tests/test-ancestor.py
author Pierre-Yves David <pierre-yves.david@ens-lyon.org>
Wed, 12 Apr 2017 16:34:05 +0200
changeset 32031 11a2461fc9b1
parent 30559 d83ca854fa21
child 32291 bd872f64a8ba
permissions -rw-r--r--
upgrade: move descriptions and selection logic in individual classes Our goal here is to get top level definition for all the format variants. Having them defined outside of the function enabled other users of that logic. They are two keys components of a format variant: 1) the name and various descriptions of its effect, 2) the code that checks if the repo is using this variant and if the config enables it. That second items make us pick a class-based approach, since different variants requires different code (even if in practice, many can reuse the same logic). Each variants define its own class that is then used like a singleton. The class-based approach also clarify the definitions part a bit since each are simple assignment in an indented block. The 'fromdefault' and 'fromconfig' are respectively replaced by a class attribute and a method to be called at the one place where "fromconfig" matters. Overall, they are many viable approach for this, but this is the one I picked.
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from __future__ import absolute_import, print_function
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import binascii
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import getopt
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import math
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import os
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import random
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import sys
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import time
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from mercurial.node import nullrev
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from mercurial import (
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    ancestor,
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    debugcommands,
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    hg,
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    ui as uimod,
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    util,
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)
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def buildgraph(rng, nodes=100, rootprob=0.05, mergeprob=0.2, prevprob=0.7):
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    '''nodes: total number of nodes in the graph
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    rootprob: probability that a new node (not 0) will be a root
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    mergeprob: probability that, excluding a root a node will be a merge
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    prevprob: probability that p1 will be the previous node
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    return value is a graph represented as an adjacency list.
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    '''
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    graph = [None] * nodes
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    for i in xrange(nodes):
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        if i == 0 or rng.random() < rootprob:
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            graph[i] = [nullrev]
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        elif i == 1:
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            graph[i] = [0]
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        elif rng.random() < mergeprob:
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            if i == 2 or rng.random() < prevprob:
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                # p1 is prev
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                p1 = i - 1
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            else:
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                p1 = rng.randrange(i - 1)
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            p2 = rng.choice(range(0, p1) + range(p1 + 1, i))
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            graph[i] = [p1, p2]
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        elif rng.random() < prevprob:
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            graph[i] = [i - 1]
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        else:
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            graph[i] = [rng.randrange(i - 1)]
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    return graph
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def buildancestorsets(graph):
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    ancs = [None] * len(graph)
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    for i in xrange(len(graph)):
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        ancs[i] = set([i])
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        if graph[i] == [nullrev]:
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            continue
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        for p in graph[i]:
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            ancs[i].update(ancs[p])
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    return ancs
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class naiveincrementalmissingancestors(object):
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    def __init__(self, ancs, bases):
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        self.ancs = ancs
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        self.bases = set(bases)
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    def addbases(self, newbases):
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        self.bases.update(newbases)
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    def removeancestorsfrom(self, revs):
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        for base in self.bases:
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            if base != nullrev:
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                revs.difference_update(self.ancs[base])
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        revs.discard(nullrev)
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    def missingancestors(self, revs):
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        res = set()
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        for rev in revs:
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            if rev != nullrev:
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                res.update(self.ancs[rev])
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        for base in self.bases:
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            if base != nullrev:
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                res.difference_update(self.ancs[base])
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        return sorted(res)
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def test_missingancestors(seed, rng):
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    # empirically observed to take around 1 second
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    graphcount = 100
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    testcount = 10
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    inccount = 10
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    nerrs = [0]
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    # the default mu and sigma give us a nice distribution of mostly
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    # single-digit counts (including 0) with some higher ones
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    def lognormrandom(mu, sigma):
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        return int(math.floor(rng.lognormvariate(mu, sigma)))
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    def samplerevs(nodes, mu=1.1, sigma=0.8):
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        count = min(lognormrandom(mu, sigma), len(nodes))
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        return rng.sample(nodes, count)
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    def err(seed, graph, bases, seq, output, expected):
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        if nerrs[0] == 0:
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            print('seed:', hex(seed)[:-1], file=sys.stderr)
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        if gerrs[0] == 0:
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            print('graph:', graph, file=sys.stderr)
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        print('* bases:', bases, file=sys.stderr)
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        print('* seq: ', seq, file=sys.stderr)
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        print('*  output:  ', output, file=sys.stderr)
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        print('*  expected:', expected, file=sys.stderr)
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        nerrs[0] += 1
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        gerrs[0] += 1
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    for g in xrange(graphcount):
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        graph = buildgraph(rng)
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        ancs = buildancestorsets(graph)
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        gerrs = [0]
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        for _ in xrange(testcount):
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            # start from nullrev to include it as a possibility
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            graphnodes = range(nullrev, len(graph))
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            bases = samplerevs(graphnodes)
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            # fast algorithm
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            inc = ancestor.incrementalmissingancestors(graph.__getitem__, bases)
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            # reference slow algorithm
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            naiveinc = naiveincrementalmissingancestors(ancs, bases)
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            seq = []
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            revs = []
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            for _ in xrange(inccount):
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                if rng.random() < 0.2:
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                    newbases = samplerevs(graphnodes)
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                    seq.append(('addbases', newbases))
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                    inc.addbases(newbases)
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                    naiveinc.addbases(newbases)
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                if rng.random() < 0.4:
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                    # larger set so that there are more revs to remove from
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                    revs = samplerevs(graphnodes, mu=1.5)
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                    seq.append(('removeancestorsfrom', revs))
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                    hrevs = set(revs)
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                    rrevs = set(revs)
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                    inc.removeancestorsfrom(hrevs)
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                    naiveinc.removeancestorsfrom(rrevs)
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                    if hrevs != rrevs:
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                        err(seed, graph, bases, seq, sorted(hrevs),
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                            sorted(rrevs))
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                else:
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                    revs = samplerevs(graphnodes)
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                    seq.append(('missingancestors', revs))
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                    h = inc.missingancestors(revs)
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                    r = naiveinc.missingancestors(revs)
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                    if h != r:
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                        err(seed, graph, bases, seq, h, r)
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# graph is a dict of child->parent adjacency lists for this graph:
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# o  13
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# |
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# | o  12
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# | |
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# | | o    11
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# | | |\
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# | | | | o  10
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# | | | | |
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# | o---+ |  9
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# | | | | |
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# o | | | |  8
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#  / / / /
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# | | o |  7
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# | | | |
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# o---+ |  6
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#  / / /
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# | | o  5
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# | |/
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# | o  4
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# | |
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# o |  3
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# | |
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# | o  2
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# |/
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# o  1
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# |
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# o  0
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graph = {0: [-1], 1: [0], 2: [1], 3: [1], 4: [2], 5: [4], 6: [4],
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         7: [4], 8: [-1], 9: [6, 7], 10: [5], 11: [3, 7], 12: [9],
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         13: [8]}
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def genlazyancestors(revs, stoprev=0, inclusive=False):
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    print(("%% lazy ancestor set for %s, stoprev = %s, inclusive = %s" %
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           (revs, stoprev, inclusive)))
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    return ancestor.lazyancestors(graph.get, revs, stoprev=stoprev,
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                                  inclusive=inclusive)
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def printlazyancestors(s, l):
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    print('membership: %r' % [n for n in l if n in s])
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    print('iteration:  %r' % list(s))
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def test_lazyancestors():
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    # Empty revs
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    s = genlazyancestors([])
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    printlazyancestors(s, [3, 0, -1])
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    # Standard example
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    s = genlazyancestors([11, 13])
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    printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
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    # Standard with ancestry in the initial set (1 is ancestor of 3)
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    s = genlazyancestors([1, 3])
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    printlazyancestors(s, [1, -1, 0])
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    # Including revs
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    s = genlazyancestors([11, 13], inclusive=True)
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    printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
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    # Test with stoprev
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    s = genlazyancestors([11, 13], stoprev=6)
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    printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
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    s = genlazyancestors([11, 13], stoprev=6, inclusive=True)
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    printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
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# The C gca algorithm requires a real repo. These are textual descriptions of
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# DAGs that have been known to be problematic.
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dagtests = [
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    '+2*2*2/*3/2',
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    '+3*3/*2*2/*4*4/*4/2*4/2*2',
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]
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def test_gca():
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    u = uimod.ui.load()
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    for i, dag in enumerate(dagtests):
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        repo = hg.repository(u, 'gca%d' % i, create=1)
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        cl = repo.changelog
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        if not util.safehasattr(cl.index, 'ancestors'):
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            # C version not available
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            return
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        debugcommands.debugbuilddag(u, repo, dag)
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        # Compare the results of the Python and C versions. This does not
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        # include choosing a winner when more than one gca exists -- we make
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        # sure both return exactly the same set of gcas.
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        for a in cl:
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            for b in cl:
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                cgcas = sorted(cl.index.ancestors(a, b))
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                pygcas = sorted(ancestor.ancestors(cl.parentrevs, a, b))
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                if cgcas != pygcas:
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                    print("test_gca: for dag %s, gcas for %d, %d:"
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                          % (dag, a, b))
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                    print("  C returned:      %s" % cgcas)
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                    print("  Python returned: %s" % pygcas)
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def main():
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    seed = None
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    opts, args = getopt.getopt(sys.argv[1:], 's:', ['seed='])
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    for o, a in opts:
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        if o in ('-s', '--seed'):
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            seed = long(a, base=0) # accepts base 10 or 16 strings
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    if seed is None:
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        try:
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            seed = long(binascii.hexlify(os.urandom(16)), 16)
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        except AttributeError:
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            seed = long(time.time() * 1000)
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    rng = random.Random(seed)
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    test_missingancestors(seed, rng)
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    test_lazyancestors()
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    test_gca()
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if __name__ == '__main__':
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    main()