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bpo-17005: Add a class to perform topological sorting to the standard library (GH-11583)

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Co-Authored-By: Tim Peters <tim.peters@gmail.com>
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208
Doc/library/functools.rst
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@ -8,10 +8,16 @@
.. moduleauthor:: Raymond Hettinger <python@rcn.com>
.. moduleauthor:: Nick Coghlan <ncoghlan@gmail.com>
.. moduleauthor:: Łukasz Langa <lukasz@langa.pl>
.. moduleauthor:: Pablo Galindo <pablogsal@gmail.com>
.. sectionauthor:: Peter Harris <scav@blueyonder.co.uk>
**Source code:** :source:`Lib/functools.py`
.. testsetup:: default
import functools
from functools import *
--------------
The :mod:`functools` module is for higher-order functions: functions that act on
@ -512,6 +518,192 @@ The :mod:`functools` module defines the following functions:
.. versionadded:: 3.8
.. class:: TopologicalSorter(graph=None)
Provides functionality to topologically sort a graph of hashable nodes.
A topological order is a linear ordering of the vertices in a graph such that for
every directed edge u -> v from vertex u to vertex v, vertex u comes before vertex
v in the ordering. For instance, the vertices of the graph may represent tasks to
be performed, and the edges may represent constraints that one task must be
performed before another; in this example, a topological ordering is just a valid
sequence for the tasks. A complete topological ordering is possible if and only if
the graph has no directed cycles, that is, if it is a directed acyclic graph.
If the optional *graph* argument is provided it must be a dictionary representing
a directed acyclic graph where the keys are nodes and the values are iterables of
all predecessors of that node in the graph (the nodes that have edges that point
to the value in the key). Additional nodes can be added to the graph using the
:meth:`~TopologicalSorter.add` method.
In the general case, the steps required to perform the sorting of a given graph
are as follows:
* Create an instance of the :class:`TopologicalSorter` with an optional initial graph.
* Add additional nodes to the graph.
* Call :meth:`~TopologicalSorter.prepare` on the graph.
* While :meth:`~TopologicalSorter.is_active` is ``True``, iterate over the
nodes returned by :meth:`~TopologicalSorter.get_ready` and process them.
Call :meth:`~TopologicalSorter.done` on each node as it finishes processing.
In case just an immediate sorting of the nodes in the graph is required and
no parallelism is involved, the convenience method :meth:`TopologicalSorter.static_order`
can be used directly. For example, this method can be used to implement a simple
version of the C3 linearization algorithm used by Python to calculate the Method
Resolution Order (MRO) of a derived class:
.. doctest::
>>> class A: pass
>>> class B(A): pass
>>> class C(A): pass
>>> class D(B, C): pass
>>> D.__mro__
(<class 'D'>, <class 'B'>, <class 'C'>, <class 'A'>, <class 'object'>)
>>> graph = {D: {B, C}, C: {A}, B: {A}, A:{object}}
>>> ts = TopologicalSorter(graph)
>>> topological_order = tuple(ts.static_order())
>>> tuple(reversed(topological_order))
(<class 'D'>, <class 'B'>, <class 'C'>, <class 'A'>, <class 'object'>)
The class is designed to easily support parallel processing of the nodes as they
become ready. For instance::
topological_sorter = TopologicalSorter()
# Add nodes to 'topological_sorter'...
topological_sorter.prepare()
while topological_sorter.is_active():
for node in topological_sorter.get_ready():
# Worker threads or processes take nodes to work on off the
# 'task_queue' queue.
task_queue.put(node)
# When the work for a node is done, workers put the node in
# 'finalized_tasks_queue' so we can get more nodes to work on.
# The definition of 'is_active()' guarantees that, at this point, at
# least one node has been placed on 'task_queue' that hasn't yet
# been passed to 'done()', so this blocking 'get()' must (eventually)
# succeed. After calling 'done()', we loop back to call 'get_ready()'
# again, so put newly freed nodes on 'task_queue' as soon as
# logically possible.
node = finalized_tasks_queue.get()
topological_sorter.done(node)
.. method:: add(node, *predecessors)
Add a new node and its predecessors to the graph. Both the *node* and
all elements in *predecessors* must be hashable.
If called multiple times with the same node argument, the set of dependencies
will be the union of all dependencies passed in.
It is possible to add a node with no dependencies (*predecessors* is not
provided) or to provide a dependency twice. If a node that has not been
provided before is included among *predecessors* it will be automatically added
to the graph with no predecessors of its own.
