, 12, 7, 0],
+                  [0, 0, 0, 14, 0, 0, 0]]

    elif problem == 3:
        # Problems from the book:
@@ -106,10 +107,9 @@ def build_puzzle(problem):
    elif problem == 6:
        # Problem 15 (Intermediate)
        puzzle = [[64, 0, 0, 0, 0, 0, 0, 0], [1, 63, 0, 59, 15, 57, 53, 0],
-                  [0, 4, 0, 14, 0, 0, 0, 0], [3, 0, 11, 0, 20, 19, 0,
-                                              50], [0, 0, 0, 0, 22, 0, 48, 40],
-                  [9, 0, 0, 32, 23, 0, 0, 41], [27, 0, 0, 0, 36, 0, 46,
-                                                0], [28, 30, 0, 35, 0, 0, 0, 0]]
+                  [0, 4, 0, 14, 0, 0, 0, 0], [3, 0, 11, 0, 20, 19, 0, 50],
+                  [0, 0, 0, 0, 22, 0, 48, 40], [9, 0, 0, 32, 23, 0, 0, 41],
+                  [27, 0, 0, 0, 36, 0, 46, 0], [28, 30, 0, 35, 0, 0, 0, 0]]
    return puzzle


@@ -172,8 +172,8 @@ def solve_hidato(puzzle, index):
                output.AddRectangle(x, r - y - 1, 1, 1, color, 'black',
                                    str(i + 1))

-            output.AddTitle('Puzzle %i solved in %f s' % (index,
-                                                          solver.WallTime()))
+            output.AddTitle('Puzzle %i solved in %f s' %
+                            (index, solver.WallTime()))
            output.Display()
        else:
            print_solution(
--- a/examples/python/integer_programming.py
+++ b/examples/python/integer_programming.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Integer programming examples that show how to use the APIs."""

-from __future__ import print_function
-
 from ortools.linear_solver import pywraplp


--- a/examples/python/jobshop_ft06_distance_sat.py
+++ b/examples/python/jobshop_ft06_distance_sat.py
@@ -24,8 +24,6 @@ This variation introduces a minimum distance between all the jobs on each
 machine.
 """

-from __future__ import print_function
-
 import collections

 from ortools.sat.python import cp_model
@@ -66,8 +64,9 @@ def jobshop_ft06_distance():
            end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))
            interval_var = model.NewIntervalVar(start_var, duration, end_var,
                                                'interval_%i_%i' % (i, j))
-            all_tasks[(i, j)] = task_type(
-                start=start_var, end=end_var, interval=interval_var)
+            all_tasks[(i, j)] = task_type(start=start_var,
+                                          end=end_var,
+                                          interval=interval_var)

    # Create disjuctive constraints.
    for i in all_machines:
@@ -102,8 +101,8 @@ def jobshop_ft06_distance():
                # We add the reified precedence to link the literal with the
                # times of the two tasks.
                min_distance = distance_between_jobs(j1, j2)
-                model.Add(job_starts[j2] >=
-                          job_ends[j1] + min_distance).OnlyEnforceIf(lit)
+                model.Add(job_starts[j2] >= job_ends[j1] +
+                          min_distance).OnlyEnforceIf(lit)

        model.AddCircuit(arcs)

--- a/examples/python/jobshop_ft06_sat.py
+++ b/examples/python/jobshop_ft06_sat.py
@@ -20,7 +20,6 @@ machine is task_type dependent.
 The objective is to minimize the maximum completion time of all
 jobs. This is called the makespan.
 """
-from __future__ import print_function

 import collections

@@ -58,8 +57,9 @@ def jobshop_ft06():
            end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))
            interval_var = model.NewIntervalVar(start_var, duration, end_var,
                                                'interval_%i_%i' % (i, j))
-            all_tasks[(i, j)] = task_type(
-                start=start_var, end=end_var, interval=interval_var)
+            all_tasks[(i, j)] = task_type(start=start_var,
+                                          end=end_var,
+                                          interval=interval_var)

    # Create disjuctive constraints.
    machine_to_jobs = {}
--- a/examples/python/jobshop_with_maintenance_sat.py
+++ b/examples/python/jobshop_with_maintenance_sat.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Jobshop with maintenance tasks using the CP-SAT solver."""

-from __future__ import absolute_import
-from __future__ import print_function

 import collections

--- a/examples/python/linear_assignment_api.py
+++ b/examples/python/linear_assignment_api.py
@@ -16,14 +16,13 @@
   http://www.ee.oulu.fi/~mpa/matreng/eem1_2-1.htm with kCost[0][1]
   modified so the optimum solution is unique.
 """
-from __future__ import print_function

+from absl import app
 from ortools.graph import pywrapgraph


 def RunAssignmentOn4x4Matrix():
-    """Test linear sum assignment on a 4x4 matrix.
-  """
+    """Test linear sum assignment on a 4x4 matrix."""
    num_sources = 4
    num_targets = 4
    cost = [[90, 76, 75, 80], [35, 85, 55, 65], [125, 95, 90, 105],
@@ -49,9 +48,9 @@ def RunAssignmentOn4x4Matrix():
            'Some input costs are too large and may cause an integer overflow.')


-def main():
+def main(_):
    RunAssignmentOn4x4Matrix()


 if __name__ == '__main__':
-    main()
+    app.run(main)
--- a/examples/python/linear_programming.py
+++ b/examples/python/linear_programming.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """Linear programming examples that show how to use the APIs."""

