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test_nnf.py
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1107 lines (861 loc) · 31.9 KB
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import copy
import pathlib
import pickle
import platform
import shutil
import threading
import types
import typing as t
import pytest
from hypothesis import (assume, event, given, strategies as st, settings,
HealthCheck)
import nnf
from nnf import (Var, And, Or, amc, dimacs, dsharp, operators,
tseitin, complete_models, config, pysat, all_models)
memoized = [
method
for method in vars(nnf.NNF).values()
if isinstance(method, types.FunctionType) and hasattr(method, "memo")
]
assert memoized, "No memoized methods found, did the implementation change?"
for method in memoized:
method.set = lambda *args: None # type: ignore
settings.register_profile('patient', deadline=2000,
suppress_health_check=(HealthCheck.too_slow,))
settings.load_profile('patient')
a, b, c = Var('a'), Var('b'), Var('c')
fig1a = (~a & b) | (a & ~b)
fig1b = (~a | ~b) & (a | b)
satlib = pathlib.Path(__file__).parent / "testdata" / "satlib"
uf20 = [dsharp.load(file.open()) for file in (satlib / "uf20").glob("*.nnf")]
uf20_cnf = [
dimacs.load(file.open()) for file in (satlib / "uf20").glob("*.cnf")
] # type: t.List[And[Or[Var]]]
# Test config default value before the tests start mucking with the state
assert config.sat_backend == "auto"
def test_all_models_basic():
assert list(nnf.all_models([])) == [{}]
assert list(nnf.all_models([1])) == [{1: False}, {1: True}]
assert len(list(nnf.all_models(range(10)))) == 1024
@given(st.sets(st.integers(), max_size=8))
def test_all_models(names):
result = list(nnf.all_models(names))
# Proper result size
assert len(result) == 2**len(names)
# Only real names, only booleans
assert all(name in names and isinstance(value, bool)
for model in result
for name, value in model.items())
# Only complete models
assert all(len(model) == len(names)
for model in result)
# No duplicate models
assert len({tuple(model.items()) for model in result}) == len(result)
def test_basic():
assert a.satisfied_by(dict(a=True))
assert (a | b).satisfied_by(dict(a=False, b=True))
assert not (a & b).satisfied_by(dict(a=True, b=False))
assert (a & b).satisfiable()
assert not (a & ~a).satisfiable()
assert not (a & (~a & b)).satisfiable()
assert ((a | b) & (b | c)).satisfiable()
def test_amc():
assert amc.NUM_SAT(fig1a) == 2
assert amc.NUM_SAT(fig1b) == 4
assert amc.GRAD(a, {'a': 0.5}, 'a') == (0.5, 1)
names = st.integers(1, 8)
@st.composite
def variables(draw):
return Var(draw(names), draw(st.booleans()))
@st.composite
def booleans(draw):
return draw(st.sampled_from((nnf.true, nnf.false)))
@st.composite
def leaves(draw):
return draw(st.one_of(variables(), booleans()))
@st.composite
def terms(draw):
return And(Var(name, draw(st.booleans()))
for name in draw(st.sets(names)))
@st.composite
def clauses(draw):
return Or(Var(name, draw(st.booleans()))
for name in draw(st.sets(names)))
@st.composite
def DNF(draw):
return Or(draw(st.frozensets(terms())))
@st.composite
def CNF(draw):
sentence = And(draw(st.frozensets(clauses())))
return sentence
@st.composite
def models(draw):
return And(
Var(name, draw(st.booleans()))
