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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/.
import itertools
import pytest
from hypothesis import assume, example, given, strategies as st
from hypothesis.errors import InvalidState
from hypothesis.internal.conjecture.dfa.lstar import IntegerNormalizer, LStar
def test_can_learn_simple_predicate():
learner = LStar(lambda s: len(s) >= 3)
learner.learn(bytes(3))
dfa = learner.dfa
assert dfa.start == 0
assert dfa.transition(0, 0) == 1
assert dfa.transition(1, 0) == 2
assert dfa.transition(2, 0) == 3
assert dfa.transition(3, 0) == 3
assert not dfa.is_accepting(0)
assert not dfa.is_accepting(1)
assert not dfa.is_accepting(2)
assert dfa.is_accepting(3)
def test_relearning_does_not_change_generation():
learner = LStar(lambda s: len(s) >= 3)
prev = learner.generation
learner.learn(bytes(3))
assert prev != learner.generation
prev = learner.generation
learner.learn(bytes(3))
assert prev == learner.generation
def test_can_learn_dead_nodes():
learner = LStar(lambda s: len(s) == 4 and max(s) <= 1)
learner.learn(bytes(4))
assert learner.dfa.matches(bytes(4))
assert learner.dfa.matches(bytes([1] * 4))
assert learner.dfa.matches(bytes([1] * 4))
learner.learn([2, 0, 0, 0])
# Need a length 5 string to distinguish this from
# something that just loops back to zero.
learner.learn([2, 0, 0, 0, 0])
dfa = learner.dfa
assert dfa.is_dead(dfa.transition(dfa.start, 2))
assert dfa.is_dead(dfa.transition(dfa.start, 3))
def test_iterates_over_learned_strings():
upper_bound = bytes([1, 2])
learner = LStar(lambda s: len(s) == 2 and max(s) <= 5 and s <= upper_bound)
learner.learn(upper_bound)
prev = -1
while learner.generation != prev:
prev = learner.generation
learner.learn([1, 2, 0])
learner.learn([6, 1, 2])
learner.learn([1, 3])
for i in range(7):
learner.learn([0, i])
learner.learn([1, i])
learner.learn([2, 0])
learner.learn([2, 0, 0, 0])
learner.learn([2, 0, 0])
learner.learn([0, 6, 0, 0])
learner.learn([1, 3, 0, 0])
learner.learn([1, 6, 0, 0])
learner.learn([0, 0, 0, 0, 0])
dfa = learner.dfa
n = 9
matches = list(itertools.islice(dfa.all_matching_strings(), n + 1))
for m in matches:
assert learner.member(m), list(m)
assert len(matches) == n
def test_iteration_with_dead_nodes():
learner = LStar(lambda s: len(s) == 3 and max(s) <= 1 and s[1] == 0)
learner.learn([1, 0, 1])
learner.learn([1, 1, 1])
learner.learn([0, 1, 1])
learner.learn([1, 1, 0])
learner.learn([1, 1, 1, 0, 1])
learner.learn([0, 0, 4])
dfa = learner.dfa
i = dfa.transition(dfa.start, 1)
assert not dfa.is_dead(i)
assert dfa.is_dead(dfa.transition(i, 2))
assert list(learner.dfa.all_matching_strings()) == [
bytes([0, 0, 0]),
bytes([0, 0, 1]),
bytes([1, 0, 0]),
bytes([1, 0, 1]),
]
def test_learning_is_just_checking_when_fully_explored():
count = 0
def accept(s):
nonlocal count
count += 1
return len(s) <= 5 and all(c == 0 for c in s)
learner = LStar(accept)
for c in [0, 1]:
for n in range(10):
learner.learn(bytes([c]) * n)
assert list(learner.dfa.all_matching_strings()) == [bytes(n) for n in range(6)]
prev = count
learner.learn([2] * 11)
calls = count - prev
assert calls == 1
def test_canonicalises_values_to_zero_where_appropriate():
calls = 0
def member(s):
nonlocal calls
calls += 1
return len(s) == 10
learner = LStar(member)
learner.learn(bytes(10))
learner.learn(bytes(11))
prev = calls
assert learner.dfa.matches(bytes([1] * 10))
assert calls == prev
def test_normalizing_defaults_to_zero():
normalizer = IntegerNormalizer()
assert normalizer.normalize(10) == 0
assert not normalizer.distinguish(10, lambda n: True)
assert normalizer.normalize(10) == 0
def test_normalizing_can_be_made_to_distinguish_values():
normalizer = IntegerNormalizer()
assert normalizer.distinguish(10, lambda n: n >= 5)
assert normalizer.normalize(10) == 5
assert normalizer.normalize(4) == 0
def test_learning_large_dfa():
"""Mostly the thing this is testing is actually that this runs in reasonable
time. A naive breadth first search will run ~forever when trying to find this
because it will have to explore all strings of length 19 before it finds one
of length 20."""
learner = LStar(lambda s: len(s) == 20)
learner.learn(bytes(20))
for i, s in enumerate(itertools.islice(learner.dfa.all_matching_strings(), 500)):
assert len(s) == 20
assert i == int.from_bytes(s, "big")
def varint_predicate(b):
if not b:
return False
n = b[0] & 15
if len(b) != n + 1:
return False
value = int.from_bytes(b[1:], "big")
return value >= 10
@st.composite
def varint(draw):
result = bytearray()
result.append(draw(st.integers(1, 255)))
n = result[0] & 15
assume(n > 0)
value = draw(st.integers(10, 256**n - 1))
result.extend(value.to_bytes(n, "big"))
return bytes(result)
@example([b"\x02\x01\n"])
@given(st.lists(varint(), min_size=1))
def test_can_learn_varint_predicate(varints):
learner = LStar(varint_predicate)
prev = -1
while learner.generation != prev:
prev = learner.generation
for s in varints:
learner.learn(s)
for s in varints:
assert learner.dfa.matches(s)
def test_cannot_reuse_dfa():
x = LStar(lambda x: len(x) == 3)
dfa = x.dfa
x.learn(bytes(3))
with pytest.raises(InvalidState):
dfa.start
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