-from __future__ import print_function
-
 from ortools.linear_solver import pywraplp


--- a/examples/python/magic_sequence_distribute.py
+++ b/examples/python/magic_sequence_distribute.py
@@ -16,21 +16,31 @@ This models aims at building a sequence of numbers such that the number of
 occurrences of i in this sequence is equal to the value of the ith number.
 It uses an aggregated formulation of the count expression called
 distribute().
-"""
-from __future__ import print_function

+Usage: python magic_sequence_distribute.py NUMBER
+"""
+
+from absl import app
+from absl import flags
 from ortools.constraint_solver import pywrapcp

+FLAGS = flags.FLAGS

-def main():
+
+def main(argv):
    # Create the solver.
    solver = pywrapcp.Solver('magic sequence')

-    size = 100
+    # Create an array of IntVars to hold the answers.
+    size = int(argv[1]) if len(argv) > 1 else 100
    all_values = list(range(0, size))
    all_vars = [solver.IntVar(0, size, 'vars_%d' % i) for i in all_values]

+    # The number of variables equal to j shall be the value of all_vars[j].
    solver.Add(solver.Distribute(all_vars, all_values, all_vars))
+
+    # The sum of all the values shall be equal to the size.
+    # (This constraint is redundant, but speeds up the search.)
    solver.Add(solver.Sum(all_vars) == size)

    solver.NewSearch(
@@ -42,4 +52,4 @@ def main():


 if __name__ == '__main__':
-    main()
+    app.run(main)
--- a/examples/python/nqueens_sat.py
+++ b/examples/python/nqueens_sat.py
@@ -11,7 +11,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """OR-tools solution to the N-queens problem."""
-from __future__ import print_function
 import time
 import sys
 from ortools.sat.python import cp_model
--- a/examples/python/prize_collecting_tsp_sat.py
+++ b/examples/python/prize_collecting_tsp_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Simple travelling salesman problem between cities."""

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/examples/python/pyflow_example.py
+++ b/examples/python/pyflow_example.py
@@ -12,7 +12,7 @@
 # limitations under the License.
 """MaxFlow and MinCostFlow examples."""

-from __future__ import print_function
+from absl import app
 from ortools.graph import pywrapgraph


@@ -29,9 +29,9 @@ def MaxFlow():
    if max_flow.Solve(0, 5) == max_flow.OPTIMAL:
        print('Total flow', max_flow.OptimalFlow(), '/', expected_total_flow)
        for i in range(max_flow.NumArcs()):
-            print(('From source %d to target %d: %d / %d' %
-                   (max_flow.Tail(i), max_flow.Head(i), max_flow.Flow(i),
-                    max_flow.Capacity(i))))
+            print('From source %d to target %d: %d / %d' %
+                  (max_flow.Tail(i), max_flow.Head(i), max_flow.Flow(i),
+                   max_flow.Capacity(i)))
        print('Source side min-cut:', max_flow.GetSourceSideMinCut())
        print('Sink side min-cut:', max_flow.GetSinkSideMinCut())
    else:
@@ -53,8 +53,9 @@ def MinCostFlow():
    min_cost_flow = pywrapgraph.SimpleMinCostFlow()
    for source in range(0, num_sources):
        for target in range(0, num_targets):
-            min_cost_flow.AddArcWithCapacityAndUnitCost(
-                source, num_sources + target, 1, costs[source][target])
+            min_cost_flow.AddArcWithCapacityAndUnitCost(source,
+                                                        num_sources + target, 1,
+                                                        costs[source][target])
    for node in range(0, num_sources):
        min_cost_flow.SetNodeSupply(node, 1)
        min_cost_flow.SetNodeSupply(num_sources + node, -1)
@@ -64,17 +65,16 @@ def MinCostFlow():
        for i in range(0, min_cost_flow.NumArcs()):
            if min_cost_flow.Flow(i) > 0:
                print('From source %d to target %d: cost %d' %
-                      (min_cost_flow.Tail(i),
-                       min_cost_flow.Head(i) - num_sources,
-                       min_cost_flow.UnitCost(i)))
+                      (min_cost_flow.Tail(i), min_cost_flow.Head(i) -
+                       num_sources, min_cost_flow.UnitCost(i)))
    else:
        print('There was an issue with the min cost flow input.')


-def main():
+def main(_):
    MaxFlow()
    MinCostFlow()


 if __name__ == '__main__':
-    main()
+    app.run(main)
--- a/examples/python/qubo_sat.py
+++ b/examples/python/qubo_sat.py
@@ -1,6 +1,5 @@
 """Solves a Qubo program using the CP-SAT solver."""

-from __future__ import print_function

 import time

--- a/examples/python/rcpsp_sat.py
+++ b/examples/python/rcpsp_sat.py
@@ -12,27 +12,24 @@
 # limitations under the License.
 """Sat based solver for the RCPSP problems (see rcpsp.proto)."""

-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
 import collections
 import time

 from google.protobuf import text_format
+from absl import app
+from absl import flags
 from ortools.data import pywraprcpsp
 from ortools.sat.python import cp_model

-PARSER = argparse.ArgumentParser()
-PARSER.add_argument(
-    '--input', default="", help='Input file to parse and solve.')
-PARSER.add_argument(
-    '--output_proto',
-    default="",
-    help='Output file to write the cp_model'
-    'proto to.')
-PARSER.add_argument('--params', default="", help='Sat solver parameters.')
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('input', '', 'Input file to parse and solve.')
+flags.DEFINE_string('output_proto', '',
+                    'Output file to write the cp_model proto to.')
+flags.DEFINE_string('params', '', 'Sat solver parameters.')
+flags.DEFINE_bool('use_interval_makespan', True,
+                  'Whether we encode the makespan using an interval or not.')
+flags.DEFINE_integer('horizon', -1, 'Force horizon.')


 class SolutionPrinter(cp_model.CpSolverSolutionCallback):
@@ -52,7 +49,7 @@ class SolutionPrinter(cp_model.CpSolverSolutionCallback):
        self.__solution_count += 1


-def solve_rcpsp(problem, proto_file, params):
+def SolveRcpsp(problem, proto_file, params):
    """Parse and solve a given RCPSP problem in proto format."""