for name in range(1, draw(st.integers(min_value=1, max_value=9)))
)
@st.composite
def MODS(draw):
num = draw(st.integers(min_value=1, max_value=9))
amount = draw(st.integers(min_value=0, max_value=10))
return Or(And(Var(name, draw(st.booleans()))
for name in range(1, num))
for _ in range(amount))
@st.composite
def internal(draw, children):
return draw(st.sampled_from((And, Or)))(draw(st.frozensets(children)))
@st.composite
def NNF(draw):
return draw(st.recursive(variables(), internal))
@st.composite
def DNNF(draw):
sentence = draw(NNF())
assume(sentence.decomposable())
return sentence
@given(DNF())
def test_hyp(sentence: nnf.Or):
assume(len(sentence.children) != 0)
assume(sentence.decomposable())
assert sentence.satisfiable()
assert sentence.vars() <= set(range(1, 9))
@given(MODS())
def test_MODS(sentence: nnf.Or):
assert sentence.smooth()
assert sentence.flat()
assert sentence.decomposable()
assert sentence.simply_conjunct()
@given(MODS())
def test_MODS_satisfiable(sentence: nnf.Or):
if len(sentence.children) > 0:
assert sentence.satisfiable()
else:
assert not sentence.satisfiable()
@pytest.fixture(scope='module', params=[True, False])
def merge_nodes(request):
return request.param
@given(sentence=DNNF())
def test_DNNF_sat_strategies(sentence: nnf.NNF, merge_nodes):
sat = sentence.satisfiable()
if sat:
assert sentence.simplify(merge_nodes) != nnf.false
assert amc.SAT(sentence)
event("Sentence satisfiable")
else:
assert sentence.simplify(merge_nodes) == nnf.false
assert not amc.SAT(sentence)
event("Sentence not satisfiable")
def test_amc_numsat():
for sentence in uf20:
assert (amc.NUM_SAT(sentence.make_smooth())
== len(list(sentence.models())))
@given(sentence=NNF())
def test_idempotent_simplification(sentence: nnf.NNF, merge_nodes):
sentence = sentence.simplify(merge_nodes)
assert sentence.simplify(merge_nodes) == sentence
@given(sentence=NNF())
def test_simplify_preserves_meaning(sentence: nnf.NNF, merge_nodes):
simple = sentence.simplify(merge_nodes)
assert sentence.equivalent(simple)
for model in sentence.models():
assert simple.satisfied_by(model)
for model in simple.models():
assert sentence.condition(model).simplify(merge_nodes) == nnf.true
@given(sentence=NNF())
def test_simplify_eliminates_bools(sentence: nnf.NNF, merge_nodes):
assume(sentence != nnf.true and sentence != nnf.false)
if any(node == nnf.true or node == nnf.false
for node in sentence.walk()):
event("Sentence contained booleans originally")
sentence = sentence.simplify(merge_nodes)
if sentence == nnf.true or sentence == nnf.false:
event("Sentence simplified to boolean")
else:
for node in sentence.walk():
assert node != nnf.true and node != nnf.false
@given(NNF())
def test_simplify_merges_internal_nodes(sentence: nnf.NNF):
if any(any(type(node) == type(child)
for child in node.children)
for node in sentence.walk()
if isinstance(node, nnf.Internal)):
event("Sentence contained immediately mergeable nodes")
# Nodes may also be merged after intermediate nodes are removed
for node in sentence.simplify().walk():
if isinstance(node, nnf.Internal):
for child in node.children:
assert type(node) != type(child)
@given(sentence=DNNF())
def test_simplify_solves_DNNF_satisfiability(sentence: nnf.NNF, merge_nodes):
if sentence.satisfiable():
