From c2ae098d34955277ca2da42f784139a2285814ce Mon Sep 17 00:00:00 2001
From: Corentin Le Molgat <corentinl@google.com>
Date: Wed, 28 Nov 2018 10:56:33 +0100
Subject: [PATCH] Apply yapf on examples/python/*.py

---
 examples/python/appointments.py               |   10 +-
 examples/python/assignment_sat.py             |   23 +-
 .../python/assignment_with_constraints_sat.py |   17 +-
 examples/python/balance_group_sat.py          |    4 +-
 examples/python/chemical_balance_lp.py        |   10 +-
 examples/python/chemical_balance_sat.py       |    6 +-
 examples/python/cvrp.py                       |    4 +-
 examples/python/cvrp_reload.py                |   19 +-
 examples/python/cvrptw.py                     |   22 +-
 examples/python/cvrptw_plot.py                |   18 +-
 examples/python/flexible_job_shop_sat.py      |    6 +-
 examples/python/gate_scheduling_sat.py        |   10 +-
 examples/python/golomb8.py                    |    4 +-
 examples/python/hidato_sat.py                 |   26 +-
 examples/python/integer_programming.py        |    4 +-
 examples/python/jobshop_ft06_distance_sat.py  |    4 +-
 examples/python/knapsack.py                   |   18 +-
 examples/python/linear_assignment_api.py      |    3 +-
 examples/python/linear_programming.py         |   15 +-
 examples/python/nqueens_sat.py                |    3 +-
 examples/python/nurses_sat.py                 |    8 +-
 examples/python/pyflow_example.py             |    7 +-
 examples/python/qubo_sat.py                   | 1163 ++++++++---------
 examples/python/random_tsp.py                 |    4 +-
 examples/python/rcpsp_sat.py                  |   17 +-
 examples/python/shift_scheduling_sat.py       |   10 +-
 ...duling_with_setup_release_due_dates_sat.py |   13 +-
 examples/python/steel_mill_slab_sat.py        |   43 +-
 examples/python/stigler_diet.py               |  341 +++--
 examples/python/sudoku_sat.py                 |    8 +-
 examples/python/task_allocation_sat.py        |  443 +++----
 .../tasks_and_workers_assignment_sat.py       |    3 +-
 examples/python/transit_time.py               |   19 +-
 examples/python/tsp.py                        |    4 +-
 examples/python/vendor_scheduling_sat.py      |   21 +-
 examples/python/vrp.py                        |    4 +-
 examples/python/vrpgs.py                      |    4 +-
 examples/python/wedding_optimal_chart_sat.py  |   53 +-
 examples/python/worker_schedule_sat.py        |  185 ++-
 39 files changed, 1168 insertions(+), 1408 deletions(-)

diff --git a/examples/python/appointments.py b/examples/python/appointments.py
index aede63f771..09e704c151 100644
--- a/examples/python/appointments.py
+++ b/examples/python/appointments.py
@@ -133,11 +133,10 @@ def get_optimal_schedule(demand, args):
     """Computes the optimal schedule for the appointment selection problem."""
     combinations = find_combinations([a[2] for a in demand], args.load_min,
                                      args.load_max, args.commute_time)
-    print(
-        'found %d possible combinations of appointements' % len(combinations))
+    print('found %d possible combinations of appointements' % len(combinations))
 
-    cost, selection = select(combinations, [a[0] for a in demand],
-                             args.num_workers)
+    cost, selection = select(combinations, [a[0]
+                                            for a in demand], args.num_workers)
     output = [(selection[i], [(combinations[i][t], demand[t][1])
                               for t in range(len(demand))
                               if combinations[i][t] != 0])
@@ -152,8 +151,7 @@ def main(args):
     for a in demand:
         print('   %d * %s : %d min' % (a[0], a[1], a[2]))
     print('commute time = %d' % args.commute_time)
-    print(
-        'accepted total duration = [%d..%d]' % (args.load_min, args.load_max))
+    print('accepted total duration = [%d..%d]' % (args.load_min, args.load_max))
     print('%d workers' % args.num_workers)
     cost, selection = get_optimal_schedule(demand, args)
     print('Optimal solution as a cost of %d' % cost)
diff --git a/examples/python/assignment_sat.py b/examples/python/assignment_sat.py
index 75f2bee1d2..78a3cbad2b 100644
--- a/examples/python/assignment_sat.py
+++ b/examples/python/assignment_sat.py
@@ -17,16 +17,15 @@ from ortools.sat.python import cp_model
 
 def main():
     # Instantiate a cp model.
-    cost = [[90, 76, 75, 70, 50, 74, 12,
-             68], [35, 85, 55, 65, 48, 101, 70, 83],
-            [125, 95, 90, 105, 59, 120, 36, 73],
-            [45, 110, 95, 115, 104, 83, 37, 71],
-            [60, 105, 80, 75, 59, 62, 93,
-             88], [45, 65, 110, 95, 47, 31, 81, 34],
-            [38, 51, 107, 41, 69, 99, 115, 48],
-            [47, 85, 57, 71, 92, 77, 109, 36],
-            [39, 63, 97, 49, 118, 56, 92, 61],
-            [47, 101, 71, 60, 88, 109, 52, 90]]
+    cost = [[90, 76, 75, 70, 50, 74, 12, 68], [35, 85, 55, 65, 48, 101, 70, 83],
+            [125, 95, 90, 105, 59,
+             120, 36, 73], [45, 110, 95, 115, 104, 83, 37,
+                            71], [60, 105, 80, 75, 59, 62, 93,
+                                  88], [45, 65, 110, 95, 47, 31, 81, 34],
+            [38, 51, 107, 41, 69, 99, 115,
+             48], [47, 85, 57, 71, 92, 77, 109,
+                   36], [39, 63, 97, 49, 118, 56,
+                         92, 61], [47, 101, 71, 60, 88, 109, 52, 90]]
 
     sizes = [10, 7, 3, 12, 15, 4, 11, 5]
     total_size_max = 15
@@ -55,8 +54,8 @@ def main():
         model.Add(sum(sizes[j] * x[i][j] for j in all_tasks) <= total_size_max)
 
     # Total cost
-    model.Add(total_cost == sum(
-        x[i][j] * cost[i][j] for j in all_tasks for i in all_workers))
+    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)
 
     solver = cp_model.CpSolver()
diff --git a/examples/python/assignment_with_constraints_sat.py b/examples/python/assignment_with_constraints_sat.py
index a0ebf5092b..71f9340f55 100644
--- a/examples/python/assignment_with_constraints_sat.py
+++ b/examples/python/assignment_with_constraints_sat.py
@@ -20,12 +20,14 @@ from ortools.sat.python import cp_model
 def solve_assignment():
     """Solve the assignment problem."""
     # 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]]
+    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
@@ -79,8 +81,7 @@ def solve_assignment():
     # Total task size for each worker is at most total_size_max
     for i in all_workers:
         model.Add(
-            sum(sizes[j] * selected[i][j]
-                for j in all_tasks) <= total_size_max)
+            sum(sizes[j] * selected[i][j] for j in all_tasks) <= total_size_max)
 
     # Group constraints.
     model.AddAllowedAssignments([works[0], works[1], works[2], works[3]],
diff --git a/examples/python/balance_group_sat.py b/examples/python/balance_group_sat.py
index 6d43d69851..f661932696 100644
--- a/examples/python/balance_group_sat.py
+++ b/examples/python/balance_group_sat.py
@@ -102,8 +102,8 @@ def main():
     item_in_group = {}
     for i in all_items:
         for g in all_groups:
-            item_in_group[(i, g)] = model.NewBoolVar(
-                'item %d in group %d' % (i, g))
+            item_in_group[(i, g)] = model.NewBoolVar('item %d in group %d' %
+                                                     (i, g))
 
     # Each group must have the same size.
     for g in all_groups:
diff --git a/examples/python/chemical_balance_lp.py b/examples/python/chemical_balance_lp.py
index 8436be98e2..8862092507 100644
--- a/examples/python/chemical_balance_lp.py
+++ b/examples/python/chemical_balance_lp.py
@@ -30,9 +30,9 @@ max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
                   ["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
 
 chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
-                ["C", 61, 149, 384, 0, 30, 1, 0.2],
-                ["D", 148, 70, 245, 0, 15, 1, 0.2],
-                ["E", 160, 158, 161, 0, 10, 1, 0.2]]
+                ["C", 61, 149, 384, 0, 30, 1,
+                 0.2], ["D", 148, 70, 245, 0, 15, 1,
+                        0.2], ["E", 160, 158, 161, 0, 10, 1, 0.2]]
 
 num_products = len(max_quantities)
 all_products = range(num_products)
@@ -87,6 +87,6 @@ for s in all_sets:
 for p in all_products:
     name = max_quantities[p][0]
     max_quantity = max_quantities[p][1]
-    quantity = sum(set_vars[s].solution_value() * chemical_set[s][p + 1]
-                   for s in all_sets)
+    quantity = sum(
+        set_vars[s].solution_value() * chemical_set[s][p + 1] for s in all_sets)
     print("%s: %f out of %f" % (name, quantity, max_quantity))
diff --git a/examples/python/chemical_balance_sat.py b/examples/python/chemical_balance_sat.py
index 4185ac236d..363513efae 100644
--- a/examples/python/chemical_balance_sat.py
+++ b/examples/python/chemical_balance_sat.py
@@ -29,9 +29,9 @@ max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
                   ["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
 
 chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
-                ["C", 61, 149, 384, 0, 30, 1, 0.2],
-                ["D", 148, 70, 245, 0, 15, 1, 0.2],
-                ["E", 160, 158, 161, 0, 10, 1, 0.2]]
+                ["C", 61, 149, 384, 0, 30, 1,
+                 0.2], ["D", 148, 70, 245, 0, 15, 1,
+                        0.2], ["E", 160, 158, 161, 0, 10, 1, 0.2]]
 
 num_products = len(max_quantities)
 all_products = range(num_products)
diff --git a/examples/python/cvrp.py b/examples/python/cvrp.py
index ba7671759a..ba0c34a641 100755
--- a/examples/python/cvrp.py
+++ b/examples/python/cvrp.py
@@ -76,8 +76,8 @@ def create_data_model():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 def create_distance_evaluator(data):
diff --git a/examples/python/cvrp_reload.py b/examples/python/cvrp_reload.py
index 02f389c182..58c2765fa6 100755
--- a/examples/python/cvrp_reload.py
+++ b/examples/python/cvrp_reload.py
@@ -114,8 +114,8 @@ def create_data_model():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 def create_distance_evaluator(data):
@@ -198,9 +198,8 @@ def create_time_evaluator(data):
         if from_node == to_node:
             travel_time = 0
         else:
-            travel_time = manhattan_distance(
-                data['locations'][from_node],
-                data['locations'][to_node]) / data['vehicle_speed']
+            travel_time = manhattan_distance(data['locations'][from_node], data[
+                'locations'][to_node]) / data['vehicle_speed']
         return travel_time
 
     _total_time = {}
@@ -212,8 +211,8 @@ def create_time_evaluator(data):
                 _total_time[from_node][to_node] = 0
             else:
                 _total_time[from_node][to_node] = int(
-                    service_time(data, from_node) +
-                    travel_time(data, from_node, to_node))
+                    service_time(data, from_node) + travel_time(
+                        data, from_node, to_node))
 
     def time_evaluator(manager, from_node, to_node):
         """Returns the total time between the two nodes"""
@@ -279,7 +278,8 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
             load_var = capacity_dimension.CumulVar(index)
             time_var = time_dimension.CumulVar(index)
             plan_output += ' {0} Load({1}) Time({2},{3}) ->'.format(
-                manager.IndexToNode(index), assignment.Value(load_var),
+                manager.IndexToNode(index),
+                assignment.Value(load_var),
                 assignment.Min(time_var), assignment.Max(time_var))
             previous_index = index
             index = assignment.Value(routing.NextVar(index))
@@ -288,7 +288,8 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
         load_var = capacity_dimension.CumulVar(index)
         time_var = time_dimension.CumulVar(index)
         plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
-            manager.IndexToNode(index), assignment.Value(load_var),
+            manager.IndexToNode(index),
+            assignment.Value(load_var),
             assignment.Min(time_var), assignment.Max(time_var))
         plan_output += 'Distance of the route: {}m\n'.format(distance)
         plan_output += 'Load of the route: {}\n'.format(
diff --git a/examples/python/cvrptw.py b/examples/python/cvrptw.py
index 297b368adb..0f11b43ad2 100755
--- a/examples/python/cvrptw.py
+++ b/examples/python/cvrptw.py
@@ -88,8 +88,8 @@ def create_data_model():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 def create_distance_evaluator(data):
@@ -147,9 +147,8 @@ def create_time_evaluator(data):
         if from_node == to_node:
             travel_time = 0
         else:
-            travel_time = manhattan_distance(
-                data['locations'][from_node],
-                data['locations'][to_node]) / data['vehicle_speed']
+            travel_time = manhattan_distance(data['locations'][from_node], data[
+                'locations'][to_node]) / data['vehicle_speed']
         return travel_time
 