Raises :exc:`ValueError` if called after :meth:`~TopologicalSorter.prepare`.
.. method:: prepare()
Mark the graph as finished and check for cycles in the graph. If any cycle is
detected, :exc:`CycleError` will be raised, but
:meth:`~TopologicalSorter.get_ready` can still be used to obtain as many nodes
as possible until cycles block more progress. After a call to this function,
the graph cannot be modified, and therefore no more nodes can be added using
:meth:`~TopologicalSorter.add`.
.. method:: is_active()
Returns ``True`` if more progress can be made and ``False`` otherwise. Progress
can be made if cycles do not block the resolution and either there are still
nodes ready that haven't yet been returned by
:meth:`TopologicalSorter.get_ready` or the number of nodes marked
:meth:`TopologicalSorter.done` is less than the number that have been returned
by :meth:`TopologicalSorter.get_ready`.
The :meth:`~TopologicalSorter.__bool__` method of this class defers to this
function, so instead of::
if ts.is_active():
...
if possible to simply do::
if ts:
...
Raises :exc:`ValueError` if called without calling :meth:`~TopologicalSorter.prepare`
previously.
.. method:: done(*nodes)
Marks a set of nodes returned by :meth:`TopologicalSorter.get_ready` as
processed, unblocking any successor of each node in *nodes* for being returned
in the future by a call to :meth:`TopologicalSorter.get_ready`.
Raises :exc:`ValueError` if any node in *nodes* has already been marked as
processed by a previous call to this method or if a node was not added to the
graph by using :meth:`TopologicalSorter.add`, if called without calling
:meth:`~TopologicalSorter.prepare` or if node has not yet been returned by
:meth:`~TopologicalSorter.get_ready`.
.. method:: get_ready()
Returns a ``tuple`` with all the nodes that are ready. Initially it returns all
nodes with no predecessors, and once those are marked as processed by calling
:meth:`TopologicalSorter.done`, further calls will return all new nodes that
have all their predecessors already processed. Once no more progress can be
made, empty tuples are returned.
made.
Raises :exc:`ValueError` if called without calling
:meth:`~TopologicalSorter.prepare` previously.
.. method:: static_order()
Returns an iterable of nodes in a topological order. Using this method
does not require to call :meth:`TopologicalSorter.prepare` or
:meth:`TopologicalSorter.done`. This method is equivalent to::
def static_order(self):
self.prepare()
while self.is_active():
node_group = self.get_ready()
yield from node_group
self.done(*node_group)
The particular order that is returned may depend on the specific order in
which the items were inserted in the graph. For example:
.. doctest::
>>> ts = TopologicalSorter()
>>> ts.add(3, 2, 1)
>>> ts.add(1, 0)
>>> print([*ts.static_order()])
[2, 0, 1, 3]
>>> ts2 = TopologicalSorter()
>>> ts2.add(1, 0)
>>> ts2.add(3, 2, 1)
>>> print([*ts2.static_order()])
[0, 2, 1, 3]
This is due to the fact that "0" and "2" are in the same level in the graph (they
would have been returned in the same call to :meth:`~TopologicalSorter.get_ready`)
and the order between them is determined by the order of insertion.
If any cycle is detected, :exc:`CycleError` will be raised.
.. versionadded:: 3.9
.. function:: update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
Update a *wrapper* function to look like the *wrapped* function. The optional
@ -621,3 +813,19 @@ differences. For instance, the :attr:`~definition.__name__` and :attr:`__doc__`
are not created automatically. Also, :class:`partial` objects defined in
classes behave like static methods and do not transform into bound methods
during instance attribute look-up.
Exceptions
----------
The :mod:`functools` module defines the following exception classes:
.. exception:: CycleError
Subclass of :exc:`ValueError` raised by :meth:`TopologicalSorter.prepare` if cycles exist
in the working graph. If multiple cycles exist, only one undefined choice among them will
be reported and included in the exception.
The detected cycle can be accessed via the second element in the :attr:`~CycleError.args`
attribute of the exception instance and consists in a list of nodes, such that each node is,
in the graph, an immediate predecessor of the next node in the list. In the reported list,
the first and the last node will be the same, to make it clear that it is cyclic.