event("Sentence is satisfiable")
assert sentence.simplify(merge_nodes) != nnf.false
else:
event("Sentence is not satisfiable")
assert sentence.simplify(merge_nodes) == nnf.false
def test_dimacs_sat_serialize():
# http://www.domagoj-babic.com/uploads/ResearchProjects/Spear/dimacs-cnf.pdf
sample_input = """c Sample SAT format
c
p sat 4
(*(+(1 3 -4)
+(4)
+(2 3)))
"""
assert dimacs.loads(sample_input) == And({
Or({Var(1), Var(3), ~Var(4)}),
Or({Var(4)}),
Or({Var(2), Var(3)})
})
@pytest.mark.parametrize(
'serialized, sentence',
[
('p sat 2\n(+((1)+((2))))', Or({Var(1), Or({Var(2)})}))
]
)
def test_dimacs_sat_weird_input(serialized: str, sentence: nnf.NNF):
assert dimacs.loads(serialized) == sentence
def test_dimacs_cnf_serialize():
sample_input = """c Example CNF format file
c
p cnf 4 3
1 3 -4 0
4 0 2
-3
"""
assert dimacs.loads(sample_input) == And({
Or({Var(1), Var(3), ~Var(4)}),
Or({Var(4)}),
Or({Var(2), ~Var(3)})
})
def test_dimacs_rejects_weird_digits():
with pytest.raises(dimacs.DecodeError):
dimacs.loads("p cnf 1 1\n¹ 0")
@given(NNF())
def test_arbitrary_dimacs_sat_serialize(sentence: nnf.NNF):
assert dimacs.loads(dimacs.dumps(sentence)) == sentence
# Removing spaces may change the meaning, but shouldn't make it invalid
# At least as far as our parser is concerned, a more sophisticated one
# could detect variables with too high names
serial = dimacs.dumps(sentence).split('\n')
serial[1] = serial[1].replace(' ', '')
dimacs.loads('\n'.join(serial))
@given(CNF())
def test_arbitrary_dimacs_cnf_serialize(sentence: And[Or[Var]]):
reloaded = dimacs.loads(dimacs.dumps(sentence, mode='cnf'))
assert reloaded.is_CNF()
assert reloaded == sentence
@given(NNF())
def test_dimacs_cnf_serialize_accepts_only_cnf(sentence: nnf.NNF):
if sentence.is_CNF():
event("CNF sentence")
dimacs.dumps(sentence, mode='cnf')
else:
event("Not CNF sentence")
with pytest.raises(dimacs.EncodeError):
dimacs.dumps(sentence, mode='cnf')
@pytest.mark.parametrize(
'fname, clauses',
[
('bf0432-007.cnf', 3667),
('sw100-1.cnf', 3100),
('uuf250-01.cnf', 1065),
('uf20-01.cnf', 90),
]
)
def test_cnf_benchmark_data(fname: str, clauses: int):
with (satlib / fname).open() as f:
sentence = dimacs.load(f)
assert isinstance(sentence, And) and len(sentence.children) == clauses
def test_dsharp_output():
with (satlib / "uf20-01.nnf").open() as f:
sentence = dsharp.load(f)
with (satlib / "uf20-01.cnf").open() as f:
clauses = dimacs.load(f)
assert sentence.decomposable()
# this is not a complete check, but clauses.models() is very expensive
assert all(clauses.satisfied_by(model) for model in sentence.models())
@given(NNF())
def test_walk_unique_nodes(sentence: nnf.NNF):
result = list(sentence.walk())
assert len(result) == len(set(result))
assert len(result) <= sentence.size() + 1
@given(st.dictionaries(st.integers(), st.booleans()))
def test_to_model(model: dict):
sentence = nnf.And(nnf.Var(k, v) for k, v in model.items())
assert sentence.to_model() == model
@given(DNNF())
def test_models_deterministic_sanity(sentence: nnf.NNF):
"""Running _models_deterministic() on a non-deterministic decomposable
sentence may return duplicate models but should not return unsatisfying
models and should return each satisfying model at least once.