     _total_time = {}
@@ -161,8 +160,8 @@ def create_time_evaluator(data):
                 _total_time[from_node][to_node] = 0
             else:
                 _total_time[from_node][to_node] = int(
-                    service_time(data, from_node) +
-                    travel_time(data, from_node, to_node))
+                    service_time(data, from_node) + travel_time(
+                        data, from_node, to_node))
 
     def time_evaluator(manager, from_node, to_node):
         """Returns the total time between the two nodes"""
@@ -222,8 +221,10 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
             time_var = time_dimension.CumulVar(index)
             slack_var = time_dimension.SlackVar(index)
             plan_output += ' {0} Load({1}) Time({2},{3}) Slack({4},{5}) ->'.format(
-                manager.IndexToNode(index), assignment.Value(load_var),
-                assignment.Min(time_var), assignment.Max(time_var),
+                manager.IndexToNode(index),
+                assignment.Value(load_var),
+                assignment.Min(time_var),
+                assignment.Max(time_var),
                 assignment.Min(slack_var), assignment.Max(slack_var))
             previous_index = index
             index = assignment.Value(routing.NextVar(index))
@@ -233,7 +234,8 @@ def print_solution(data, manager, routing, assignment):  # pylint:disable=too-ma
         time_var = time_dimension.CumulVar(index)
         slack_var = time_dimension.SlackVar(index)
         plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
-            manager.IndexToNode(index), assignment.Value(load_var),
+            manager.IndexToNode(index),
+            assignment.Value(load_var),
             assignment.Min(time_var), assignment.Max(time_var))
         plan_output += 'Distance of the route: {0}m\n'.format(distance)
         plan_output += 'Load of the route: {}\n'.format(
diff --git a/examples/python/cvrptw_plot.py b/examples/python/cvrptw_plot.py
index 7b5de8fba2..824b3ae8db 100644
--- a/examples/python/cvrptw_plot.py
+++ b/examples/python/cvrptw_plot.py
@@ -92,10 +92,10 @@ class Customers():
             self.extents = extents  #: The lower left and upper right points
             #: Location[lat,lon]: the centre point of the area.
             self.center = Location(
-                extents['urcrnrlat'] -
-                0.5 * (extents['urcrnrlat'] - extents['llcrnrlat']),
-                extents['urcrnrlon'] -
-                0.5 * (extents['urcrnrlon'] - extents['llcrnrlon']))
+                extents['urcrnrlat'] - 0.5 *
+                (extents['urcrnrlat'] - extents['llcrnrlat']),
+                extents['urcrnrlon'] - 0.5 *
+                (extents['urcrnrlon'] - extents['llcrnrlon']))
         else:
             #: Location[lat,lon]: the centre point of the area.
             (clat, clon) = self.center = Location(center[0], center[1])
@@ -103,14 +103,12 @@ class Customers():
             circ_earth = np.pi * rad_earth
             #: The lower left and upper right points
             self.extents = {
-                'llcrnrlon':
-                (clon -
-                 180 * box_size / (circ_earth * np.cos(np.deg2rad(clat)))),
+                'llcrnrlon': (clon - 180 * box_size /
+                              (circ_earth * np.cos(np.deg2rad(clat)))),
                 'llcrnrlat':
                 clat - 180 * box_size / circ_earth,
-                'urcrnrlon':
-                (clon +
-                 180 * box_size / (circ_earth * np.cos(np.deg2rad(clat)))),
+                'urcrnrlon': (clon + 180 * box_size /
+                              (circ_earth * np.cos(np.deg2rad(clat)))),
                 'urcrnrlat':
                 clat + 180 * box_size / circ_earth
             }
diff --git a/examples/python/flexible_job_shop_sat.py b/examples/python/flexible_job_shop_sat.py
index 87aa7319a7..28c89374d8 100644
--- a/examples/python/flexible_job_shop_sat.py
+++ b/examples/python/flexible_job_shop_sat.py
@@ -38,9 +38,9 @@ def flexible_jobshop():
     jobs = [[[(3, 0), (1, 1), (5, 2)], [(2, 0), (4, 1), (6, 2)],
              [(2, 0), (3, 1), (1, 2)]],
             [[(2, 0), (3, 1), (4, 2)], [(1, 0), (5, 1), (4, 2)],
-             [(2, 0), (1, 1), (4, 2)]],
-            [[(2, 0), (1, 1), (4, 2)], [(2, 0), (3, 1), (4, 2)],
-             [(3, 0), (1, 1), (5, 2)]]]
+             [(2, 0), (1, 1), (4, 2)]], [[(2, 0), (1, 1),
+                                          (4, 2)], [(2, 0), (3, 1), (4, 2)],
+                                         [(3, 0), (1, 1), (5, 2)]]]
 
     num_jobs = len(jobs)
     all_jobs = range(num_jobs)
diff --git a/examples/python/gate_scheduling_sat.py b/examples/python/gate_scheduling_sat.py
index 22d198cd77..6b226519ff 100644
--- a/examples/python/gate_scheduling_sat.py
+++ b/examples/python/gate_scheduling_sat.py
@@ -51,8 +51,7 @@ def main():
         start = model.NewIntVar(0, horizon, 'start_%i' % i)
         duration = jobs[i][0]
         end = model.NewIntVar(0, horizon, 'end_%i' % i)
-        interval = model.NewIntervalVar(start, duration, end,
-                                        'interval_%i' % i)
+        interval = model.NewIntervalVar(start, duration, end, 'interval_%i' % i)
         starts.append(start)
         intervals.append(interval)
         ends.append(end)
@@ -114,8 +113,7 @@ def main():
             d_y = jobs[i][1]
             s_y = performed_machine * (max_length - d_y)
             output.AddRectangle(start, s_y, d_x, d_y,
-                                color_manager.RandomColor(), 'black',
-                                'j%i' % i)
+                                color_manager.RandomColor(), 'black', 'j%i' % i)
 
         output.AddXScale()
         output.AddYScale()
@@ -126,8 +124,8 @@ def main():
         for i in all_jobs:
             performed_machine = 1 - solver.Value(performed[i])
             start = solver.Value(starts[i])
-            print('  - Job %i starts at %i on machine %i' %
-                  (i, start, performed_machine))
+            print('  - Job %i starts at %i on machine %i' % (i, start,
+                                                             performed_machine))
         print('Statistics')
         print('  - conflicts : %i' % solver.NumConflicts())
         print('  - branches  : %i' % solver.NumBranches())
diff --git a/examples/python/golomb8.py b/examples/python/golomb8.py
index ac4c81f896..27dffbde59 100644
--- a/examples/python/golomb8.py
+++ b/examples/python/golomb8.py
@@ -42,8 +42,8 @@ def main():
     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)
+            marks[j] - marks[i]
+            for i in range(0, size - 1) for j in range(i + 1, size)
         ]))
 
     solver.Add(marks[size - 1] - marks[size - 2] > marks[1] - marks[0])
diff --git a/examples/python/hidato_sat.py b/examples/python/hidato_sat.py
index f12a6de2f6..1bc30a3489 100644
--- a/examples/python/hidato_sat.py
+++ b/examples/python/hidato_sat.py
@@ -31,10 +31,13 @@ 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))]
+    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))
+    ]
 
 
 def print_solution(positions, rows, cols):
@@ -80,8 +83,8 @@ 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:
@@ -103,9 +106,10 @@ 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
 
 
@@ -168,8 +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(
diff --git a/examples/python/integer_programming.py b/examples/python/integer_programming.py
index 7ea2fbc667..6f3a6e5149 100644
--- a/examples/python/integer_programming.py
+++ b/examples/python/integer_programming.py
@@ -80,8 +80,8 @@ def SolveAndPrint(solver, variable_list):
 
 
 def Announce(solver, api_type):
-    print(('---- Integer programming example with ' + solver + ' (' + api_type
-           + ') -----'))
+    print(('---- Integer programming example with ' + solver + ' (' + api_type +
+           ') -----'))
 
 
 def RunAllIntegerExampleNaturalLanguageAPI():
diff --git a/examples/python/jobshop_ft06_distance_sat.py b/examples/python/jobshop_ft06_distance_sat.py
index 06c12ab0c0..7b5867505b 100644
--- a/examples/python/jobshop_ft06_distance_sat.py
+++ b/examples/python/jobshop_ft06_distance_sat.py
@@ -102,8 +102,8 @@ 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)
 
diff --git a/examples/python/knapsack.py b/examples/python/knapsack.py
index 5437721fb1..cb4adaac07 100644
--- a/examples/python/knapsack.py
+++ b/examples/python/knapsack.py
@@ -21,23 +21,23 @@ def main():
         pywrapknapsack_solver.KnapsackSolver.
         KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, 'test')
     profits = [
-        360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48,
-        147, 78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514,
-        28, 87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389,
-        276, 312
+        360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,
+        78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28,
+        87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276,
+        312
     ]
     weights = [[
-        7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9,
-        0, 42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44,
-        71, 3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13
+        7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,
+        42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71,
+        3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13
     ]]
     capacities = [850]
     optimal_profit = 7534
     solver.Init(profits, weights, capacities)
     computed_profit = solver.Solve()
 
-    print(('optimal profit = ' + str(computed_profit) + '/' +
-           str(optimal_profit)))
+    print((
+        'optimal profit = ' + str(computed_profit) + '/' + str(optimal_profit)))
 
 
 if __name__ == '__main__':
diff --git a/examples/python/linear_assignment_api.py b/examples/python/linear_assignment_api.py
index 75b33338e9..e497ab4e61 100644
--- a/examples/python/linear_assignment_api.py
+++ b/examples/python/linear_assignment_api.py
@@ -46,8 +46,7 @@ def RunAssignmentOn4x4Matrix():
         print('No perfect matching exists.')
     elif solve_status == assignment.POSSIBLE_OVERFLOW:
         print(
-            'Some input costs are too large and may cause an integer overflow.'
-        )
+            'Some input costs are too large and may cause an integer overflow.')
 
 
 def main():
diff --git a/examples/python/linear_programming.py b/examples/python/linear_programming.py
index 8e93466091..40740e7c72 100644
--- a/examples/python/linear_programming.py
+++ b/examples/python/linear_programming.py
@@ -70,12 +70,15 @@ def main():
     print('x: reduced cost =', x.reduced_cost())
     print('y: reduced cost =', y.reduced_cost())
     activities = solver.ComputeConstraintActivities()
-    print('constraint0: dual value =', constraint0.dual_value(),
-          ' activities =', activities[constraint0.index()])
-    print('constraint1: dual value =', constraint1.dual_value(),
-          ' activities =', activities[constraint1.index()])
-    print('constraint2: dual value =', constraint2.dual_value(),
-          ' activities =', activities[constraint2.index()])
+    print('constraint0: dual value =',
+          constraint0.dual_value(), ' activities =',
+          activities[constraint0.index()])
+    print('constraint1: dual value =',
+          constraint1.dual_value(), ' activities =',
+          activities[constraint1.index()])
+    print('constraint2: dual value =',
+          constraint2.dual_value(), ' activities =',
+          activities[constraint2.index()])
 
 
 if __name__ == '__main__':
diff --git a/examples/python/nqueens_sat.py b/examples/python/nqueens_sat.py
index f459190d9d..aab8560930 100644
--- a/examples/python/nqueens_sat.py
+++ b/examples/python/nqueens_sat.py
@@ -53,8 +53,7 @@ def main(board_size):
     # Creates the variables.
     # The array index is the column, and the value is the row.
     queens = [
-        model.NewIntVar(0, board_size - 1, 'x%i' % i)
-        for i in range(board_size)
+        model.NewIntVar(0, board_size - 1, 'x%i' % i) for i in range(board_size)
     ]
     # Creates the constraints.
 