    # Determine problem type.
@@ -80,6 +77,8 @@ def solve_rcpsp(problem, proto_file, params):
    all_resources = range(num_resources)

    horizon = problem.deadline if problem.deadline != -1 else problem.horizon
+    if FLAGS.horizon > 0:
+        horizon = FLAGS.horizon
    if horizon == -1:  # Naive computation.
        horizon = sum(max(r.duration for r in t.recipes) for t in problem.tasks)
        if problem.is_rcpsp_max:
@@ -189,6 +188,9 @@ def solve_rcpsp(problem, proto_file, params):

    # Create makespan variable
    makespan = model.NewIntVar(0, horizon, 'makespan')
+    interval_makespan = model.NewIntervalVar(
+        makespan, model.NewIntVar(1, horizon, 'interval_makespan_size'),
+        model.NewConstant(horizon + 1), 'interval_makespan')

    # Add precedences.
    if problem.is_rcpsp_max:
@@ -217,6 +219,8 @@ def solve_rcpsp(problem, proto_file, params):
        for t in all_active_tasks:
            for n in problem.tasks[t].successors:
                if n == num_tasks - 1:
+                    # TODO(user): I guess these are still useful, but we might want to
+                    #    experiment with removing them.
                    model.Add(task_ends[t] <= makespan)
                else:
                    model.Add(task_ends[t] <= task_starts[n])
@@ -241,13 +245,19 @@ def solve_rcpsp(problem, proto_file, params):
            max_cost += c * resource.unit_cost
        elif resource.renewable:
            if intervals_per_resource[r]:
-                model.AddCumulative(intervals_per_resource[r],
-                                    demands_per_resource[r], c)
+                if FLAGS.use_interval_makespan:
+                    model.AddCumulative(
+                        intervals_per_resource[r] + [interval_makespan],
+                        demands_per_resource[r] + [c], c)
+                else:
+                    model.AddCumulative(intervals_per_resource[r],
+                                        demands_per_resource[r], c)
        elif presences_per_resource[r]:  # Non empty non renewable resource.
            if problem.is_consumer_producer:
-                model.AddReservoirConstraint(
-                    starts_per_resource[r], demands_per_resource[r],
-                    resource.min_capacity, resource.max_capacity)
+                model.AddReservoirConstraint(starts_per_resource[r],
+                                             demands_per_resource[r],
+                                             resource.min_capacity,
+                                             resource.max_capacity)
            else:
                model.Add(
                    sum(presences_per_resource[r][i] *
@@ -257,8 +267,9 @@ def solve_rcpsp(problem, proto_file, params):
    # Objective.
    if problem.is_resource_investment:
        objective = model.NewIntVar(0, max_cost, 'capacity_costs')
-        model.Add(objective == sum(problem.resources[i].unit_cost * capacities[
-            i] for i in range(len(capacities))))
+        model.Add(objective == sum(problem.resources[i].unit_cost *
+                                   capacities[i]
+                                   for i in range(len(capacities))))
    else:
        objective = makespan

@@ -272,17 +283,17 @@ def solve_rcpsp(problem, proto_file, params):
    # Solve model.
    solver = cp_model.CpSolver()
    if params:
-        text_format.Merge(params, solver.parameters)
+        text_format.Parse(params, solver.parameters)
    solution_printer = SolutionPrinter()
    solver.SolveWithSolutionCallback(model, solution_printer)
    print(solver.ResponseStats())


-def main(args):
+def main(_):
    rcpsp_parser = pywraprcpsp.RcpspParser()
-    rcpsp_parser.ParseFile(args.input)
-    solve_rcpsp(rcpsp_parser.Problem(), args.output_proto, args.params)
+    rcpsp_parser.ParseFile(FLAGS.input)
+    SolveRcpsp(rcpsp_parser.Problem(), FLAGS.output_proto, FLAGS.params)


 if __name__ == '__main__':
-    main(PARSER.parse_args())
+    app.run(main)
--- a/examples/python/reallocate_sat.py
+++ b/examples/python/reallocate_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Reallocate production to smooth it over years."""

-from __future__ import print_function

 import collections

--- a/examples/python/shift_scheduling_sat.py
+++ b/examples/python/shift_scheduling_sat.py
@@ -12,21 +12,17 @@
 # limitations under the License.
 """Creates a shift scheduling problem and solves it."""

-from __future__ import print_function
-
-import argparse
-
 from ortools.sat.python import cp_model

 from google.protobuf import text_format
+from absl import app
+from absl import flags

-PARSER = argparse.ArgumentParser()
-PARSER.add_argument(
-    '--output_proto',
-    default="",
-    help='Output file to write the cp_model'
-    'proto to.')
-PARSER.add_argument('--params', default="", help='Sat solver parameters.')
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('output_proto', '',
+                    'Output file to write the cp_model proto to.')
+flags.DEFINE_string('params', '', 'Sat solver parameters.')


 def negated_bounded_span(works, start, length):
@@ -92,13 +88,13 @@ def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,

    # Forbid sequences that are too short.
    for length in range(1, hard_min):
-        for start in range(len(works) - length + 1):
+        for start in range(len(works) - length - 1):
            model.AddBoolOr(negated_bounded_span(works, start, length))

    # Penalize sequences that are below the soft limit.
    if min_cost > 0:
        for length in range(hard_min, soft_min):
-            for start in range(len(works) - length + 1):
+            for start in range(len(works) - length - 1):
                span = negated_bounded_span(works, start, length)
                name = ': under_span(start=%i, length=%i)' % (start, length)
                lit = model.NewBoolVar(prefix + name)
@@ -112,7 +108,7 @@ def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,
    # Penalize sequences that are above the soft limit.
    if max_cost > 0:
        for length in range(soft_max + 1, hard_max + 1):
-            for start in range(len(works) - length + 1):
+            for start in range(len(works) - length - 1):
                span = negated_bounded_span(works, start, length)
                name = ': over_span(start=%i, length=%i)' % (start, length)
                lit = model.NewBoolVar(prefix + name)
@@ -123,7 +119,7 @@ def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,
                cost_coefficients.append(max_cost * (length - soft_max))