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Doc/whatsnew/3.9.rst
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@ -166,6 +166,13 @@ ftplib
if the given timeout for their constructor is zero to prevent the creation of
a non-blocking socket. (Contributed by Dong-hee Na in :issue:`39259`.)
functools
---------
Add the :class:`functools.TopologicalSorter` class to offer functionality to perform
topological sorting of graphs. (Contributed by Pablo Galindo, Tim Peters and Larry
Hastings in :issue:`17005`.)
gc
--

249
Lib/functools.py
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@ -10,8 +10,9 @@
# See C source code for _functools credits/copyright
__all__ = ['update_wrapper', 'wraps', 'WRAPPER_ASSIGNMENTS', 'WRAPPER_UPDATES',
'total_ordering', 'cmp_to_key', 'lru_cache', 'reduce', 'partial',
'partialmethod', 'singledispatch', 'singledispatchmethod']
'total_ordering', 'cmp_to_key', 'lru_cache', 'reduce',
'TopologicalSorter', 'CycleError',
'partial', 'partialmethod', 'singledispatch', 'singledispatchmethod']
from abc import get_cache_token
from collections import namedtuple
@ -192,6 +193,250 @@ def total_ordering(cls):
setattr(cls, opname, opfunc)
return cls
################################################################################
### topological sort
################################################################################
_NODE_OUT = -1
_NODE_DONE = -2
class _NodeInfo:
__slots__ = 'node', 'npredecessors', 'successors'
def __init__(self, node):
# The node this class is augmenting.
self.node = node
# Number of predecessors, generally >= 0. When this value falls to 0,
# and is returned by get_ready(), this is set to _NODE_OUT and when the
# node is marked done by a call to done(), set to _NODE_DONE.
self.npredecessors = 0
# List of successor nodes. The list can contain duplicated elements as
# long as they're all reflected in the successor's npredecessors attribute).
self.successors = []
class CycleError(ValueError):
"""Subclass of ValueError raised by TopologicalSorterif cycles exist in the graph
If multiple cycles exist, only one undefined choice among them will be reported
and included in the exception. The detected cycle can be accessed via the second
element in the *args* attribute of the exception instance and consists in a list
of nodes, such that each node is, in the graph, an immediate predecessor of the
next node in the list. In the reported list, the first and the last node will be
the same, to make it clear that it is cyclic.
"""
pass
class TopologicalSorter:
"""Provides functionality to topologically sort a graph of hashable nodes"""
def __init__(self, graph=None):
self._node2info = {}
self._ready_nodes = None
self._npassedout = 0
self._nfinished = 0
if graph is not None:
for node, predecessors in graph.items():
self.add(node, *predecessors)
def _get_nodeinfo(self, node):
if (result := self._node2info.get(node)) is None:
self._node2info[node] = result = _NodeInfo(node)
return result
def add(self, node, *predecessors):
"""Add a new node and its predecessors to the graph.
Both the *node* and all elements in *predecessors* must be hashable.
If called multiple times with the same node argument, the set of dependencies
will be the union of all dependencies passed in.
It is possible to add a node with no dependencies (*predecessors* is not provided)
as well as provide a dependency twice. If a node that has not been provided before
is included among *predecessors* it will be automatically added to the graph with
no predecessors of its own.
Raises ValueError if called after "prepare".
"""
if self._ready_nodes is not None:
raise ValueError("Nodes cannot be added after a call to prepare()")
# Create the node -> predecessor edges
nodeinfo = self._get_nodeinfo(node)
nodeinfo.npredecessors += len(predecessors)
# Create the predecessor -> node edges
for pred in predecessors:
pred_info = self._get_nodeinfo(pred)
pred_info.successors.append(node)
def prepare(self):
"""Mark the graph as finished and check for cycles in the graph.
If any cycle is detected, "CycleError" will be raised, but "get_ready" can
still be used to obtain as many nodes as possible until cycles block more
progress. After a call to this function, the graph cannot be modified and
therefore no more nodes can be added using "add".
"""
if self._ready_nodes is not None:
raise ValueError("cannot prepare() more than once")
self._ready_nodes = [i.node for i in self._node2info.values()
if i.npredecessors == 0]
# ready_nodes is set before we look for cycles on purpose:
# if the user wants to catch the CycleError, that's fine,
# they can continue using the instance to grab as many
# nodes as possible before cycles block more progress
cycle = self._find_cycle()
if cycle:
raise CycleError(f"nodes are in a cycle", cycle)
def get_ready(self):
"""Return a tuple of all the nodes that are ready.