diff --git a/examples/python/nurses_sat.py b/examples/python/nurses_sat.py
index 9f7308a6c0..6f07bbe4eb 100644
--- a/examples/python/nurses_sat.py
+++ b/examples/python/nurses_sat.py
@@ -63,8 +63,8 @@ def main():
     for n in all_nurses:
         for d in all_days:
             for s in all_shifts:
-                shifts[(n, d,
-                        s)] = model.NewBoolVar('shift_n%id%is%i' % (n, d, s))
+                shifts[(n, d, s)] = model.NewBoolVar('shift_n%id%is%i' % (n, d,
+                                                                          s))
 
     # Makes assignments different on each day, that is each shift is assigned at
     # most one nurse. As we have the same number of nurses and shifts, then each
@@ -88,8 +88,8 @@ def main():
     works_shift = {}
     for n in all_nurses:
         for s in all_shifts:
-            works_shift[(n,
-                         s)] = model.NewBoolVar('works_shift_n%is%i' % (n, s))
+            works_shift[(n, s)] = model.NewBoolVar('works_shift_n%is%i' % (n,
+                                                                           s))
             model.AddMaxEquality(works_shift[(n, s)],
                                  [shifts[(n, d, s)] for d in all_days])
 
diff --git a/examples/python/pyflow_example.py b/examples/python/pyflow_example.py
index e3544b86b5..53a0d86abe 100644
--- a/examples/python/pyflow_example.py
+++ b/examples/python/pyflow_example.py
@@ -63,9 +63,10 @@ def MinCostFlow():
         print('Total flow', min_cost_flow.OptimalCost(), '/', expected_cost)
         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)))
+                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)))
     else:
         print('There was an issue with the min cost flow input.')
 
diff --git a/examples/python/qubo_sat.py b/examples/python/qubo_sat.py
index 744a663e1f..bb91060a70 100644
--- a/examples/python/qubo_sat.py
+++ b/examples/python/qubo_sat.py
@@ -6,641 +6,534 @@ import time
 