    # Just forbid any sequence of true variables with length hard_max + 1
-    for start in range(len(works) - hard_max):
+    for start in range(len(works) - hard_max - 1):
        model.AddBoolOr(
            [works[i].Not() for i in range(start, start + hard_max + 1)])
    return cost_literals, cost_coefficients
@@ -221,7 +217,7 @@ def solve_shift_scheduling(params, output_proto):
        (3, 0, 5, -2),
        # Employee 5 wants a night shift on the second Thursday.
        (5, 3, 10, -2),
-        # Employee 2 does not want a night shift on the first Friday.
+        # Employee 2 does not want a night shift on the third Friday.
        (2, 3, 4, 4)
    ]

@@ -308,8 +304,8 @@ def solve_shift_scheduling(params, output_proto):
            works = [work[e, shift, d] for d in range(num_days)]
            variables, coeffs = add_soft_sequence_constraint(
                model, works, hard_min, soft_min, min_cost, soft_max, hard_max,
-                max_cost, 'shift_constraint(employee %i, shift %i)' % (e,
-                                                                       shift))
+                max_cost,
+                'shift_constraint(employee %i, shift %i)' % (e, shift))
            obj_bool_vars.extend(variables)
            obj_bool_coeffs.extend(coeffs)

@@ -332,8 +328,8 @@ def solve_shift_scheduling(params, output_proto):
        for e in range(num_employees):
            for d in range(num_days - 1):
                transition = [
-                    work[e, previous_shift, d].Not(),
-                    work[e, next_shift, d + 1].Not()
+                    work[e, previous_shift, d].Not(), work[e, next_shift,
+                                                           d + 1].Not()
                ]
                if cost == 0:
                    model.AddBoolOr(transition)
@@ -367,9 +363,9 @@ def solve_shift_scheduling(params, output_proto):
    # Objective
    model.Minimize(
        sum(obj_bool_vars[i] * obj_bool_coeffs[i]
-            for i in range(len(obj_bool_vars)))
-        + sum(obj_int_vars[i] * obj_int_coeffs[i]
-              for i in range(len(obj_int_vars))))
+            for i in range(len(obj_bool_vars))) +
+        sum(obj_int_vars[i] * obj_int_coeffs[i]
+            for i in range(len(obj_int_vars))))

    if output_proto:
        print('Writing proto to %s' % output_proto)
@@ -378,9 +374,8 @@ def solve_shift_scheduling(params, output_proto):

    # Solve the model.
    solver = cp_model.CpSolver()
-    solver.parameters.num_search_workers = 8
    if params:
-        text_format.Merge(params, solver.parameters)
+        text_format.Parse(params, solver.parameters)
    solution_printer = cp_model.ObjectiveSolutionPrinter()
    status = solver.SolveWithSolutionCallback(model, solution_printer)

@@ -414,13 +409,16 @@ def solve_shift_scheduling(params, output_proto):
                      (var.Name(), solver.Value(var), obj_int_coeffs[i]))

    print()
-    print(solver.ResponseStats())
+    print('Statistics')
+    print('  - status          : %s' % solver.StatusName(status))
+    print('  - conflicts       : %i' % solver.NumConflicts())
+    print('  - branches        : %i' % solver.NumBranches())
+    print('  - wall time       : %f s' % solver.WallTime())


-def main(args):
-    """Main."""
-    solve_shift_scheduling(args.params, args.output_proto)
+def main(_):
+    solve_shift_scheduling(FLAGS.params, FLAGS.output_proto)


 if __name__ == '__main__':
-    main(PARSER.parse_args())
+    app.run(main)
--- a/examples/python/single_machine_scheduling_with_setup_release_due_dates_sat.py
+++ b/examples/python/single_machine_scheduling_with_setup_release_due_dates_sat.py
@@ -11,9 +11,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Single machine jobshop with setup times, release dates and due dates."""
-from __future__ import print_function
-from __future__ import absolute_import
-from __future__ import division

 import argparse

--- a/examples/python/steel_mill_slab_sat.py
+++ b/examples/python/steel_mill_slab_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Solves the Stell Mill Slab problem with 4 different techniques."""

-from __future__ import print_function

 import argparse
 import collections
--- a/examples/python/stigler_diet.py
+++ b/examples/python/stigler_diet.py
@@ -14,8 +14,6 @@
 # limitations under the License.
 """Stigler diet example"""

-from __future__ import print_function
-from six.moves import xrange
 from ortools.linear_solver import pywraplp


--- a/examples/python/sudoku_sat.py
+++ b/examples/python/sudoku_sat.py
@@ -12,8 +12,6 @@
 # limitations under the License.
 """This model implements a sudoku solver."""

-from __future__ import print_function
-
 from ortools.sat.python import cp_model


@@ -28,10 +26,10 @@ def solve_sudoku():
    cell = list(range(0, cell_size))

    initial_grid = [[0, 6, 0, 0, 5, 0, 0, 2, 0], [0, 0, 0, 3, 0, 0, 0, 9, 0],
-                    [7, 0, 0, 6, 0, 0, 0, 1, 0], [0, 0, 6, 0, 3, 0, 4, 0, 0], [
-                        0, 0, 4, 0, 7, 0, 1, 0, 0
-                    ], [0, 0, 5, 0, 9, 0, 8, 0, 0], [0, 4, 0, 0, 0, 1, 0, 0, 6],
-                    [0, 3, 0, 0, 0, 8, 0, 0, 0], [0, 2, 0, 0, 4, 0, 0, 5, 0]]
+                    [7, 0, 0, 6, 0, 0, 0, 1, 0], [0, 0, 6, 0, 3, 0, 4, 0, 0],
+                    [0, 0, 4, 0, 7, 0, 1, 0, 0], [0, 0, 5, 0, 9, 0, 8, 0, 0],
+                    [0, 4, 0, 0, 0, 1, 0, 0, 6], [0, 3, 0, 0, 0, 8, 0, 0, 0],
+                    [0, 2, 0, 0, 4, 0, 0, 5, 0]]