Initially it returns all nodes with no predecessors; once those are marked
as processed by calling "done", further calls will return all new nodes that
have all their predecessors already processed. Once no more progress can be made,
empty tuples are returned.
Raises ValueError if called without calling "prepare" previously.
"""
if self._ready_nodes is None:
raise ValueError("prepare() must be called first")
# Get the nodes that are ready and mark them
result = tuple(self._ready_nodes)
n2i = self._node2info
for node in result:
n2i[node].npredecessors = _NODE_OUT
# Clean the list of nodes that are ready and update
# the counter of nodes that we have returned.
self._ready_nodes.clear()
self._npassedout += len(result)
return result
def is_active(self):
"""Return True if more progress can be made and ``False`` otherwise.
Progress can be made if cycles do not block the resolution and either there
are still nodes ready that haven't yet been returned by "get_ready" or the
number of nodes marked "done" is less than the number that have been returned
by "get_ready".
Raises ValueError if called without calling "prepare" previously.
"""
if self._ready_nodes is None:
raise ValueError("prepare() must be called first")
return self._nfinished < self._npassedout or bool(self._ready_nodes)
def __bool__(self):
return self.is_active()
def done(self, *nodes):
"""Marks a set of nodes returned by "get_ready" as processed.
This method unblocks any successor of each node in *nodes* for being returned
in the future by a a call to "get_ready"
Raises :exec:`ValueError` if any node in *nodes* has already been marked as
processed by a previous call to this method, if a node was not added to the
graph by using "add" or if called without calling "prepare" previously or if
node has not yet been returned by "get_ready".
"""
if self._ready_nodes is None:
raise ValueError("prepare() must be called first")
n2i = self._node2info
for node in nodes:
# Check if we know about this node (it was added previously using add()
if (nodeinfo := n2i.get(node)) is None:
raise ValueError(f"node {node!r} was not added using add()")
# If the node has not being returned (marked as ready) previously, inform the user.
stat = nodeinfo.npredecessors
if stat != _NODE_OUT:
if stat >= 0:
raise ValueError(f"node {node!r} was not passed out (still not ready)")
elif stat == _NODE_DONE:
raise ValueError(f"node {node!r} was already marked done")
else:
assert False, f"node {node!r}: unknown status {stat}"
# Mark the node as processed
nodeinfo.npredecessors = _NODE_DONE
# Go to all the successors and reduce the number of predecessors, collecting all the ones
# that are ready to be returned in the next get_ready() call.
for successor in nodeinfo.successors:
successor_info = n2i[successor]
successor_info.npredecessors -= 1
if successor_info.npredecessors == 0:
self._ready_nodes.append(successor)
self._nfinished += 1
def _find_cycle(self):
n2i = self._node2info
stack = []
itstack = []
seen = set()
node2stacki = {}
for node in n2i:
if node in seen:
continue
while True:
if node in seen:
# If we have seen already the node and is in the
# current stack we have found a cycle.
if node in node2stacki:
return stack[node2stacki[node]:] + [node]
# else go on to get next successor
else:
seen.add(node)
itstack.append(iter(n2i[node].successors).__next__)
node2stacki[node] = len(stack)
stack.append(node)
# Backtrack to the topmost stack entry with
# at least another successor.
while stack:
try:
node = itstack[-1]()
break
except StopIteration:
del node2stacki[stack.pop()]
itstack.pop()
else:
break
return None
def static_order(self):
"""Returns an iterable of nodes in a topological order.
The particular order that is returned may depend on the specific
order in which the items were inserted in the graph.
Using this method does not require to call "prepare" or "done". If any
cycle is detected, :exc:`CycleError` will be raised.