 from ortools.sat.python import cp_model
 
-
-RAW_DATA = [
-    [
-        0, 0, 49.774821, -59.5968886, -46.0773896, 0, -65.166109, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 47.0957778, 15.259961, -98.7983264, 0, 0, 0,
-        -20.7757184, 0, 87.2645672, 0, -22.7888772, 0, 0, 0, -40.4980904,
-        -19.7307486, -23.222078, 0, -77.5263128, 0, 0, 56.6204008, 0, 0, 0, 0,
-        0, 0, 0, 31.378421, 0, 97.3441448, 35.1309806, 0, 0, -40.5727886,
-        -50.7308566, 0, 0, -69.9304568, 0, 38.5385914, 0, -22.1243232, 0, 0, 0,
-        0, -62.5102538, 8.0801276, 46.7998066, -2.3292106, 0, 0, 0, 8.774031,
-        0, 0, -65.6505736
-    ],
-    [
-        -67.2935466, -64.4354852, -96.6712204, 0, 0, -60.7812272, 0, 0, 0, 0,
-        0, -7.9966864, 0, 0, 0, 0, 0, 0, 0, 89.7672338, 0, 0, 0, 0, 98.9607046,
-        0, 28.6714432, 0, 0, 0, -26.2738856, 0, 0, 68.363956, 0, 0, 0, 0,
-        54.7406868, 0, 0, 0, 0, 94.2320734, 0, 0, 0, 0, 0, 0, 0, 0, -2.9647794,
-        39.7161716, -54.7931288, 0, 0, 0, 0, -47.2284892, 0, 0, 0, -8.6421808,
-        -35.399612, 0, 0, 62.1912668, 0, -6.8930716, 0, 0, -17.0801284, 0, 0,
-        68.6533416
-    ],
-    [
-        0, 0, 0, 81.165396, 83.773254, 0, -25.1603, 0, 0, 50.225725, 0,
-        -3.8242274, 0, 0, 36.2078566, 0, 0, 0, 0, 0, 0, 0, 0, 15.551432, 0, 0,
-        -33.6446236, 0, 0, 0, 36.6171324, 0, 0, 0, 0, 67.9591934, 0,
-        22.1428016, 0, -27.2961928, 0, 0, 0, -97.4961564, 90.4062526, 0, 0, 0,
-        -90.0532814, 0, 98.8332924, 0, 0, -13.8994926, 0, 17.1962884, 0, 0, 0,
-        -55.1654678, 0, 0, 0, 85.829554, 0, -37.971164, 64.233136, -17.9943296,
-        0, 0, 0, 0, -67.7509796, 0, 0, 10.0750712
-    ],
-    [
-        0, 37.2783148, 0, 0, 0, 36.4959506, 0, 0, 0, 0, 0, 0, 61.201323, 0,
-        14.4328522, 48.4078064, 0, 0, 0, 0, 0, 0, 0, -47.0969056, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 26.720439, 0, 0, 62.1987576, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, -65.2085246, 0, 0, 0, 0, 0, 0, 73.3019432, -14.3431238,
-        0, 0, 0, 0, 0, 0, 0, 0, 2.1565846, 0, 0, 0.7733644, 0, 5.9090456, 0,
-        -39.7724192
-    ],
-    [
-        0, 0, 0, 0, -24.4555532, 0, 0, -5.5484574, 0, 25.4685054, 0.7906104,
-        4.273133, 0, 52.12973, 0, -12.8040828, 0, 0, 81.888381, 64.0713498, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62.9088768, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        20.869713, 0, 0, -71.5835872, 0, 0, 80.7237808, 0, 0, 0, 0,
-        -60.1883708, 0, 0, 0, 0, 0, 0, 0, 0, 85.0393326, 23.6045316,
-        -18.8849834, 0, 0, 0, -90.8065188, -9.5037982, 14.3196654, 0,
-        -28.9290306, 0, 0, -41.5575766
-    ],
-    [
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8.9478934, 0,
-        -83.6040618, 0, 0, 0, 0, -14.3874822, 77.2528714, 0, 0, 99.2966066, 0,
-        21.7889114, -37.7629282, 0, -11.6026582, 0, 0, 0, 0, 74.422603, 0, 0,
-        -79.239245, -31.9686324, 0, 0, 0, 0, 0, 0, -29.8797178, 0, 0,
-        85.2723062, 0, 0, -8.8031188, 0, 0, -20.043565, 0, 0, -70.733454, 0,
-        -94.7231762, -85.4584516, 0, 0, 0, 0, -27.6068624, 0, -79.787783, 0, 0,
-        -55.1894266
-    ],
-    [
-        1.9374354, 0, 1.4807184, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 61.298952, 0, 0, 0, -90.5702054, 0, 67.381115, 0, -68.684637, 0, 0,
-        0, 0, 0, 0, 0, 0, 93.7807934, 0, 8.8213302, -15.9020466, 0, 0, 0, 0,
-        23.8157662, 0, 0, 0, 0, 0, 0, 0, 67.3461972, 0, 0, 0, 0, 0, 43.3263744,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 87.7149706, 0, 0, 0, -18.5133574,
-        -30.0338992
-    ],
-    [
-        6.3937798, 78.7697644, 28.4485838, 0, 0, 0.1352466, 0, 0, 74.5767122,
-        -13.8340168, 0, 0, 0, 77.2929426, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        17.9243834, 0, 0, 82.2239878, 0, 0, 0, 0, 64.3440016, 90.109577,
-        46.8926522, -2.4494366, 0, 84.7413412, 0, 0, -4.216108, 0, 0,
-        -79.8684776, 0, 0, 74.8706758, -64.4518992, 0, 0, 0, 0, -34.4895004, 0,
-        0, 0, -74.1158858, -37.7803516, 0, 0, 0, 0, 0, 80.0054296, 0, 0, 0, 0,
-        0, 0, 0, 0, -84.5832026, 0, 0, 71.2540694
-    ],
-    [
-        0, 0, 0, 0, -74.9257454, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        14.9675444, 0, 0, 0, 0, 0, -27.5236912, 0, 0, -80.4993438, 0,
-        -81.8494538, 0, 0, 0, 0, 0, -18.6802002, 0, 0, 0, 0, 0, 0, 0,
-        61.4131076, 0, 0, -55.1421034, 0, 0, -18.576761, 72.3500914, 0, 0, 0,
-        0, 0, 0, -23.6460116, 43.1258024, 0, 93.701872, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, -86.5772554
-    ],
-    [
-        0, 0, 93.0762916, 0, 0, 0, 0, 0, 0, 0, -37.2663938, 86.8303764, 0, 0,
-        0, -51.9596226, 0, 35.6722618, -91.438259, 0, 0, -70.6277108, 0,
-        -82.9146992, 58.0327648, 0, 0, 0, 0, 0, 0, 0, 0, 13.3727302, 0, 0, 0,
-        23.5719942, 0, -21.5445476, 74.1541634, 60.6365036, 97.4447708, 0, 0,
-        0, 0, 82.5869498, 0, 85.1132108, 0, 0, 0, 0, 0, 0, 97.4012534, 0, 0, 0,
-        0, -45.1504048, 1.0619934, 59.7140264, 0, 0, 0, 0, 0, 4.177461, 0, 0,
-        0, -75.7039276, 0, 0.0421338
-    ],
-    [
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -71.171869, 0, 0, 8.685266, 0, 0, 0, 0,
-        33.9089574, 0, -31.6154498, 0, 0, 0, 0, 0, 5.3182746, 0, 0, 0, 0, 0, 0,
-        3.0361166, -10.364305, 0, 0, 0, 0, 0, 0, 0, 0, -83.9738444, 0, 0,
-        -7.9170212, 0, 0, 0, -28.7575682, 0, 0, 0, -29.9216686, 0, 0,
-        83.4050918, -39.5247364, -6.7028846, 0, 0, 0, -23.6080482, 0, 0, 0, 0,
-        0, 0, -18.380154, 46.9252306, 0, 0, 26.1618372, 99.6235254
-    ],
-    [
-        0, 0, 0, 0, 0, 0, 0, 52.008307, 0, 0, 0, 0, 0, 92.4974102, -76.3015714,
-        0, 0, 0, 0, 0, 0, 0, -56.4879132, 0, 0, 0, 50.1473938, 0, 0, 0, 0, 0,
-        0, 0, 0, 40.2219566, 0, 0, 0, 84.5162074, 0, 0, 0, -73.3030606, 0,
-        -10.9258316, 0, 0, 0, 0, 97.5496436, -70.5026182, 0, 62.3611696, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 96.4362226, 0, 0, 0, 0, 0, 0, 0,
-        -6.0104764, 15.7466756
-    ],
-    [
-        -38.7678174, 0, 0, 0, 0, 10.5238486, 0, 0, 0, 31.6876676, 79.6111978,
-        0, 0, 0, 0, 45.7314046, 0, 0, 0, -10.0125122, 0, 93.3170242,
-        -96.4566224, -5.853298, 0, -82.2848728, 0, 0, 0, 0, 0, 0, 0,
-        43.3427638, 24.6186934, 0, 44.859548, 0, -63.8196424, 0, 32.6630616,
-        41.48423, 0, -42.9613722, 0, 0, -68.8954844, 0, 0, 0, 0, 0, 0, 0, 0,
-        27.7424034, -33.4867534, -49.1827758, 0, 18.7014116, 0, 0, 59.049662,
-        0, 0, 0, 0, 0, 0, -29.6305028, 0, 0, 0, 0, 0, 98.2266078
-    ],
-    [
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -14.6583468, 0, -74.4490466, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, -94.9604028, 0, 0, -48.28403, -41.3534342, 0, 0, 0,
-        62.9532972, 0, 0, 4.030284, 0, -60.478996, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, -46.5887848, 39.4565458, 0, 0, 0, 0, 0, 0, 0,
-        -48.5071236, 0, 0, 0, 41.8640204, 0, 0, 0, 74.271524, 0, 15.5769242, 0,
-        -61.4793904, 52.4500934
-    ],
-    [
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 50.8387116, 0, 46.490051, 0, -54.9751352,
-        0, 43.0696416, 0, 0, 0, 0, 80.5337704, 0, 0, -16.0325234, 0, 0, 0, 0,
-        0, 0, 0, 0, 63.186351, 0, 0, 0, 0, 0, 75.2218604, -27.3783446, 0, 0, 0,
-        0, -85.021934, 60.9043202, 55.7344594, 0, 41.1687556, 0, 5.574124, 0,
-        0, -5.0028254, -40.2614834, 0, 0, 0, 0, 0, 0, 22.207679, 0, 0, 0, 0, 0,
-        65.0504204, 0, -61.4580018, -90.137276, 19.2277196, 0, 0, -73.8615034
-    ],
-    [
-        0, 0, 0, 0, 0, -21.3303052, -75.4586018, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 84.6730538, -3.4478344, 0, -76.083503,
-        19.9372656, -38.9938322, 0, 0, 36.0135034, 0, -56.8457144, 0, 0,
-        63.4198336, 0, 0, 0, 0, 0, -63.4419798, 28.5629988, 0, 0, 0, 0,
-        38.8001126, 0, 0, 47.2148438, 0, -19.256673, -24.8778354, -47.8193252,
-        0, 0, -38.7279908, 0, 0, 0, -34.5546658, 0, -96.7675822, 0, 0, 0, 0, 0,
-        0, 0, 0, 27.9437518
-    ],
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+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56.7568638, 0, 0, 66.7588344, 0, 0, 0,
+    99.284785, 0, 0, 0, 0, 0, 0, -99.2850048, -85.4466004
+], [
+    0, 0, -34.544278, 0, 0, -36.135124, -33.4259354, 0, 0, -60.4256326, 0,
+    44.7734168, 0, 0, 0, 0, -88.6744704, 0, 0, 0, 0, 0, 0, 0, 0, -95.3752508,
+    4.596855, 85.2924422, 70.9081648, 22.0390844, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    1.6872456, 0, 0, 0, 0, 0, 0, 0, -37.4782422, 0, 0, 0, 0, 0, 0, 0, 0,
+    4.7642096, 0, 0, -33.6313218, 0, 0, 72.977526, 0, 0, 0, 0, 0, 0, 2.5063252,
+    0, 0, 0, 0, 0, -64.8677902
+], [
+    0, 0, 0, 3.718989, 0, 0, 49.3814782, 77.868826, 0, 0, 0, 0, 0, 0, 0, 0,
+    -49.7008846, 0, 4.696494, 0, 0, 0, 0, 0, 0, 0, -59.4928602, 0, 0, 0, 0, 0,
+    82.6840644, -0.996624, -15.8014198, -93.098215, 0, 0, 41.2709314, 0,
+    -90.9633198, -56.911012, 0, 0, 0, 0, 0, 0, 0, 0, 0, -26.02585, 0, 0, 0, 0,
+    0, 0, 61.2353938, 0, 0, 0, 0, 21.376448, 0, 0, 0, 0, 0, 0, 0, 97.0578858, 0,
+    0, 0, 5.1265226
+], [
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -19.5643656, 0, 0,
+    -92.0307416, 0, 0, 0, 0, 55.003856, 0, 0, -20.4708104, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 54.4588824, 0, 0, 0, 0, 0, 0, 0, 0,
+    -57.0182252, 0, 0, -48.3222128, 0, 0, 0, -41.1121382, 0, 0, 0, 0, 0, 0,
+    -12.2773602, 0, -28.6924808, 50.0415338
+], [
+    0, 0, 0, -73.9719246, 0, 0, 0, 0, 0, 0, 0, 39.4110332, 0, 0, -21.6757132, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 74.8739354, 0, 0,
+    -52.9210426, 0, 46.7750368, 63.6311858, 0, 0, 0, 0, 25.5310902, 0,
+    -3.0369126, 0, 32.5437816, 0, 0, 78.9107496, 32.0416448, 0, 0, 15.9408846,
+    0, 0, 0, 0, -67.7582854, -52.6103086, 0, 0, 0, 0, 0, 0, 11.8676176, 0, 0,
+    -41.8820812, 43.608357, 0, 0, -64.3752572
+], [
+    0, -15.1648222, -57.5273074, -94.2919124, -3.0777222, -77.345826,
+    91.2777856, 0, 0, 0, 0, 13.5044774, 0, 0, 0, 0, 0, 0, 0, 0, 33.5509618, 0,
+    0, -33.291092, -60.3050638, 0, 0, 0, 94.610171, 0, 0, 0, 0, 0, 0,
+    -47.270154, -81.403122, 0, 0, 47.829269, 0, 0, 0, 0, -47.4699122, 0, 0,
+    -31.0161918, 0, 0, -54.993563, -9.6544012, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    -17.0644648, 0, 0, 0, 0, 0, 0, 49.0555938, -82.4686508, 0, 0, 97.7299292, 0,
+    0, -93.1718028
+], [
+    0, 0, 0, 45.5639954, 0, 0, 0, 0, 0, 92.3127826, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, -5.8746296, 22.9079182, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    25.2727812, 0, 0, 0, 0, 0, 0, 65.0012614, 0, 67.4376806, 0, 0, 14.0298002,
+    0, 0, 0, 0, 0, 33.432514, 43.000429, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20.2757068,
+    4.0567932, 0, 0, -15.8737758, 0, 0, 0, 0, 29.8957398, 50.0218946, 17.0262346
+], [
+    46.1658228, 19.630724, 0, 0, 0, 0, 0, 80.6073204, 24.5091878, 0, 0, 0,
+    73.9258848, 0, 0, 0, -98.2044476, 0, 0, 0, 0, -59.2213968, 0, -70.1450004,
+    0, 0, 0, 42.4721292, 0, -73.0346074, 0, 67.9829686, 0, 0, 0, 0, 6.6597886,
+    39.7215082, 0, 0, 0, 52.505292, 95.9204336, 0, 0, 5.8797776, 0, 0, 0,
+    36.0738246, 0, -15.2667614, 0, 0, 0, 0, 0, 0, 0, 0, 0, 50.963131, 0, 0, 0,
+    49.3354032, 13.7868492, 0, 77.3708062, 26.0888548, 0, 0, 0, 0, -60.0881692,
+    24.2459968
+], [
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56.562023, 0, 0, 24.9145996, 0, 0, 0, 0,
+    32.8196724, -75.8879774, -12.4005106, 0, 0, 0, 0, -18.824482, -45.0330046,
+    0, 0, -47.5493022, 0, 0, 0, 8.1879572, 0, 0, 4.5700222, -28.6731266, 0, 0,
+    0, 0, -83.2735948, 5.4624948, 0, 0, 0, 0, 0, 0, 0, 83.664375, -52.8630198,
+    58.5805764, 0, 0, 0, 45.5781336, 0, 0, -1.9768322, 0, 0, 0, 0, 91.1151128,
+    0, 0, 0, 0, 0, 0, -65.5158586, 0, -22.12762
+], [
+    0, 0, 0, 0, 0, -50.8301006, 0, -57.183698, -24.666489, 43.0557936, 0, 0, 0,
+    -44.0812116, 0, 0, 0, 97.1670056, 0, 0, 0, 0, 83.4511366, 0, -96.8912356,
+    -6.1095452, 87.6643268, 0, 0, 0, -32.5223614, 0, 0, 0, 0, -18.5596964, 0,
+    56.1790224, 0, 25.6035684, 0, 0, 0, 0, -46.8740154, 0, 0, 0, 0, 0, 0, 0,
+    -44.4329434, -34.719678, 85.6384822, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    48.9669952, 0, 0, 0, 0, 0, 0, 0, 0, 0, -28.2571694
+], [
+    0, 0, 0, 0, 42.1192158, 18.3800218, 0, 0, 33.4475094, 0, -39.4713432, 0,
+    54.9744572, 0, -6.6305664, 79.0252374, 77.6908818, -61.5099746, 0, 0,
+    38.4501814, 0, 0, -96.4250704, -27.809694, -34.9250884, 63.2997728, 0,
+    -98.8800042, 0, -7.682428, 0, 0, 0, 0, 0, 0, 7.7256608, 0, 48.5546152, 0, 0,
+    0, 0, 0, 0, -58.963373, 0, 0, 0, 0, 0, 86.5062664, 7.0006556, 0, 0, 0, 0, 0,
+    0, 3.1958572, 0, -46.2861844, 0, 0, 0, 0, 30.0710902, 46.6948898, 0,
+    68.4681908, 0, 0, 0, 0, -51.3930766
+], [
+    7.1969236, 0, -79.6160432, -39.947334, -15.2636342, 0, 10.0645898,
+    -57.8968238, -31.9945476, 0, 91.5839662, -52.777566, 0, 0, 0, 0,
+    -40.9252974, 0, 0, 0, -45.5113982, 70.585349, 0, 0, 0, 0, 0, 0, 0, 0,
+    -66.2523308, 0, 0, 0, 0, 0, 0, 0, 0, 85.7454324, -7.1148372, 0, 93.6584844,
+    0, 0, 0, 0, 0, 0, -8.0414788, 0, 0, 0, 0, 51.3263388, 0, 0, 79.892422, 0, 0,
+    -64.6934098, 0, 0, -45.9577622, 0, 0, 0, -46.8290526, 0, 48.2417506, 0, 0,
+    0, 0, -2.4115812, -50.7156922
+], [
+    62.9870494, -81.5584552, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 53.7333104, 0,
+    50.6438202, -57.7938262, 0, 0, 0, 0, -78.1108892, 0, 0, 0, 0, 0, 0, 0,
+    87.6584692, 0, 58.2960112, -17.5355398, 12.0497204, -60.5414862, 0, 0, 0, 0,
+    0, 0, 0, 0, 38.586472, -19.7940052, 94.423359, -89.557933, 0, 0,
+    -77.0715722, 0, -87.707166, 70.8585278, 0, 9.0616914, 91.333051, 0, 0, 0,
+    5.8166112, 0, 24.7793442, -51.000038, 0, 0, 0, 0, 0, 0, 79.9657776,
+    97.4969126, 0, 0, 0, 0, -73.8994394
+], [
+    10.133741, 0, 0, 0, 95.6910336, 0, 0, 0, 0, -22.6696236, 0, 0, 0, 0,
+    15.137996, 35.7088464, 0, -16.1971956, 0, -29.4834358, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, -89.868739, 24.0040126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    40.7292396, 35.7709458, 0, 34.690347, 22.3083532, 0, 0, 0, 0, 0,
+    -48.4224164, 0, 0, 0, 0, 91.7670744, 0, 0, 69.4045014, 0, 60.5937114,
+    -38.9993134, 0, 0, 0, 0, 0, 55.4599018, 0, 86.689944
+], [
+    0, 0, -24.842169, -52.997003, 0, 0, 0, 0, 0, 0, 0, 5.9075842, 0, 0,
+    -91.1447252, -5.3147106, 0, 0, 0, 4.4670454, 34.97343, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, -5.3957052, 0, 0, 0, 0, -19.2118838, 0, 0, 0, 0, 0, 0, 0, 0,
+    64.9559324, 0, 0, -10.0586402, 0, -74.8523334, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    84.8495464, 0, 0, 0, 0, 0, 0, 33.0825986, 46.995148, 0, 0, 0, -4.243203, 0,
+    0, -24.0124188
+]]
 
 
 class ObjectiveSolutionPrinter(cp_model.CpSolverSolutionCallback):
diff --git a/examples/python/random_tsp.py b/examples/python/random_tsp.py
index 46db637f18..02a5070722 100755
--- a/examples/python/random_tsp.py
+++ b/examples/python/random_tsp.py
@@ -80,8 +80,8 @@ class RandomMatrix(object):
                         distance_max)
 
     def Distance(self, manager, from_index, to_index):
-        return self.matrix[manager.IndexToNode(from_index)][
-            manager.IndexToNode(to_index)]
+        return self.matrix[manager.IndexToNode(from_index)][manager.IndexToNode(
+            to_index)]
 
 
 def main(args):
diff --git a/examples/python/rcpsp_sat.py b/examples/python/rcpsp_sat.py
index cc618b99c6..933935274a 100644
--- a/examples/python/rcpsp_sat.py
+++ b/examples/python/rcpsp_sat.py
@@ -81,8 +81,7 @@ def solve_rcpsp(problem, proto_file, params):
 
     horizon = problem.deadline if problem.deadline != -1 else problem.horizon
     if horizon == -1:  # Naive computation.
-        horizon = sum(
-            max(r.duration for r in t.recipes) for t in problem.tasks)
+        horizon = sum(max(r.duration for r in t.recipes) for t in problem.tasks)
         if problem.is_rcpsp_max:
             for t in problem.tasks:
                 for sd in t.successor_delays:
@@ -116,8 +115,7 @@ def solve_rcpsp(problem, proto_file, params):
         if len(task.recipes) == 1:
             # Create interval.
             recipe = task.recipes[0]
-            task_starts[t] = model.NewIntVar(0, horizon,
-                                             'start_of_task_%i' % t)
+            task_starts[t] = model.NewIntVar(0, horizon, 'start_of_task_%i' % t)
             task_ends[t] = model.NewIntVar(0, horizon, 'end_of_task_%i' % t)
             interval = model.NewIntervalVar(task_starts[t], recipe.duration,
                                             task_ends[t], 'interval_%i' % t)
@@ -173,8 +171,7 @@ def solve_rcpsp(problem, proto_file, params):
                         presences_per_resource[res].append(is_present)
 