    grid = {}
    for i in line:
@@ -68,14 +66,7 @@ def solve_sudoku():
    status = solver.Solve(model)
    if status == cp_model.OPTIMAL:
        for i in line:
-            output = ''
-            for j in line:
-                output += str(int(solver.Value(grid[(i, j)]))) + ' '
-                if j == 2 or j == 5:
-                    output += '| '
-            print(output)
-            if i == 2 or i == 5:
-                print('------|-------|-------')
+            print([int(solver.Value(grid[(i, j)])) for j in line])


 solve_sudoku()
--- a/examples/python/task_allocation_sat.py
+++ b/examples/python/task_allocation_sat.py
@@ -15,7 +15,6 @@ see
 http://yetanothermathprogrammingconsultant.blogspot.com/2018/09/minizinc-cpsat-vs-mip.html
 """

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/examples/python/tasks_and_workers_assignment_sat.py
+++ b/examples/python/tasks_and_workers_assignment_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Tasks and workers to group assignment to average sum(cost) / #workers"""

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/examples/python/transit_time.py
+++ b/examples/python/transit_time.py
@@ -21,8 +21,6 @@
   here we use: 114m x 80m city block
 """

-from __future__ import print_function
-from six.moves import xrange
 from ortools.constraint_solver import pywrapcp
 from ortools.constraint_solver import routing_enums_pb2

@@ -173,9 +171,9 @@ class CreateTimeEvaluator(object):
        """Initializes the total time matrix."""
        self._total_time = {}
        # precompute total time to have time callback in O(1)
-        for from_node in xrange(data.num_locations):
+        for from_node in range(data.num_locations):
            self._total_time[from_node] = {}
-            for to_node in xrange(data.num_locations):
+            for to_node in range(data.num_locations):
                if from_node == to_node:
                    self._total_time[from_node][to_node] = 0
                else:
--- a/examples/python/tsp_sat.py
+++ b/examples/python/tsp_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Simple travelling salesman problem between cities."""

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/examples/python/vendor_scheduling_sat.py
+++ b/examples/python/vendor_scheduling_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Solves a simple shift scheduling problem."""

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

 from ortools.sat.python import cp_model

--- a/examples/python/wedding_optimal_chart_sat.py
+++ b/examples/python/wedding_optimal_chart_sat.py
@@ -36,7 +36,6 @@ Adapted from
 https://github.com/google/or-tools/blob/master/examples/csharp/wedding_optimal_chart.cs
 """

-from __future__ import print_function
 import time
 from ortools.sat.python import cp_model

--- a/examples/python/worker_schedule_sat.py
+++ b/examples/python/worker_schedule_sat.py
@@ -10,7 +10,6 @@
 # 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

--- a/examples/python/zebra_sat.py
+++ b/examples/python/zebra_sat.py
@@ -31,7 +31,6 @@ The Norwegian lives next to the blue house.

 Who owns a zebra and who drinks water?
 """
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/algorithms/samples/knapsack.py
+++ b/ortools/algorithms/samples/knapsack.py
@@ -13,7 +13,6 @@
 """A simple knapsack problem."""
 # [START program]
 # [START import]
-from __future__ import print_function
 from ortools.algorithms import pywrapknapsack_solver
 # [END import]

--- a/ortools/algorithms/samples/simple_knapsack_program.py
+++ b/ortools/algorithms/samples/simple_knapsack_program.py
@@ -13,7 +13,6 @@
 # [START program]
 """A simple knapsack problem."""
 # [START import]
-from __future__ import print_function
 from ortools.algorithms import pywrapknapsack_solver
 # [END import]

--- a/ortools/constraint_solver/samples/cvrp.py
+++ b/ortools/constraint_solver/samples/cvrp.py
@@ -23,10 +23,8 @@
   Distances are in meters.
 """

-from __future__ import print_function

 from functools import partial
-from six.moves import xrange

 from ortools.constraint_solver import pywrapcp
 from ortools.constraint_solver import routing_enums_pb2
@@ -84,9 +82,9 @@ def create_distance_evaluator(data):
    """Creates callback to return distance between points."""
    _distances = {}
    # precompute distance between location to have distance callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _distances[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _distances[from_node][to_node] = 0
            else:
@@ -132,7 +130,7 @@ def print_solution(data, routing, manager, assignment):  # pylint:disable=too-ma
    total_distance = 0
    total_load = 0
    capacity_dimension = routing.GetDimensionOrDie('Capacity')
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
        distance = 0
--- a/ortools/constraint_solver/samples/cvrp_reload.py
+++ b/ortools/constraint_solver/samples/cvrp_reload.py
@@ -23,10 +23,8 @@
   Distances are in meters.
 """

-from __future__ import print_function

 from functools import partial
-from six.moves import xrange

 from ortools.constraint_solver import pywrapcp
 from ortools.constraint_solver import routing_enums_pb2
@@ -122,9 +120,9 @@ def create_distance_evaluator(data):
    """Creates callback to return distance between points."""
    _distances = {}
    # precompute distance between location to have distance callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _distances[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _distances[from_node][to_node] = 0
            else:
@@ -204,9 +202,9 @@ def create_time_evaluator(data):

    _total_time = {}
    # precompute total time to have time callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _total_time[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _total_time[from_node][to_node] = 0
            else:
@@ -243,7 +241,7 @@ def add_time_window_constraints(routing, manager, data, time_evaluator):
        routing.AddToAssignment(time_dimension.SlackVar(index))
    # Add time window constraints for each vehicle start node
    # and 'copy' the slack var in the solution object (aka Assignment) to print it
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
                                                data['time_windows'][0][1])
@@ -264,13 +262,13 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
    capacity_dimension = routing.GetDimensionOrDie('Capacity')
    time_dimension = routing.GetDimensionOrDie('Time')
    dropped = []
-    for order in xrange(0, routing.nodes()):
+    for order in range(0, routing.nodes()):
        index = manager.NodeToIndex(order)
        if assignment.Value(routing.NextVar(index)) == index:
            dropped.append(order)
    print('dropped orders: {}'.format(dropped))

