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dca90c364e
find_deepest is used to find the "best" ancestors given a list. In the main loop it keeps an invariant called 'ninteresting' which is supposed to contain the number of non-zero entries in the 'interesting' array. This invariant is incorrectly maintained, however, which leads the the algorithm returning an empty result for certain graphs. This has been fixed. Also, the 'interesting' array is supposed to fit 2^ancestors values, but is incorrectly allocated to twice that size. This has been fixed as well. The tests in test-ancestor.py compare the Python and C versions of the code, and report the error correctly, since the Python version works correct. Even so, I have added an additional test against the expected result, in the event that both algorithms have an identical error in the future. This fixes issue5623.
276 lines
8.7 KiB
Python
276 lines
8.7 KiB
Python
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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pycompat,
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ui as uimod,
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util,
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)
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if pycompat.ispy3:
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long = int
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xrange = range
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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(list(range(0, p1)) + list(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] = {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, and, optionally, known pairs
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# of revisions and their expected ancestor list.
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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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('+2*2*/2*4*/4*/3*2/4', {(6, 7): [3, 5]}),
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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, tests) in enumerate(dagtests):
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repo = hg.repository(u, b'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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# Also compare against expected results, if available.
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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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expected = None
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if (a, b) in tests:
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expected = tests[(a, b)]
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if cgcas != pygcas or (expected and cgcas != expected):
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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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if expected:
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print(" expected: %s" % expected)
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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()
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