             # Create the master interval for the task.
-            task_starts[t] = model.NewIntVar(0, horizon,
-                                             'start_of_task_%i' % t)
+            task_starts[t] = model.NewIntVar(0, horizon, 'start_of_task_%i' % t)
             task_ends[t] = model.NewIntVar(0, horizon, 'end_of_task_%i' % t)
             duration = model.NewIntVar(min_size, max_size,
                                        'duration_of_task_%i' % t)
@@ -187,8 +184,7 @@ def solve_rcpsp(problem, proto_file, params):
                 model.Add(
                     task_starts[t] == starts_per_task[t][r]).OnlyEnforceIf(p)
                 model.Add(task_ends[t] == ends_per_task[t][r]).OnlyEnforceIf(p)
-                model.Add(
-                    duration == task.recipes[r].duration).OnlyEnforceIf(p)
+                model.Add(duration == task.recipes[r].duration).OnlyEnforceIf(p)
             model.Add(sum(presences_per_task[t]) == 1)
 
     # Create makespan variable
@@ -261,9 +257,8 @@ 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
 
diff --git a/examples/python/shift_scheduling_sat.py b/examples/python/shift_scheduling_sat.py
index 25f269a78f..840a7a6c9c 100644
--- a/examples/python/shift_scheduling_sat.py
+++ b/examples/python/shift_scheduling_sat.py
@@ -308,8 +308,8 @@ 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)
 
@@ -367,9 +367,9 @@ 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)
diff --git 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
index dba2d55ebc..f6306c3b84 100644
--- 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
@@ -198,8 +198,7 @@ def main(args):
         name_suffix = '_%i' % job_id
         start = model.NewIntVar(release_date, due_date, 's' + name_suffix)
         end = model.NewIntVar(release_date, due_date, 'e' + name_suffix)
-        interval = model.NewIntervalVar(start, duration, end,
-                                        'i' + name_suffix)
+        interval = model.NewIntervalVar(start, duration, end, 'i' + name_suffix)
         starts.append(start)
         ends.append(end)
         intervals.append(interval)
@@ -235,8 +234,8 @@ def main(args):
                 model.Add(starts[j] == ends[i] +
                           setup_times[i + 1][j]).OnlyEnforceIf(lit)
             else:
-                model.Add(starts[j] >= ends[i] +
-                          setup_times[i + 1][j]).OnlyEnforceIf(lit)
+                model.Add(starts[j] >=
+                          ends[i] + setup_times[i + 1][j]).OnlyEnforceIf(lit)
 
     model.AddCircuit(arcs)
 
@@ -269,9 +268,9 @@ def main(args):
     solver.SolveWithSolutionCallback(model, solution_printer)
     print(solver.ResponseStats())
     for job_id in all_jobs:
-        print(
-            'job %i starts at %i end ends at %i' %
-            (job_id, solver.Value(starts[job_id]), solver.Value(ends[job_id])))
+        print('job %i starts at %i end ends at %i' %
+              (job_id, solver.Value(starts[job_id]),
+               solver.Value(ends[job_id])))
 
 
 if __name__ == '__main__':
diff --git a/examples/python/steel_mill_slab_sat.py b/examples/python/steel_mill_slab_sat.py
index 3d36cc1b17..14a9d03792 100644
--- a/examples/python/steel_mill_slab_sat.py
+++ b/examples/python/steel_mill_slab_sat.py
@@ -347,14 +347,12 @@ def steel_mill_slab(problem, break_symmetries, output_proto):
         for s in all_slabs
     ]
     color_is_in_slab = [[
-        model.NewBoolVar('color_%i_in_slab_%i' % (c + 1, s))
-        for c in all_colors
+        model.NewBoolVar('color_%i_in_slab_%i' % (c + 1, s)) for c in all_colors
     ] for s in all_slabs]
 
     # Compute load of all slabs.
     for s in all_slabs:
-        model.Add(
-            sum(assign[o][s] * widths[o] for o in all_orders) == loads[s])
+        model.Add(sum(assign[o][s] * widths[o] for o in all_orders) == loads[s])
 
     # Orders are assigned to one slab.
     for o in all_orders:
@@ -444,8 +442,9 @@ def steel_mill_slab(problem, break_symmetries, output_proto):
 
     ### Output the solution.
     if status == cp_model.OPTIMAL:
-        print('Loss = %i, time = %f s, %i conflicts' % (
-            solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))
+        print('Loss = %i, time = %f s, %i conflicts' %
+              (solver.ObjectiveValue(), solver.WallTime(),
+               solver.NumConflicts()))
     else:
         print('No solution')
 
@@ -477,9 +476,7 @@ def collect_valid_slabs(capacities, colors, widths, loss_array, all_colors):
     assign = [model.NewBoolVar('assign_%i' % o) for o in all_orders]
     load = model.NewIntVar(0, max_capacity, 'load')
 
-    color_in_slab = [
-        model.NewBoolVar('color_%i' % (c + 1)) for c in all_colors
-    ]
+    color_in_slab = [model.NewBoolVar('color_%i' % (c + 1)) for c in all_colors]
 
     # Compute load.
     model.Add(sum(assign[o] * widths[o] for o in all_orders) == load)
@@ -538,9 +535,7 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
     assign = [[
         model.NewBoolVar('assign_%i_to_slab_%i' % (o, s)) for s in all_slabs
     ] for o in all_orders]
-    loads = [
-        model.NewIntVar(0, max_capacity, 'load_%i' % s) for s in all_slabs
-    ]
+    loads = [model.NewIntVar(0, max_capacity, 'load_%i' % s) for s in all_slabs]
     losses = [model.NewIntVar(0, max_loss, 'loss_%i' % s) for s in all_slabs]
 
     unsorted_valid_slabs = collect_valid_slabs(capacities, colors, widths,
@@ -553,8 +548,8 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
 
     for s in all_slabs:
         model.AddAllowedAssignments(
-            [assign[o][s] for o in all_orders] + [losses[s], loads[s]],
-            valid_slabs)
+            [assign[o][s]
+             for o in all_orders] + [losses[s], loads[s]], valid_slabs)
 
     # Orders are assigned to one slab.
     for o in all_orders:
@@ -595,8 +590,7 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
                 for w, os in local_width_to_order.items():
                     if len(os) > 1:
                         for p in range(len(os) - 1):
-                            ordered_equivalent_orders.append((os[p],
-                                                              os[p + 1]))
+                            ordered_equivalent_orders.append((os[p], os[p + 1]))
         for w, os in width_to_unique_color_order.items():
             if len(os) > 1:
                 for p in range(len(os) - 1):
@@ -640,8 +634,9 @@ def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
 
     ### Output the solution.
     if status == cp_model.OPTIMAL:
-        print('Loss = %i, time = %f s, %i conflicts' % (
-            solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))
+        print('Loss = %i, time = %f s, %i conflicts' %
+              (solver.ObjectiveValue(), solver.WallTime(),
+               solver.NumConflicts()))
     else:
         print('No solution')
 
@@ -685,8 +680,7 @@ def steel_mill_slab_with_column_generation(problem, output_proto):
 
     for o in all_orders:
         model.Add(
-            sum(selected[i] for i in all_valid_slabs
-                if valid_slabs[i][o]) == 1)
+            sum(selected[i] for i in all_valid_slabs if valid_slabs[i][o]) == 1)
 
     # Redundant constraint (sum of loads == sum of widths).
     model.Add(
@@ -696,8 +690,8 @@ def steel_mill_slab_with_column_generation(problem, output_proto):
     # Objective.
     max_loss = max(valid_slabs[i][-2] for i in all_valid_slabs)
     obj = model.NewIntVar(0, num_slabs * max_loss, 'obj')
-    model.Add(obj == sum(
-        selected[i] * valid_slabs[i][-2] for i in all_valid_slabs))
+    model.Add(
+        obj == sum(selected[i] * valid_slabs[i][-2] for i in all_valid_slabs))
     model.Minimize(obj)
 
     print('Model created')
@@ -715,8 +709,9 @@ def steel_mill_slab_with_column_generation(problem, output_proto):
 
     ### Output the solution.
     if status == cp_model.OPTIMAL:
-        print('Loss = %i, time = %f s, %i conflicts' % (
-            solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))
+        print('Loss = %i, time = %f s, %i conflicts' %
+              (solver.ObjectiveValue(), solver.WallTime(),
+               solver.NumConflicts()))
     else:
         print('No solution')
 
diff --git a/examples/python/stigler_diet.py b/examples/python/stigler_diet.py
index eae14d96a3..99bf2ddd9e 100644
--- a/examples/python/stigler_diet.py
+++ b/examples/python/stigler_diet.py
@@ -22,203 +22,156 @@ from ortools.linear_solver import pywraplp
 def main():
     """Entry point of the program"""
     # Nutrient minimums.
-    nutrients = [['Calories (kcal)', 3], ['Protein (g)', 70],
-                 ['Calcium (g)', 0.8], ['Iron (mg)', 12],
-                 ['Vitamin A (KIU)', 5], ['Vitamin B1 (mg)', 1.8],
-                 ['Vitamin B2 (mg)', 2.7], ['Niacin (mg)', 18],
-                 ['Vitamin C (mg)', 75]]
+    nutrients = [['Calories (kcal)', 3], ['Protein (g)', 70], [
+        'Calcium (g)', 0.8
+    ], ['Iron (mg)', 12], ['Vitamin A (KIU)', 5], ['Vitamin B1 (mg)', 1.8],
+                 ['Vitamin B2 (mg)', 2.7], ['Niacin (mg)',
+                                            18], ['Vitamin C (mg)', 75]]
 