-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
        distance = 0
--- a/ortools/constraint_solver/samples/cvrptw.py
+++ b/ortools/constraint_solver/samples/cvrptw.py
@@ -23,10 +23,8 @@
   Distances are in meters and time in minutes.
 """

-from __future__ import print_function

 from functools import partial
-from six.moves import xrange

 from ortools.constraint_solver import pywrapcp
 from ortools.constraint_solver import routing_enums_pb2
@@ -96,9 +94,9 @@ def create_distance_evaluator(data):
    """Creates callback to return distance between points."""
    _distances = {}
    # precompute distance between location to have distance callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _distances[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _distances[from_node][to_node] = 0
            else:
@@ -153,9 +151,9 @@ def create_time_evaluator(data):

    _total_time = {}
    # precompute total time to have time callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _total_time[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _total_time[from_node][to_node] = 0
            else:
@@ -192,7 +190,7 @@ def add_time_window_constraints(routing, manager, data, time_evaluator_index):
        routing.AddToAssignment(time_dimension.SlackVar(index))
    # Add time window constraints for each vehicle start node
    # and 'copy' the slack var in the solution object (aka Assignment) to print it
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
                                                data['time_windows'][0][1])
@@ -212,7 +210,7 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
    total_time = 0
    capacity_dimension = routing.GetDimensionOrDie('Capacity')
    time_dimension = routing.GetDimensionOrDie('Time')
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
        distance = 0
--- a/ortools/constraint_solver/samples/cvrptw_break.py
+++ b/ortools/constraint_solver/samples/cvrptw_break.py
@@ -23,10 +23,8 @@
   Distances are in meters and time in minutes.
 """

-from __future__ import print_function

 from functools import partial
-from six.moves import xrange

 from ortools.constraint_solver import pywrapcp
 from ortools.constraint_solver import routing_enums_pb2
@@ -97,9 +95,9 @@ def create_distance_evaluator(data):
    """Creates callback to return distance between points."""
    _distances = {}
    # precompute distance between location to have distance callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _distances[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _distances[from_node][to_node] = 0
            else:
@@ -154,9 +152,9 @@ def create_time_evaluator(data):

    _total_time = {}
    # precompute total time to have time callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _total_time[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _total_time[from_node][to_node] = 0
            else:
@@ -193,7 +191,7 @@ def add_time_window_constraints(routing, manager, data, time_evaluator_index):
        routing.AddToAssignment(time_dimension.SlackVar(index))
    # Add time window constraints for each vehicle start node
    # and 'copy' the slack var in the solution object (aka Assignment) to print it
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
                                                data['time_windows'][0][1])
@@ -211,7 +209,7 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma

    print('Breaks:')
    intervals = assignment.IntervalVarContainer()
-    for i in xrange(intervals.Size()):
+    for i in range(intervals.Size()):
        brk = intervals.Element(i)
        if brk.PerformedValue() == 1:
            print('{}: Start({}) Duration({})'.format(
@@ -226,7 +224,7 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
    total_time = 0
    capacity_dimension = routing.GetDimensionOrDie('Capacity')
    time_dimension = routing.GetDimensionOrDie('Time')
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
        distance = 0
@@ -298,7 +296,7 @@ def main():
    # Add breaks
    time_dimension = routing.GetDimensionOrDie("Time")
    node_visit_transit = {}
-    for n in xrange(routing.Size()):
+    for n in range(routing.Size()):
        if n >= data['num_locations']:
            node_visit_transit[n] = 0
        else:
@@ -306,7 +304,7 @@ def main():
                data['demands'][n] * data['time_per_demand_unit'])

    break_intervals = {}
-    #for v in xrange(data['num_vehicles']):
+    #for v in range(data['num_vehicles']):
    for v in [0]:
        vehicle_break = data['breaks'][v]
        break_intervals[v] = [
--- a/ortools/constraint_solver/samples/simple_cp_program.py
+++ b/ortools/constraint_solver/samples/simple_cp_program.py
@@ -14,7 +14,6 @@
 """Simple Constraint optimization example."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import pywrapcp
 # [END import]

--- a/ortools/constraint_solver/samples/simple_routing_program.py
+++ b/ortools/constraint_solver/samples/simple_routing_program.py
@@ -14,7 +14,6 @@
 """Vehicle Routing example."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/tsp.py
+++ b/ortools/constraint_solver/samples/tsp.py
@@ -18,7 +18,6 @@ http://en.wikipedia.org/wiki/Travelling_salesman_problem.
 """

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/tsp_circuit_board.py
+++ b/ortools/constraint_solver/samples/tsp_circuit_board.py
@@ -14,7 +14,6 @@
 """Simple travelling salesman problem on a circuit board."""

 # [START import]
-from __future__ import print_function
 import math
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
--- a/ortools/constraint_solver/samples/tsp_cities.py
+++ b/ortools/constraint_solver/samples/tsp_cities.py
@@ -14,7 +14,6 @@
 """Simple travelling salesman problem between cities."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp

--- a/ortools/constraint_solver/samples/tsp_distance_matrix.py
+++ b/ortools/constraint_solver/samples/tsp_distance_matrix.py
@@ -14,7 +14,6 @@
 """Simple Travelling Salesman Problem."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp.py
+++ b/ortools/constraint_solver/samples/vrp.py
@@ -14,7 +14,6 @@
 """Simple Vehicles Routing Problem."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_capacity.py
+++ b/ortools/constraint_solver/samples/vrp_capacity.py
@@ -14,7 +14,6 @@
 """Capacited Vehicles Routing Problem (CVRP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_drop_nodes.py
+++ b/ortools/constraint_solver/samples/vrp_drop_nodes.py
@@ -14,7 +14,6 @@
 """Capacited Vehicles Routing Problem (CVRP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_global_span.py
+++ b/ortools/constraint_solver/samples/vrp_global_span.py
@@ -14,7 +14,6 @@
 """Vehicles Routing Problem (VRP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_initial_routes.py
+++ b/ortools/constraint_solver/samples/vrp_initial_routes.py
@@ -14,7 +14,6 @@
 """Vehicles Routing Problem (VRP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import pywrapcp

