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# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
from random import Random
import pytest
from hypothesis import HealthCheck, settings
from hypothesis.internal.conjecture import utils as cu
from hypothesis.internal.conjecture.engine import ConjectureData, ConjectureRunner
from hypothesis.strategies._internal import SearchStrategy
POISON = "POISON"
MAX_INT = 2**32 - 1
class PoisonedTree(SearchStrategy):
"""Generates variable sized tuples with an implicit tree structure.
The actual result is flattened out, but the hierarchy is implicit in
the data.
"""
def __init__(self, p):
super().__init__()
self.__p = p
def do_draw(self, data):
if cu.biased_coin(data, self.__p):
return data.draw(self) + data.draw(self)
else:
# We draw n as two separate calls so that it doesn't show up as a
# single block. If it did, the heuristics that allow us to move
# blocks around would fire and it would move right, which would
# then allow us to shrink it more easily.
n = (data.draw_bits(16) << 16) | data.draw_bits(16)
if n == MAX_INT:
return (POISON,)
else:
return (None,)
LOTS = 10**6
TEST_SETTINGS = settings(
database=None,
suppress_health_check=HealthCheck.all(),
max_examples=LOTS,
deadline=None,
)
@pytest.mark.parametrize("size", [2, 5, 10])
@pytest.mark.parametrize("seed", [0, 15993493061449915028])
def test_can_reduce_poison_from_any_subtree(size, seed):
"""This test validates that we can minimize to any leaf node of a binary
tree, regardless of where in the tree the leaf is."""
random = Random(seed)
# Initially we create the minimal tree of size n, regardless of whether it
# is poisoned (which it won't be - the poison event essentially never
# happens when drawing uniformly at random).
# Choose p so that the expected size of the tree is equal to the desired
# size.
p = 1.0 / (2.0 - 1.0 / size)
strat = PoisonedTree(p)
def test_function(data):
v = data.draw(strat)
if len(v) >= size:
data.mark_interesting()
runner = ConjectureRunner(test_function, random=random, settings=TEST_SETTINGS)
runner.generate_new_examples()
runner.shrink_interesting_examples()
(data,) = runner.interesting_examples.values()
assert len(ConjectureData.for_buffer(data.buffer).draw(strat)) == size
starts = [b.start for b in data.blocks if b.length == 2]
assert len(starts) % 2 == 0
for i in range(0, len(starts), 2):
# Now for each leaf position in the tree we try inserting a poison
# value artificially. Additionally, we add a marker to the end that
# must be preserved. The marker means that we are not allow to rely on
# discarding the end of the buffer to get the desired shrink.
u = starts[i]
marker = bytes([1, 2, 3, 4])
def test_function_with_poison(data):
v = data.draw(strat)
m = data.draw_bytes(len(marker))
if POISON in v and m == marker:
data.mark_interesting()
runner = ConjectureRunner(
test_function_with_poison, random=random, settings=TEST_SETTINGS
)
runner.cached_test_function(
data.buffer[:u] + bytes([255]) * 4 + data.buffer[u + 4 :] + marker
)
assert runner.interesting_examples
runner.shrink_interesting_examples()
(shrunk,) = runner.interesting_examples.values()
assert ConjectureData.for_buffer(shrunk.buffer).draw(strat) == (POISON,)
|