"""
self.prepare()
while self.is_active():
node_group = self.get_ready()
yield from node_group
self.done(*node_group)
################################################################################
### cmp_to_key() function converter

274
Lib/test/test_functools.py
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@ -3,7 +3,7 @@ import builtins
import collections
import collections.abc
import copy
from itertools import permutations
from itertools import permutations, chain
import pickle
from random import choice
import sys
@ -13,9 +13,12 @@ import time
import typing
import unittest
import unittest.mock
import os
from weakref import proxy
import contextlib
from test.support.script_helper import assert_python_ok
import functools
py_functools = support.import_fresh_module('functools', blocked=['_functools'])
@ -1158,6 +1161,275 @@ class Orderable_LT:
return self.value == other.value
class TestTopologicalSort(unittest.TestCase):
def _test_graph(self, graph, expected):
def static_order_with_groups(ts):
ts.prepare()
while ts.is_active():
nodes = ts.get_ready()
for node in nodes:
ts.done(node)
yield nodes
ts = functools.TopologicalSorter(graph)
self.assertEqual(list(static_order_with_groups(ts)), list(expected))
ts = functools.TopologicalSorter(graph)
self.assertEqual(list(ts.static_order()), list(chain(*expected)))
def _assert_cycle(self, graph, cycle):
ts = functools.TopologicalSorter()
for node, dependson in graph.items():
ts.add(node, *dependson)
try:
ts.prepare()
except functools.CycleError as e:
msg, seq = e.args
self.assertIn(' '.join(map(str, cycle)),
' '.join(map(str, seq * 2)))
else:
raise
def test_simple_cases(self):
self._test_graph(
{2: {11},
9: {11, 8},
10: {11, 3},
11: {7, 5},
8: {7, 3}},
[(3, 5, 7), (11, 8), (2, 10, 9)]
)
self._test_graph({1: {}}, [(1,)])
self._test_graph({x: {x+1} for x in range(10)},
[(x,) for x in range(10, -1, -1)])
self._test_graph({2: {3}, 3: {4}, 4: {5}, 5: {1},
11: {12}, 12: {13}, 13: {14}, 14: {15}},
[(1, 15), (5, 14), (4, 13), (3, 12), (2, 11)])
self._test_graph({
0: [1, 2],
1: [3],
2: [5, 6],
3: [4],
4: [9],
5: [3],
6: [7],
7: [8],
8: [4],
9: []
},
[(9,), (4,), (3, 8), (1, 5, 7), (6,), (2,), (0,)]
)
self._test_graph({
0: [1, 2],
1: [],
2: [3],
3: []
},
[(1, 3), (2,), (0,)]
)
self._test_graph({
0: [1, 2],
1: [],
2: [3],
3: [],
4: [5],
5: [6],
6: []
},
[(1, 3, 6), (2, 5), (0, 4)]
)
def test_no_dependencies(self):
self._test_graph(
{1: {2},
3: {4},
5: {6}},
[(2, 4, 6), (1, 3, 5)]
)
self._test_graph(
{1: set(),
3: set(),
5: set()},
[(1, 3, 5)]
)
def test_the_node_multiple_times(self):
# Test same node multiple times in dependencies
self._test_graph({1: {2}, 3: {4}, 0: [2, 4, 4, 4, 4, 4]},
[(2, 4), (1, 3, 0)])
# Test adding the same dependency multiple times
ts = functools.TopologicalSorter()
ts.add(1, 2)
ts.add(1, 2)
ts.add(1, 2)
self.assertEqual([*ts.static_order()], [2, 1])
def test_graph_with_iterables(self):
dependson = (2*x + 1 for x in range(5))
ts = functools.TopologicalSorter({0: dependson})
self.assertEqual(list(ts.static_order()), [1, 3, 5, 7, 9, 0])
def test_add_dependencies_for_same_node_incrementally(self):
# Test same node multiple times
ts = functools.TopologicalSorter()
ts.add(1, 2)
ts.add(1, 3)
ts.add(1, 4)
ts.add(1, 5)
ts2 = functools.TopologicalSorter({1: {2, 3, 4, 5}})
self.assertEqual([*ts.static_order()], [*ts2.static_order()])
def test_empty(self):
self._test_graph({}, [])
def test_cycle(self):
# Self cycle
self._assert_cycle({1: {1}}, [1, 1])
# Simple cycle
self._assert_cycle({1: {2}, 2: {1}}, [1, 2, 1])
# Indirect cycle
self._assert_cycle({1: {2}, 2: {3}, 3: {1}}, [1, 3, 2, 1])
# not all elements involved in a cycle
self._assert_cycle({1: {2}, 2: {3}, 3: {1}, 5: {4}, 4: {6}}, [1, 3, 2, 1])