 # [END import]
--- a/ortools/constraint_solver/samples/vrp_pickup_delivery.py
+++ b/ortools/constraint_solver/samples/vrp_pickup_delivery.py
@@ -14,7 +14,6 @@
 """Simple Pickup Delivery Problem (PDP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_pickup_delivery_fifo.py
+++ b/ortools/constraint_solver/samples/vrp_pickup_delivery_fifo.py
@@ -14,7 +14,6 @@
 """Simple Pickup Delivery Problem (PDP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_pickup_delivery_lifo.py
+++ b/ortools/constraint_solver/samples/vrp_pickup_delivery_lifo.py
@@ -14,7 +14,6 @@
 """Simple Pickup Delivery Problem (PDP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_resources.py
+++ b/ortools/constraint_solver/samples/vrp_resources.py
@@ -14,7 +14,6 @@
 """Vehicles Routing Problem (VRP) with Resource Constraints."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_starts_ends.py
+++ b/ortools/constraint_solver/samples/vrp_starts_ends.py
@@ -14,7 +14,6 @@
 """Simple Vehicles Routing Problem."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_time_windows.py
+++ b/ortools/constraint_solver/samples/vrp_time_windows.py
@@ -14,7 +14,6 @@
 """Vehicles Routing Problem (VRP) with Time Windows."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrp_with_time_limit.py
+++ b/ortools/constraint_solver/samples/vrp_with_time_limit.py
@@ -14,7 +14,6 @@
 """Vehicles Routing Problem (VRP)."""

 # [START import]
-from __future__ import print_function
 from ortools.constraint_solver import routing_enums_pb2
 from ortools.constraint_solver import pywrapcp
 # [END import]
--- a/ortools/constraint_solver/samples/vrpgs.py
+++ b/ortools/constraint_solver/samples/vrpgs.py
@@ -23,10 +23,8 @@
   Distances are in meters.
 """

-from __future__ import print_function

 from functools import partial
-from six.moves import xrange

 from ortools.constraint_solver import pywrapcp
 from ortools.constraint_solver import routing_enums_pb2
@@ -73,9 +71,9 @@ def create_distance_evaluator(data):
    """Creates callback to return distance between points."""
    _distances = {}
    # precompute distance between location to have distance callback in O(1)
-    for from_node in xrange(data['num_locations']):
+    for from_node in range(data['num_locations']):
        _distances[from_node] = {}
-        for to_node in xrange(data['num_locations']):
+        for to_node in range(data['num_locations']):
            if from_node == to_node:
                _distances[from_node][to_node] = 0
            else:
@@ -112,7 +110,7 @@ def print_solution(data, routing, manager, assignment):  # pylint:disable=too-ma
    """Prints assignment on console"""
    print('Objective: {}'.format(assignment.ObjectiveValue()))
    total_distance = 0
-    for vehicle_id in xrange(data['num_vehicles']):
+    for vehicle_id in range(data['num_vehicles']):
        index = routing.Start(vehicle_id)
        plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
        distance = 0
--- a/ortools/graph/samples/simple_max_flow_program.py
+++ b/ortools/graph/samples/simple_max_flow_program.py
@@ -13,7 +13,6 @@
 # [START program]
 """From Taha 'Introduction to Operations Research', example 6.4-2."""
 # [START import]
-from __future__ import print_function
 from ortools.graph import pywrapgraph
 # [END import]

--- a/ortools/graph/samples/simple_min_cost_flow_program.py
+++ b/ortools/graph/samples/simple_min_cost_flow_program.py
@@ -13,7 +13,6 @@
 # [START program]
 """From Bradley, H. and M., 'Applied Mathematical Programming', figure 8.1."""
 # [START import]
-from __future__ import print_function
 from ortools.graph import pywrapgraph
 # [END import]

--- a/ortools/linear_solver/samples/bin_packing_mip.cc
+++ b/ortools/linear_solver/samples/bin_packing_mip.cc
@@ -41,7 +41,7 @@ void BinPackingMip() {

  // [START solver]
  // Create the mip solver with the CBC backend.
-  MPSolver solver("bin_packing_mip", MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
+  MPSolver solver("bin_packing_mip", MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING);
  // [END solver]

  // [START program_part2]
--- a/ortools/linear_solver/samples/bin_packing_mip.py
+++ b/ortools/linear_solver/samples/bin_packing_mip.py
@@ -13,7 +13,6 @@
 """Solve a simple bin packing problem using a MIP solver."""
 # [START program]
 # [START import]
-from __future__ import print_function
 from ortools.linear_solver import pywraplp

 # [END import]
--- a/ortools/linear_solver/samples/integer_programming_example.py
+++ b/ortools/linear_solver/samples/integer_programming_example.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Small example to illustrate solving a MIP problem."""
 # [START program]
-from __future__ import print_function
 # [START import]
 from ortools.linear_solver import pywraplp
 # [END import]
--- a/ortools/linear_solver/samples/linear_programming_example.py
+++ b/ortools/linear_solver/samples/linear_programming_example.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Linear optimization example."""
 # [START program]
-from __future__ import print_function
 # [START import]
 from ortools.linear_solver import pywraplp

--- a/ortools/linear_solver/samples/mip_var_array.py
+++ b/ortools/linear_solver/samples/mip_var_array.py
@@ -13,7 +13,6 @@
 """MIP example that uses a variable array."""
 # [START program]
 # [START import]
-from __future__ import print_function
 from ortools.linear_solver import pywraplp