     # Commodity, Unit, 1939 price (cents), Calories (kcal), Protein (g), Calcium (g), Iron (mg),
     # Vitamin A (KIU), Vitamin B1 (mg), Vitamin B2 (mg), Niacin (mg), Vitamin C (mg)
-    data = [
-        [
-            'Wheat Flour (Enriched)', '10 lb.', 36, 44.7, 1411, 2, 365, 0,
-            55.4, 33.3, 441, 0
-        ], ['Macaroni', '1 lb.', 14.1, 11.6, 418, 0.7, 54, 0, 3.2, 1.9, 68, 0],
-        [
-            'Wheat Cereal (Enriched)', '28 oz.', 24.2, 11.8, 377, 14.4, 175, 0,
-            14.4, 8.8, 114, 0
-        ],
-        ['Corn Flakes', '8 oz.', 7.1, 11.4, 252, 0.1, 56, 0, 13.5, 2.3, 68, 0],
-        [
-            'Corn Meal', '1 lb.', 4.6, 36.0, 897, 1.7, 99, 30.9, 17.4, 7.9,
-            106, 0
-        ],
-        [
-            'Hominy Grits', '24 oz.', 8.5, 28.6, 680, 0.8, 80, 0, 10.6, 1.6,
-            110, 0
-        ], ['Rice', '1 lb.', 7.5, 21.2, 460, 0.6, 41, 0, 2, 4.8, 60, 0],
-        [
-            'Rolled Oats', '1 lb.', 7.1, 25.3, 907, 5.1, 341, 0, 37.1, 8.9, 64,
-            0
-        ],
-        [
-            'White Bread (Enriched)', '1 lb.', 7.9, 15.0, 488, 2.5, 115, 0,
-            13.8, 8.5, 126, 0
-        ],
-        [
-            'Whole Wheat Bread', '1 lb.', 9.1, 12.2, 484, 2.7, 125, 0, 13.9,
-            6.4, 160, 0
-        ], ['Rye Bread', '1 lb.', 9.1, 12.4, 439, 1.1, 82, 0, 9.9, 3, 66, 0],
-        ['Pound Cake', '1 lb.', 24.8, 8.0, 130, 0.4, 31, 18.9, 2.8, 3, 17, 0],
-        ['Soda Crackers', '1 lb.', 15.1, 12.5, 288, 0.5, 50, 0, 0, 0, 0, 0],
-        ['Milk', '1 qt.', 11, 6.1, 310, 10.5, 18, 16.8, 4, 16, 7, 177],
-        [
-            'Evaporated Milk (can)', '14.5 oz.', 6.7, 8.4, 422, 15.1, 9, 26, 3,
-            23.5, 11, 60
-        ], ['Butter', '1 lb.', 30.8, 10.8, 9, 0.2, 3, 44.2, 0, 0.2, 2, 0],
-        ['Oleomargarine', '1 lb.', 16.1, 20.6, 17, 0.6, 6, 55.8, 0.2, 0, 0, 0],
-        ['Eggs', '1 doz.', 32.6, 2.9, 238, 1.0, 52, 18.6, 2.8, 6.5, 1, 0],
-        [
-            'Cheese (Cheddar)', '1 lb.', 24.2, 7.4, 448, 16.4, 19, 28.1, 0.8,
-            10.3, 4, 0
-        ], ['Cream', '1/2 pt.', 14.1, 3.5, 49, 1.7, 3, 16.9, 0.6, 2.5, 0, 17],
-        [
-            'Peanut Butter', '1 lb.', 17.9, 15.7, 661, 1.0, 48, 0, 9.6, 8.1,
-            471, 0
-        ],
-        ['Mayonnaise', '1/2 pt.', 16.7, 8.6, 18, 0.2, 8, 2.7, 0.4, 0.5, 0, 0],
-        ['Crisco', '1 lb.', 20.3, 20.1, 0, 0, 0, 0, 0, 0, 0, 0],
-        ['Lard', '1 lb.', 9.8, 41.7, 0, 0, 0, 0.2, 0, 0.5, 5, 0],
-        [
-            'Sirloin Steak', '1 lb.', 39.6, 2.9, 166, 0.1, 34, 0.2, 2.1, 2.9,
-            69, 0
-        ],
-        [
-            'Round Steak', '1 lb.', 36.4, 2.2, 214, 0.1, 32, 0.4, 2.5, 2.4, 87,
-            0
-        ], ['Rib Roast', '1 lb.', 29.2, 3.4, 213, 0.1, 33, 0, 0, 2, 0, 0],
-        ['Chuck Roast', '1 lb.', 22.6, 3.6, 309, 0.2, 46, 0.4, 1, 4, 120, 0],
-        ['Plate', '1 lb.', 14.6, 8.5, 404, 0.2, 62, 0, 0.9, 0, 0, 0],
-        [
-            'Liver (Beef)', '1 lb.', 26.8, 2.2, 333, 0.2, 139, 169.2, 6.4,
-            50.8, 316, 525
-        ],
-        ['Leg of Lamb', '1 lb.', 27.6, 3.1, 245, 0.1, 20, 0, 2.8, 3.9, 86, 0],
-        [
-            'Lamb Chops (Rib)', '1 lb.', 36.6, 3.3, 140, 0.1, 15, 0, 1.7, 2.7,
-            54, 0
-        ], [
-            'Pork Chops', '1 lb.', 30.7, 3.5, 196, 0.2, 30, 0, 17.4, 2.7, 60, 0
-        ],
-        [
-            'Pork Loin Roast', '1 lb.', 24.2, 4.4, 249, 0.3, 37, 0, 18.2, 3.6,
-            79, 0
-        ], ['Bacon', '1 lb.', 25.6, 10.4, 152, 0.2, 23, 0, 1.8, 1.8, 71, 0],
-        ['Ham, smoked', '1 lb.', 27.4, 6.7, 212, 0.2, 31, 0, 9.9, 3.3, 50, 0],
-        ['Salt Pork', '1 lb.', 16, 18.8, 164, 0.1, 26, 0, 1.4, 1.8, 0, 0],
-        [
-            'Roasting Chicken', '1 lb.', 30.3, 1.8, 184, 0.1, 30, 0.1, 0.9,
-            1.8, 68, 46
-        ],
-        ['Veal Cutlets', '1 lb.', 42.3, 1.7, 156, 0.1, 24, 0, 1.4, 2.4, 57, 0],
-        [
-            'Salmon, Pink (can)', '16 oz.', 13, 5.8, 705, 6.8, 45, 3.5, 1, 4.9,
-            209, 0
-        ], ['Apples', '1 lb.', 4.4, 5.8, 27, 0.5, 36, 7.3, 3.6, 2.7, 5, 544],
-        ['Bananas', '1 lb.', 6.1, 4.9, 60, 0.4, 30, 17.4, 2.5, 3.5, 28, 498],
-        ['Lemons', '1 doz.', 26, 1.0, 21, 0.5, 14, 0, 0.5, 0, 4, 952],
-        [
-            'Oranges', '1 doz.', 30.9, 2.2, 40, 1.1, 18, 11.1, 3.6, 1.3, 10,
-            1998
-        ],
-        [
-            'Green Beans', '1 lb.', 7.1, 2.4, 138, 3.7, 80, 69, 4.3, 5.8, 37,
-            862
-        ], ['Cabbage', '1 lb.', 3.7, 2.6, 125, 4.0, 36, 7.2, 9, 4.5, 26, 5369],
-        [
-            'Carrots', '1 bunch', 4.7, 2.7, 73, 2.8, 43, 188.5, 6.1, 4.3, 89,
-            608
-        ], ['Celery', '1 stalk', 7.3, 0.9, 51, 3.0, 23, 0.9, 1.4, 1.4, 9, 313],
-        ['Lettuce', '1 head', 8.2, 0.4, 27, 1.1, 22, 112.4, 1.8, 3.4, 11, 449],
-        ['Onions', '1 lb.', 3.6, 5.8, 166, 3.8, 59, 16.6, 4.7, 5.9, 21, 1184],
-        [
-            'Potatoes', '15 lb.', 34, 14.3, 336, 1.8, 118, 6.7, 29.4, 7.1, 198,
-            2522
-        ],
-        [
-            'Spinach', '1 lb.', 8.1, 1.1, 106, 0, 138, 918.4, 5.7, 13.8, 33,
-            2755
-        ],
-        [
-            'Sweet Potatoes', '1 lb.', 5.1, 9.6, 138, 2.7, 54, 290.7, 8.4, 5.4,
-            83, 1912
-        ],
-        [
-            'Peaches (can)', 'No. 2 1/2', 16.8, 3.7, 20, 0.4, 10, 21.5, 0.5, 1,
-            31, 196
-        ],
-        [
-            'Pears (can)', 'No. 2 1/2', 20.4, 3.0, 8, 0.3, 8, 0.8, 0.8, 0.8, 5,
-            81
-        ],
-        [
-            'Pineapple (can)', 'No. 2 1/2', 21.3, 2.4, 16, 0.4, 8, 2, 2.8, 0.8,
-            7, 399
-        ],
-        [
-            'Asparagus (can)', 'No. 2', 27.7, 0.4, 33, 0.3, 12, 16.3, 1.4, 2.1,
-            17, 272
-        ],
-        [
-            'Green Beans (can)', 'No. 2', 10, 1.0, 54, 2, 65, 53.9, 1.6, 4.3,
-            32, 431
-        ],
-        [
-            'Pork and Beans (can)', '16 oz.', 7.1, 7.5, 364, 4, 134, 3.5, 8.3,
-            7.7, 56, 0
-        ],
-        [
-            'Corn (can)', 'No. 2', 10.4, 5.2, 136, 0.2, 16, 12, 1.6, 2.7, 42,
-            218
-        ],
-        [
-            'Peas (can)', 'No. 2', 13.8, 2.3, 136, 0.6, 45, 34.9, 4.9, 2.5, 37,
-            370
-        ],
-        [
-            'Tomatoes (can)', 'No. 2', 8.6, 1.3, 63, 0.7, 38, 53.2, 3.4, 2.5,
-            36, 1253
-        ],
-        [
-            'Tomato Soup (can)', '10 1/2 oz.', 7.6, 1.6, 71, 0.6, 43, 57.9,
-            3.5, 2.4, 67, 862
-        ],
-        [
-            'Peaches, Dried', '1 lb.', 15.7, 8.5, 87, 1.7, 173, 86.8, 1.2, 4.3,
-            55, 57
-        ],
-        [
-            'Prunes, Dried', '1 lb.', 9, 12.8, 99, 2.5, 154, 85.7, 3.9, 4.3,
-            65, 257
-        ],
-        [
-            'Raisins, Dried', '15 oz.', 9.4, 13.5, 104, 2.5, 136, 4.5, 6.3,
-            1.4, 24, 136
-        ],
-        [
-            'Peas, Dried', '1 lb.', 7.9, 20.0, 1367, 4.2, 345, 2.9, 28.7, 18.4,
-            162, 0
-        ],
-        [
-            'Lima Beans, Dried', '1 lb.', 8.9, 17.4, 1055, 3.7, 459, 5.1, 26.9,
-            38.2, 93, 0
-        ],
-        [
-            'Navy Beans, Dried', '1 lb.', 5.9, 26.9, 1691, 11.4, 792, 0, 38.4,
-            24.6, 217, 0
-        ], ['Coffee', '1 lb.', 22.4, 0, 0, 0, 0, 0, 4, 5.1, 50, 0],
-        ['Tea', '1/4 lb.', 17.4, 0, 0, 0, 0, 0, 0, 2.3, 42, 0],
-        ['Cocoa', '8 oz.', 8.6, 8.7, 237, 3, 72, 0, 2, 11.9, 40, 0],
-        ['Chocolate', '8 oz.', 16.2, 8.0, 77, 1.3, 39, 0, 0.9, 3.4, 14, 0],
-        ['Sugar', '10 lb.', 51.7, 34.9, 0, 0, 0, 0, 0, 0, 0, 0],