# Multiple cycles
self._assert_cycle({1: {2}, 2: {1}, 3: {4}, 4: {5}, 6: {7}, 7: {6}},
[1, 2, 1])
# Cycle in the middle of the graph
self._assert_cycle({1: {2}, 2: {3}, 3: {2, 4}, 4: {5}}, [3, 2])
def test_calls_before_prepare(self):
ts = functools.TopologicalSorter()
with self.assertRaisesRegex(ValueError, r"prepare\(\) must be called first"):
ts.get_ready()
with self.assertRaisesRegex(ValueError, r"prepare\(\) must be called first"):
ts.done(3)
with self.assertRaisesRegex(ValueError, r"prepare\(\) must be called first"):
ts.is_active()
def test_prepare_multiple_times(self):
ts = functools.TopologicalSorter()
ts.prepare()
with self.assertRaisesRegex(ValueError, r"cannot prepare\(\) more than once"):
ts.prepare()
def test_invalid_nodes_in_done(self):
ts = functools.TopologicalSorter()
ts.add(1, 2, 3, 4)
ts.add(2, 3, 4)
ts.prepare()
ts.get_ready()
with self.assertRaisesRegex(ValueError, "node 2 was not passed out"):
ts.done(2)
with self.assertRaisesRegex(ValueError, r"node 24 was not added using add\(\)"):
ts.done(24)
def test_done(self):
ts = functools.TopologicalSorter()
ts.add(1, 2, 3, 4)
ts.add(2, 3)
ts.prepare()
self.assertEqual(ts.get_ready(), (3, 4))
# If we don't mark anything as done, get_ready() returns nothing
self.assertEqual(ts.get_ready(), ())
ts.done(3)
# Now 2 becomes available as 3 is done
self.assertEqual(ts.get_ready(), (2,))
self.assertEqual(ts.get_ready(), ())
ts.done(4)
ts.done(2)
# Only 1 is missing
self.assertEqual(ts.get_ready(), (1,))
self.assertEqual(ts.get_ready(), ())
ts.done(1)
self.assertEqual(ts.get_ready(), ())
self.assertFalse(ts.is_active())
def test_is_active(self):
ts = functools.TopologicalSorter()
ts.add(1, 2)
ts.prepare()
self.assertTrue(ts.is_active())
self.assertEqual(ts.get_ready(), (2,))
self.assertTrue(ts.is_active())
ts.done(2)
self.assertTrue(ts.is_active())
self.assertEqual(ts.get_ready(), (1,))
self.assertTrue(ts.is_active())
ts.done(1)
self.assertFalse(ts.is_active())
def test_not_hashable_nodes(self):
ts = functools.TopologicalSorter()
self.assertRaises(TypeError, ts.add, dict(), 1)
self.assertRaises(TypeError, ts.add, 1, dict())
self.assertRaises(TypeError, ts.add, dict(), dict())
def test_order_of_insertion_does_not_matter_between_groups(self):
def get_groups(ts):
ts.prepare()
while ts.is_active():
nodes = ts.get_ready()
ts.done(*nodes)
yield set(nodes)
ts = functools.TopologicalSorter()
ts.add(3, 2, 1)
ts.add(1, 0)
ts.add(4, 5)
ts.add(6, 7)
ts.add(4, 7)
ts2 = functools.TopologicalSorter()
ts2.add(1, 0)
ts2.add(3, 2, 1)
ts2.add(4, 7)
ts2.add(6, 7)
ts2.add(4, 5)
self.assertEqual(list(get_groups(ts)), list(get_groups(ts2)))
def test_static_order_does_not_change_with_the_hash_seed(self):
def check_order_with_hash_seed(seed):
code = """if 1:
import functools
ts = functools.TopologicalSorter()
ts.add('blech', 'bluch', 'hola')
ts.add('abcd', 'blech', 'bluch', 'a', 'b')
ts.add('a', 'a string', 'something', 'b')
ts.add('bluch', 'hola', 'abcde', 'a', 'b')
print(list(ts.static_order()))
"""
env = os.environ.copy()
# signal to assert_python not to do a copy
# of os.environ on its own
env['__cleanenv'] = True
env['PYTHONHASHSEED'] = str(seed)
out = assert_python_ok('-c', code, **env)
return out
run1 = check_order_with_hash_seed(1234)
run2 = check_order_with_hash_seed(31415)
self.assertNotEqual(run1, "")
self.assertNotEqual(run2, "")
self.assertEqual(run1, run2)
class TestLRU:
def test_lru(self):

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Add :class:`functools.TopologicalSorter` to the :mod:`functools` module to
offers functionality to perform topological sorting of graphs. Patch by
Pablo Galindo, Tim Peters and Larry Hastings.