 # [END import]
--- a/ortools/linear_solver/samples/multiple_knapsack_mip.cc
+++ b/ortools/linear_solver/samples/multiple_knapsack_mip.cc
@@ -45,7 +45,7 @@ void MultipleKnapsackMip() {
  // [START solver]
  // Create the mip solver with the CBC backend.
  MPSolver solver("multiple_knapsack_mip",
-                  MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
+                  MPSolver::SCIP_MIXED_INTEGER_PROGRAMMING);
  // [END solver]

  // [START program_part2]
--- a/ortools/linear_solver/samples/multiple_knapsack_mip.py
+++ b/ortools/linear_solver/samples/multiple_knapsack_mip.py
@@ -13,7 +13,6 @@
 """Solve a multiple knapsack problem using a MIP solver."""
 # [START program]
 # [START import]
-from __future__ import print_function
 from ortools.linear_solver import pywraplp

 # [END import]
--- a/ortools/linear_solver/samples/simple_lp_program.py
+++ b/ortools/linear_solver/samples/simple_lp_program.py
@@ -13,7 +13,6 @@
 """Minimal example to call the GLOP solver."""
 # [START program]
 # [START import]
-from __future__ import print_function
 from ortools.linear_solver import pywraplp
 # [END import]

--- a/ortools/linear_solver/samples/simple_mip_program.py
+++ b/ortools/linear_solver/samples/simple_mip_program.py
@@ -13,7 +13,6 @@
 """Integer programming examples that show how to use the APIs."""
 # [START program]
 # [START import]
-from __future__ import print_function
 from ortools.linear_solver import pywraplp
 # [END import]

--- a/ortools/sat/samples/binpacking_problem_sat.py
+++ b/ortools/sat/samples/binpacking_problem_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Solves a binpacking problem using the CP-SAT solver."""

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/bool_or_sample_sat.py
+++ b/ortools/sat/samples/bool_or_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample to demonstrates a simple Boolean constraint."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/boolean_product_sample_sat.py
+++ b/ortools/sat/samples/boolean_product_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample that encodes the product of two Boolean variables."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/channeling_sample_sat.py
+++ b/ortools/sat/samples/channeling_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Link integer constraints together."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/cp_is_fun_sat.py
+++ b/ortools/sat/samples/cp_is_fun_sat.py
@@ -20,7 +20,6 @@ This problem has 72 different solutions in base 10.
 """

 # [START program]
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/earliness_tardiness_cost_sample_sat.py
+++ b/ortools/sat/samples/earliness_tardiness_cost_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Encodes an convex piecewise linear function."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/interval_sample_sat.py
+++ b/ortools/sat/samples/interval_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample to demonstrates how to build an interval."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/literal_sample_sat.py
+++ b/ortools/sat/samples/literal_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample to demonstrate Boolean variable and literals."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/minimal_jobshop_sat.py
+++ b/ortools/sat/samples/minimal_jobshop_sat.py
@@ -13,7 +13,6 @@
 """Minimal jobshop example."""

 # [START program]
-from __future__ import print_function

 import collections

--- a/ortools/sat/samples/multiple_knapsack_sat.py
+++ b/ortools/sat/samples/multiple_knapsack_sat.py
@@ -14,9 +14,6 @@
 """Solves a multiple knapsack problem using the CP-SAT solver."""

 # [START import]
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/no_overlap_sample_sat.py
+++ b/ortools/sat/samples/no_overlap_sample_sat.py
@@ -12,9 +12,6 @@
 # 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

--- a/ortools/sat/samples/optional_interval_sample_sat.py
+++ b/ortools/sat/samples/optional_interval_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample to demonstrates how to build an optional interval."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/rabbits_and_pheasants_sat.py
+++ b/ortools/sat/samples/rabbits_and_pheasants_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Rabbits and Pheasants quizz."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/ranking_sample_sat.py
+++ b/ortools/sat/samples/ranking_sample_sat.py
@@ -12,7 +12,6 @@
 # limitations under the License.
 """Code sample to demonstrates how to rank intervals."""

-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/reified_sample_sat.py
+++ b/ortools/sat/samples/reified_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Simple model with a reified constraint."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/scheduling_with_calendar_sample_sat.py
+++ b/ortools/sat/samples/scheduling_with_calendar_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample to demonstrate how an interval can span across a break."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/search_for_all_solutions_sample_sat.py
+++ b/ortools/sat/samples/search_for_all_solutions_sample_sat.py
@@ -13,9 +13,6 @@
 """Code sample that solves a model and displays all solutions."""

 # [START program]
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/simple_sat_program.py
+++ b/ortools/sat/samples/simple_sat_program.py
@@ -13,9 +13,6 @@
 """Simple solve."""

 # [START program]
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/solution_hinting_sample_sat.py
+++ b/ortools/sat/samples/solution_hinting_sample_sat.py
@@ -13,9 +13,6 @@
 """Code sample that solves a model using solution hinting."""

 # [START program]
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/solve_and_print_intermediate_solutions_sample_sat.py
+++ b/ortools/sat/samples/solve_and_print_intermediate_solutions_sample_sat.py
@@ -13,9 +13,6 @@
 """Solves an optimization problem and displays all intermediate solutions."""

 # [START program]
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/solve_with_time_limit_sample_sat.py
+++ b/ortools/sat/samples/solve_with_time_limit_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Solves a problem with a time limit."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/step_function_sample_sat.py
+++ b/ortools/sat/samples/step_function_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Implements a step function."""

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

 from ortools.sat.python import cp_model

--- a/ortools/sat/samples/stop_after_n_solutions_sample_sat.py
+++ b/ortools/sat/samples/stop_after_n_solutions_sample_sat.py
@@ -12,9 +12,6 @@
 # limitations under the License.
 """Code sample that solves a model and displays a small number of solutions."""

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

 from ortools.sat.python import cp_model