-        ['Corn Syrup', '24 oz.', 13.7, 14.7, 0, 0.5, 74, 0, 0, 0, 5, 0],
-        ['Molasses', '18 oz.', 13.6, 9.0, 0, 10.3, 244, 0, 1.9, 7.5, 146, 0],
-        [
-            'Strawberry Preserves', '1 lb.', 20.5, 6.4, 11, 0.4, 7, 0.2, 0.2,
-            0.4, 3, 0
-        ]
-    ]
+    data = [[
+        'Wheat Flour (Enriched)', '10 lb.', 36, 44.7, 1411, 2, 365, 0, 55.4,
+        33.3, 441, 0
+    ], ['Macaroni', '1 lb.', 14.1, 11.6, 418, 0.7, 54, 0, 3.2, 1.9, 68, 0], [
+        'Wheat Cereal (Enriched)', '28 oz.', 24.2, 11.8, 377, 14.4, 175, 0,
+        14.4, 8.8, 114, 0
+    ], ['Corn Flakes', '8 oz.', 7.1, 11.4, 252, 0.1, 56, 0, 13.5, 2.3, 68, 0], [
+        'Corn Meal', '1 lb.', 4.6, 36.0, 897, 1.7, 99, 30.9, 17.4, 7.9, 106, 0
+    ], [
+        'Hominy Grits', '24 oz.', 8.5, 28.6, 680, 0.8, 80, 0, 10.6, 1.6, 110, 0
+    ], ['Rice', '1 lb.', 7.5, 21.2, 460, 0.6, 41, 0, 2, 4.8, 60, 0], [
+        'Rolled Oats', '1 lb.', 7.1, 25.3, 907, 5.1, 341, 0, 37.1, 8.9, 64, 0
+    ], [
+        'White Bread (Enriched)', '1 lb.', 7.9, 15.0, 488, 2.5, 115, 0, 13.8,
+        8.5, 126, 0
+    ], [
+        'Whole Wheat Bread', '1 lb.', 9.1, 12.2, 484, 2.7, 125, 0, 13.9, 6.4,
+        160, 0
+    ], ['Rye Bread', '1 lb.', 9.1, 12.4, 439, 1.1, 82, 0, 9.9, 3, 66, 0], [
+        'Pound Cake', '1 lb.', 24.8, 8.0, 130, 0.4, 31, 18.9, 2.8, 3, 17, 0
+    ], ['Soda Crackers', '1 lb.', 15.1, 12.5, 288, 0.5, 50, 0, 0, 0, 0, 0], [
+        'Milk', '1 qt.', 11, 6.1, 310, 10.5, 18, 16.8, 4, 16, 7, 177
+    ], [
+        'Evaporated Milk (can)', '14.5 oz.', 6.7, 8.4, 422, 15.1, 9, 26, 3,
+        23.5, 11, 60
+    ], ['Butter', '1 lb.', 30.8, 10.8, 9, 0.2, 3, 44.2, 0, 0.2, 2, 0], [
+        'Oleomargarine', '1 lb.', 16.1, 20.6, 17, 0.6, 6, 55.8, 0.2, 0, 0, 0
+    ], ['Eggs', '1 doz.', 32.6, 2.9, 238, 1.0, 52, 18.6, 2.8, 6.5, 1, 0], [
+        'Cheese (Cheddar)', '1 lb.', 24.2, 7.4, 448, 16.4, 19, 28.1, 0.8, 10.3,
+        4, 0
+    ], ['Cream', '1/2 pt.', 14.1, 3.5, 49, 1.7, 3, 16.9, 0.6, 2.5, 0, 17], [
+        'Peanut Butter', '1 lb.', 17.9, 15.7, 661, 1.0, 48, 0, 9.6, 8.1, 471, 0
+    ], ['Mayonnaise', '1/2 pt.', 16.7, 8.6, 18, 0.2, 8, 2.7, 0.4, 0.5, 0, 0], [
+        'Crisco', '1 lb.', 20.3, 20.1, 0, 0, 0, 0, 0, 0, 0, 0
+    ], ['Lard', '1 lb.', 9.8, 41.7, 0, 0, 0, 0.2, 0, 0.5, 5, 0], [
+        'Sirloin Steak', '1 lb.', 39.6, 2.9, 166, 0.1, 34, 0.2, 2.1, 2.9, 69, 0
+    ], ['Round Steak', '1 lb.', 36.4, 2.2, 214, 0.1, 32, 0.4, 2.5, 2.4, 87, 0
+       ], ['Rib Roast', '1 lb.', 29.2, 3.4, 213, 0.1, 33, 0, 0, 2, 0, 0], [
+           'Chuck Roast', '1 lb.', 22.6, 3.6, 309, 0.2, 46, 0.4, 1, 4, 120, 0
+       ], ['Plate', '1 lb.', 14.6, 8.5, 404, 0.2, 62, 0, 0.9, 0, 0, 0], [
+           'Liver (Beef)', '1 lb.', 26.8, 2.2, 333, 0.2, 139, 169.2, 6.4, 50.8,
+           316, 525
+       ], [
+           'Leg of Lamb', '1 lb.', 27.6, 3.1, 245, 0.1, 20, 0, 2.8, 3.9, 86, 0
+       ], [
+           'Lamb Chops (Rib)',
+           '1 lb.', 36.6, 3.3, 140, 0.1, 15, 0, 1.7, 2.7, 54, 0
+       ], [
+           'Pork Chops', '1 lb.', 30.7, 3.5, 196, 0.2, 30, 0, 17.4, 2.7, 60, 0
+       ], [
+           'Pork Loin Roast',
+           '1 lb.', 24.2, 4.4, 249, 0.3, 37, 0, 18.2, 3.6, 79, 0
+       ], ['Bacon', '1 lb.', 25.6, 10.4, 152, 0.2, 23, 0, 1.8, 1.8, 71, 0], [
+           'Ham, smoked', '1 lb.', 27.4, 6.7, 212, 0.2, 31, 0, 9.9, 3.3, 50, 0
+       ], ['Salt Pork', '1 lb.', 16, 18.8, 164, 0.1, 26, 0, 1.4, 1.8, 0, 0], [
+           'Roasting Chicken', '1 lb.', 30.3, 1.8, 184, 0.1, 30, 0.1, 0.9, 1.8,
+           68, 46
+       ], [
+           'Veal Cutlets', '1 lb.', 42.3, 1.7, 156, 0.1, 24, 0, 1.4, 2.4, 57, 0
+       ], [
+           'Salmon, Pink (can)', '16 oz.', 13, 5.8, 705, 6.8, 45, 3.5,
+           1, 4.9, 209, 0
+       ], ['Apples', '1 lb.', 4.4, 5.8, 27, 0.5, 36, 7.3, 3.6, 2.7, 5, 544], [
+           'Bananas', '1 lb.', 6.1, 4.9, 60, 0.4, 30, 17.4, 2.5, 3.5, 28, 498
+       ], ['Lemons', '1 doz.', 26, 1.0, 21, 0.5, 14, 0, 0.5, 0, 4, 952], [
+           'Oranges', '1 doz.', 30.9, 2.2, 40, 1.1, 18, 11.1, 3.6, 1.3, 10, 1998
+       ], [
+           'Green Beans', '1 lb.', 7.1, 2.4, 138, 3.7, 80, 69, 4.3, 5.8, 37, 862
+       ], ['Cabbage', '1 lb.', 3.7, 2.6, 125, 4.0, 36, 7.2, 9, 4.5, 26, 5369], [
+           'Carrots', '1 bunch', 4.7, 2.7, 73, 2.8, 43, 188.5, 6.1, 4.3, 89, 608
+       ], ['Celery', '1 stalk', 7.3, 0.9, 51, 3.0, 23, 0.9, 1.4, 1.4, 9, 313], [
+           'Lettuce', '1 head', 8.2, 0.4, 27, 1.1, 22, 112.4, 1.8, 3.4, 11, 449
+       ], ['Onions', '1 lb.', 3.6, 5.8, 166, 3.8, 59, 16.6, 4.7, 5.9, 21,
+           1184], [
+               'Potatoes', '15 lb.', 34, 14.3, 336, 1.8, 118, 6.7, 29.4, 7.1,
+               198, 2522
+           ], [
+               'Spinach', '1 lb.', 8.1, 1.1, 106, 0, 138, 918.4, 5.7, 13.8, 33,
+               2755
+           ], [
+               'Sweet Potatoes', '1 lb.', 5.1, 9.6, 138, 2.7, 54, 290.7, 8.4,
+               5.4, 83, 1912
+           ], [
+               'Peaches (can)', 'No. 2 1/2', 16.8, 3.7, 20, 0.4, 10, 21.5, 0.5,
+               1, 31, 196
+           ], [
+               'Pears (can)', 'No. 2 1/2', 20.4, 3.0, 8, 0.3, 8, 0.8, 0.8, 0.8,
+               5, 81
+           ], [
+               'Pineapple (can)', 'No. 2 1/2', 21.3, 2.4, 16, 0.4, 8, 2, 2.8,
+               0.8, 7, 399
+           ], [
+               'Asparagus (can)', 'No. 2', 27.7, 0.4, 33, 0.3, 12, 16.3, 1.4,
+               2.1, 17, 272
+           ], [
+               'Green Beans (can)', 'No. 2', 10, 1.0, 54, 2, 65, 53.9, 1.6, 4.3,
+               32, 431
+           ], [
+               'Pork and Beans (can)', '16 oz.', 7.1, 7.5, 364, 4, 134, 3.5,
+               8.3, 7.7, 56, 0
+           ], [
+               'Corn (can)', 'No. 2', 10.4, 5.2, 136, 0.2, 16, 12, 1.6, 2.7, 42,
+               218
+           ], [
+               'Peas (can)', 'No. 2', 13.8, 2.3, 136, 0.6, 45, 34.9, 4.9, 2.5,
+               37, 370
+           ], [
+               'Tomatoes (can)', 'No. 2', 8.6, 1.3, 63, 0.7, 38, 53.2, 3.4, 2.5,
+               36, 1253
+           ], [
+               'Tomato Soup (can)', '10 1/2 oz.', 7.6, 1.6, 71, 0.6, 43, 57.9,
+               3.5, 2.4, 67, 862
+           ], [
+               'Peaches, Dried', '1 lb.', 15.7, 8.5, 87, 1.7, 173, 86.8, 1.2,
+               4.3, 55, 57
+           ], [
+               'Prunes, Dried', '1 lb.', 9, 12.8, 99, 2.5, 154, 85.7, 3.9, 4.3,
+               65, 257
+           ], [
+               'Raisins, Dried', '15 oz.', 9.4, 13.5, 104, 2.5, 136, 4.5, 6.3,
+               1.4, 24, 136
+           ], [
+               'Peas, Dried', '1 lb.', 7.9, 20.0, 1367, 4.2, 345, 2.9, 28.7,
+               18.4, 162, 0
+           ], [
+               'Lima Beans, Dried', '1 lb.', 8.9, 17.4, 1055, 3.7, 459, 5.1,
+               26.9, 38.2, 93, 0
+           ], [
+               'Navy Beans, Dried', '1 lb.', 5.9, 26.9, 1691, 11.4, 792, 0,
+               38.4, 24.6, 217, 0
+           ], ['Coffee', '1 lb.', 22.4, 0, 0, 0, 0, 0, 4, 5.1, 50,
+               0], ['Tea', '1/4 lb.', 17.4, 0, 0, 0, 0, 0, 0, 2.3, 42, 0],
+            ['Cocoa', '8 oz.', 8.6, 8.7, 237, 3, 72, 0, 2, 11.9, 40, 0], [
+                'Chocolate', '8 oz.', 16.2, 8.0, 77, 1.3, 39, 0, 0.9, 3.4, 14, 0
+            ], ['Sugar', '10 lb.', 51.7, 34.9, 0, 0, 0, 0, 0, 0, 0, 0],
+            ['Corn Syrup', '24 oz.', 13.7, 14.7, 0, 0.5, 74, 0, 0, 0, 5, 0], [
+                'Molasses', '18 oz.', 13.6, 9.0, 0, 10.3, 244, 0, 1.9, 7.5, 146,
+                0
+            ], [
+                'Strawberry Preserves', '1 lb.', 20.5, 6.4, 11, 0.4, 7, 0.2,
+                0.2, 0.4, 3, 0
+            ]]
 
     # Instantiate a Glop solver, naming it LinearExample.
     solver = pywraplp.Solver('StiglerDietExample',
diff --git a/examples/python/sudoku_sat.py b/examples/python/sudoku_sat.py
index c1dd516548..642ab0226e 100644
--- a/examples/python/sudoku_sat.py
+++ b/examples/python/sudoku_sat.py
@@ -28,10 +28,10 @@ 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:
diff --git a/examples/python/task_allocation_sat.py b/examples/python/task_allocation_sat.py
index c3987e10b9..45b72156d3 100644
--- a/examples/python/task_allocation_sat.py
+++ b/examples/python/task_allocation_sat.py
@@ -27,252 +27,203 @@ def main():
         0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0
-    ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
-                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
-                     1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0
-                 ],
-                 [
-                     0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
-                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
-                     1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
-                     1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
-                     1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
-                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
-                     1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ],
-                 [
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
-                     1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-                 ]]
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
+        1, 1
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
+        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
+        1, 0
+    ], [
+        0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
+        1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
+        1, 1
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
+        1, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0
+    ]]
 
     ntasks = len(available)
     nslots = len(available[0])
diff --git a/examples/python/tasks_and_workers_assignment_sat.py b/examples/python/tasks_and_workers_assignment_sat.py
index 74b985054e..b8687498ae 100644
--- a/examples/python/tasks_and_workers_assignment_sat.py
+++ b/examples/python/tasks_and_workers_assignment_sat.py
@@ -78,8 +78,7 @@ def tasks_and_workers_assignment_sat():
         model.Add(n == sum(x[i, j] for i in all_workers))
         c = model.NewIntVar(0, sum_of_costs * scaling,
                             'sum_of_costs_of_group_%i' % j)
-        model.Add(c == sum(
-            y[k, j] * task_cost[k] * scaling for k in all_tasks))
+        model.Add(c == sum(y[k, j] * task_cost[k] * scaling for k in all_tasks))
         a = model.NewIntVar(0, sum_of_costs * scaling,
                             'average_cost_of_group_%i' % j)
         model.AddDivisionEquality(a, c, n)
diff --git a/examples/python/transit_time.py b/examples/python/transit_time.py
index daa7460627..1cdd8df631 100755
--- a/examples/python/transit_time.py
+++ b/examples/python/transit_time.py
@@ -147,8 +147,8 @@ class DataProblem():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 class CreateTimeEvaluator(object):
@@ -165,9 +165,8 @@ class CreateTimeEvaluator(object):
         if from_node == to_node:
             travel_time = 0
         else:
-            travel_time = manhattan_distance(
-                data.locations[from_node],
-                data.locations[to_node]) / data.vehicle.speed
+            travel_time = manhattan_distance(data.locations[
+                from_node], data.locations[to_node]) / data.vehicle.speed
         return travel_time
 
     def __init__(self, data):
@@ -181,8 +180,8 @@ class CreateTimeEvaluator(object):
                     self._total_time[from_node][to_node] = 0
                 else:
                     self._total_time[from_node][to_node] = int(
-                        self.service_time(data, from_node) +
-                        self.travel_time(data, from_node, to_node))
+                        self.service_time(data, from_node) + self.travel_time(
+                            data, from_node, to_node))
 
     def time_evaluator(self, from_node, to_node):
         """Returns the total time between the two nodes"""
@@ -209,8 +208,7 @@ def main():
     # Print Transit Time
     time_evaluator = CreateTimeEvaluator(data).time_evaluator
     print('Route 0:')
-    print_transit_time([[0, 5], [5, 8], [8, 6], [6, 2], [2, 0]],
-                       time_evaluator)
+    print_transit_time([[0, 5], [5, 8], [8, 6], [6, 2], [2, 0]], time_evaluator)
 
     print('Route 1:')
     print_transit_time([[0, 9], [9, 14], [14, 16], [16, 10], [10, 0]],
@@ -221,8 +219,7 @@ def main():
                        time_evaluator)
 
     print('Route 3:')
-    print_transit_time([[0, 7], [7, 4], [4, 3], [3, 1], [1, 0]],
-                       time_evaluator)
+    print_transit_time([[0, 7], [7, 4], [4, 3], [3, 1], [1, 0]], time_evaluator)
 
 
 if __name__ == '__main__':
diff --git a/examples/python/tsp.py b/examples/python/tsp.py
index e7630fb0e2..f794d7c101 100755
--- a/examples/python/tsp.py
+++ b/examples/python/tsp.py
@@ -65,8 +65,8 @@ def create_data_model():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 def create_distance_evaluator(data):
diff --git a/examples/python/vendor_scheduling_sat.py b/examples/python/vendor_scheduling_sat.py
index c741a93b21..98ca23248a 100644
--- a/examples/python/vendor_scheduling_sat.py
+++ b/examples/python/vendor_scheduling_sat.py
@@ -39,9 +39,8 @@ class SolutionPrinter(cp_model.CpSolverSolutionCallback):
         print('Solution %i: ', self.__solution_count)
         print('  min vendors:', self.__min_vendors)
         for i in range(self.__num_vendors):
-            print(
-                '  - vendor %i: ' % i, self.__possible_schedules[self.Value(
-                    self.__selected_schedules[i])])
+            print('  - vendor %i: ' % i, self.__possible_schedules[self.Value(
+                self.__selected_schedules[i])])
         print()
 
         for j in range(self.__num_hours):
@@ -73,12 +72,12 @@ def main():
     # Last columns are :
     #   index_of_the_schedule, sum of worked hours (per work type).
     # The index is useful for branching.
-    possible_schedules = [[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 8],
-                          [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 4],
-                          [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2, 5],
-                          [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 4],
-                          [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 3],
-                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]]
+    possible_schedules = [[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
+                           8], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,
+                                4], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2,
+                                     5], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 4],
+                          [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4,
+                           3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]]
 
     num_possible_schedules = len(possible_schedules)
     selected_schedules = []
@@ -137,8 +136,8 @@ def main():
     print('  - conflicts : %i' % solver.NumConflicts())
     print('  - branches  : %i' % solver.NumBranches())
     print('  - wall time : %f s' % solver.WallTime())
-    print('  - number of solutions found: %i' %
-          solution_printer.solution_count())
+    print(
+        '  - number of solutions found: %i' % solution_printer.solution_count())
 
 
 if __name__ == '__main__':
diff --git a/examples/python/vrp.py b/examples/python/vrp.py
index 5028776a78..052d3f49f6 100755
--- a/examples/python/vrp.py
+++ b/examples/python/vrp.py
@@ -65,8 +65,8 @@ def create_data_model():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 def create_distance_evaluator(data):
diff --git a/examples/python/vrpgs.py b/examples/python/vrpgs.py
index f852b8e2e9..cb625c8553 100755
--- a/examples/python/vrpgs.py
+++ b/examples/python/vrpgs.py
@@ -65,8 +65,8 @@ def create_data_model():
 #######################
 def manhattan_distance(position_1, position_2):
     """Computes the Manhattan distance between two points"""
-    return (abs(position_1[0] - position_2[0]) +
-            abs(position_1[1] - position_2[1]))
+    return (
+        abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
 
 
 def create_distance_evaluator(data):
diff --git a/examples/python/wedding_optimal_chart_sat.py b/examples/python/wedding_optimal_chart_sat.py
index 1f2d4c2c2b..1d9323aaec 100644
--- a/examples/python/wedding_optimal_chart_sat.py
+++ b/examples/python/wedding_optimal_chart_sat.py
@@ -88,23 +88,23 @@ def BuildData():
 
     # Connection matrix: who knows who, and how strong
     # is the relation
-    C = [[1, 50, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [50, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 1, 50, 1, 1, 1, 1, 10, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 50, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 1, 1, 1, 50, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 1, 1, 50, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 1, 1, 1, 1, 1, 50, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 1, 1, 1, 1, 50, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [1, 1, 10, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 50, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 1, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
-         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]
+    C = [[1, 50, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+          0], [50, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+               0], [1, 1, 1, 50, 1, 1, 1, 1, 10, 0, 0, 0, 0, 0, 0, 0,
+                    0], [1, 1, 50, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+         [1, 1, 1, 1, 1, 50, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+          0], [1, 1, 1, 1, 50, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+               0], [1, 1, 1, 1, 1, 1, 1, 50, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+         [1, 1, 1, 1, 1, 1, 50, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+          0], [1, 1, 10, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
+               0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 50, 1, 1, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 1, 1, 1, 1, 1, 1,
+          1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
+               1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [
+                   0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
+               ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [
+                   0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1
+               ], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]
 
     # Names of the guests. B: Bride side, G: Groom side
     names = [
@@ -133,8 +133,8 @@ def solve_with_discrete_model():
     seats = {}
     for t in all_tables:
         for g in all_guests:
-            seats[(t, g)] = model.NewBoolVar(
-                "guest %i seats on table %i" % (g, t))
+            seats[(t, g)] = model.NewBoolVar("guest %i seats on table %i" % (g,
+                                                                             t))
 
     colocated = {}
     for g1 in range(num_guests - 1):
@@ -151,8 +151,9 @@ def solve_with_discrete_model():
 
     # Objective
     model.Maximize(
-        sum(C[g1][g2] * colocated[g1, g2] for g1 in range(num_guests - 1)
-            for g2 in range(g1 + 1, num_guests) if C[g1][g2] > 0))
+        sum(C[g1][g2] * colocated[g1, g2]
+            for g1 in range(num_guests - 1) for g2 in range(g1 + 1, num_guests)
+            if C[g1][g2] > 0))
 
     #
     # Constraints
@@ -186,17 +187,17 @@ def solve_with_discrete_model():
     # Min known neighbors rule.
     for t in all_tables:
         model.Add(
-            sum(same_table[(g1, g2, t)] for g1 in range(num_guests - 1)
-                for g2 in range(g1 + 1, num_guests)
-                for t in all_tables if C[g1][g2] > 0) >= min_known_neighbors)
+            sum(same_table[(g1, g2, t)]
+                for g1 in range(num_guests - 1)
+                for g2 in range(g1 + 1, num_guests) for t in all_tables
+                if C[g1][g2] > 0) >= min_known_neighbors)
 
     # Symmetry breaking. First guest seats on the first table.
     model.Add(seats[(0, 0)] == 1)
 
     ### Solve model.
     solver = cp_model.CpSolver()
-    solution_printer = WeddingChartPrinter(seats, names, num_tables,
-                                           num_guests)
+    solution_printer = WeddingChartPrinter(seats, names, num_tables, num_guests)
     solver.SolveWithSolutionCallback(model, solution_printer)
 
     print("Statistics")
diff --git a/examples/python/worker_schedule_sat.py b/examples/python/worker_schedule_sat.py
index 6c8c7c6ea9..5ecfef81e9 100644
--- a/examples/python/worker_schedule_sat.py
+++ b/examples/python/worker_schedule_sat.py
@@ -19,14 +19,13 @@ def schedule():
 
     # Input data.
     positions = [
-        1, 2, 8, 10, 5, 3, 4, 3, 6, 6, 4, 5, 4, 3, 4, 4, 3, 4, 2, 1, 0, 0, 0,
-        0, 1, 2, 9, 9, 4, 3, 4, 3, 5, 4, 5, 2, 5, 6, 6, 7, 4, 2, 1, 0, 0, 0, 0,
-        0, 0, 2, 7, 6, 5, 2, 4, 4, 6, 6, 4, 5, 5, 5, 7, 5, 4, 4, 2, 3, 1, 0, 0,
-        0, 1, 2, 9, 7, 2, 2, 4, 2, 4, 5, 3, 2, 6, 7, 5, 6, 4, 4, 2, 1, 0, 0, 0,
-        0, 2, 2, 8, 8, 6, 3, 3, 3, 10, 9, 6, 3, 3, 4, 5, 4, 5, 4, 2, 1, 0, 0,
-        0, 0, 1, 2, 9, 5, 5, 4, 5, 2, 5, 7, 5, 3, 4, 8, 4, 4, 2, 3, 1, 0, 0, 0,
-        0, 0, 1, 2, 10, 5, 5, 4, 5, 2, 4, 6, 7, 4, 4, 5, 4, 4, 3, 3, 2, 1, 0,
-        0, 0, 0
+        1, 2, 8, 10, 5, 3, 4, 3, 6, 6, 4, 5, 4, 3, 4, 4, 3, 4, 2, 1, 0, 0, 0, 0,
+        1, 2, 9, 9, 4, 3, 4, 3, 5, 4, 5, 2, 5, 6, 6, 7, 4, 2, 1, 0, 0, 0, 0, 0,
+        0, 2, 7, 6, 5, 2, 4, 4, 6, 6, 4, 5, 5, 5, 7, 5, 4, 4, 2, 3, 1, 0, 0, 0,
+        1, 2, 9, 7, 2, 2, 4, 2, 4, 5, 3, 2, 6, 7, 5, 6, 4, 4, 2, 1, 0, 0, 0, 0,
+        2, 2, 8, 8, 6, 3, 3, 3, 10, 9, 6, 3, 3, 4, 5, 4, 5, 4, 2, 1, 0, 0, 0, 0,
+        1, 2, 9, 5, 5, 4, 5, 2, 5, 7, 5, 3, 4, 8, 4, 4, 2, 3, 1, 0, 0, 0, 0, 0,
+        1, 2, 10, 5, 5, 4, 5, 2, 4, 6, 7, 4, 4, 5, 4, 4, 3, 3, 2, 1, 0, 0, 0, 0
     ]
 
     possible_shifts = [[
@@ -38,103 +37,79 @@ def schedule():
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 40
-    ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
-                           1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-                           1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 40
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
-                           1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
-                           1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 40
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-                           1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
-                           1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
-                           1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 40
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 16
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-                           1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 6, 16
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 7, 16
-                       ],
-                       [
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
-                           1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
-                           1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                           0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 8, 40
-                       ]]
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 40
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        2, 40
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        3, 40
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        4, 0
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        5, 16
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        6, 16
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        7, 16
+    ], [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+        8, 40
+    ]]
 
     # Useful numbers.
     num_slots = len(positions)