"""
assert model_set(sentence._models_decomposable()) == model_set(
sentence._models_deterministic()
)
def test_models_deterministic_trivial():
assert list(nnf.true._models_deterministic()) == [{}]
assert list(nnf.false._models_deterministic()) == []
assert list(a._models_deterministic()) == [{"a": True}]
@pytest.mark.parametrize(
'sentence, size',
[
((a & b), 2),
(a & (a | b), 4),
((a | b) & (~a | ~b), 6),
(And({
Or({a, b}),
And({a, Or({a, b})}),
}), 6)
]
)
def test_size(sentence: nnf.NNF, size: int):
assert sentence.size() == size
@pytest.mark.parametrize(
'a, b, contradictory',
[
(a, ~a, True),
(a, b, False),
(a, a, False),
(a & b, a & ~b, True),
(a & (a | b), b, False),
(a & (a | b), ~a, True),
]
)
def test_contradicts(a: nnf.NNF, b: nnf.NNF, contradictory: bool):
assert a.contradicts(b) == contradictory
@given(NNF())
def test_false_contradicts_everything(sentence: nnf.NNF):
assert nnf.false.contradicts(sentence)
@given(DNNF())
def test_equivalent(sentence: nnf.NNF):
assert sentence.equivalent(sentence)
assert sentence.equivalent(sentence | nnf.false)
assert sentence.equivalent(sentence & (nnf.Var('A') | ~nnf.Var('A')))
if sentence.satisfiable():
assert not sentence.equivalent(sentence & nnf.false)
assert not sentence.equivalent(sentence & nnf.Var('A'))
else:
assert sentence.equivalent(sentence & nnf.false)
assert sentence.equivalent(sentence & nnf.Var('A'))
@given(NNF(), NNF())
def test_random_equivalent(a: nnf.NNF, b: nnf.NNF):
if a.vars() != b.vars():
if a.equivalent(b):
event("Equivalent, different vars")
assert b.equivalent(a)
for model in a.models():
assert b.condition(model).valid()
for model in b.models():
assert a.condition(model).valid()
else:
event("Not equivalent, different vars")
assert (any(not b.condition(model).valid()
for model in a.models()) or
any(not a.condition(model).valid()
for model in b.models()))
else:
if a.equivalent(b):
event("Equivalent, same vars")
assert b.equivalent(a)
assert model_set(a.models()) == model_set(b.models())
else:
event("Not equivalent, same vars")
assert model_set(a.models()) != model_set(b.models())
@given(NNF())
def test_smoothing(sentence: nnf.NNF):
if not sentence.smooth():
event("Sentence not smooth yet")
smoothed = sentence.make_smooth()
assert type(sentence) is type(smoothed)
assert smoothed.smooth()
assert sentence.equivalent(smoothed)
assert smoothed.make_smooth() == smoothed
else:
event("Sentence already smooth")
assert sentence.make_smooth() == sentence
def hashable_dict(model):
return frozenset(model.items())
def model_set(model_gen):
return frozenset(map(hashable_dict, model_gen))
def test_uf20_models():
for sentence in uf20:
assert sentence.decomposable()
m = list(sentence._models_decomposable())
models = model_set(m)
assert len(m) == len(models)
assert models == model_set(sentence._models_deterministic())
def test_instantiating_base_classes_fails():
with pytest.raises(TypeError):
nnf.NNF()
with pytest.raises(TypeError):
nnf.Internal()
with pytest.raises(TypeError):
nnf.Internal({nnf.Var(3)})
@given(NNF())
def test_negation(sentence: nnf.NNF):
n_vars = len(sentence.vars())
models_orig = model_set(sentence.models())
models_negated = model_set(sentence.negate().models())
assert len(models_orig) + len(models_negated) == 2**n_vars
assert len(models_orig | models_negated) == 2**n_vars
@given(NNF())
def test_model_counting(sentence: nnf.NNF):
assert sentence.model_count() == len(list(sentence.models()))
def test_uf20_model_counting():
for sentence in uf20:
nnf.NNF._deterministic_sentences.pop(id(sentence), None)
assert sentence.model_count() == len(list(sentence.models()))
sentence.mark_deterministic()
assert sentence.model_count() == len(list(sentence.models()))
@given(NNF())
def test_validity(sentence: nnf.NNF):
if sentence.valid():
event("Valid sentence")
assert all(sentence.satisfied_by(model)
for model in nnf.all_models(sentence.vars()))
else:
event("Invalid sentence")
assert any(not sentence.satisfied_by(model)
for model in nnf.all_models(sentence.vars()))
def test_uf20_validity():
for sentence in uf20:
nnf.NNF._deterministic_sentences.pop(id(sentence), None)
assert not sentence.valid()
sentence.mark_deterministic()
assert not sentence.valid()
@given(CNF())
def test_is_CNF(sentence: nnf.NNF):
assert sentence.is_CNF()
assert sentence.is_CNF(strict=True)
assert not sentence.is_DNF()
def test_is_CNF_examples():
assert And().is_CNF()
assert And().is_CNF(strict=True)
assert And({Or()}).is_CNF()
assert And({Or()}).is_CNF(strict=True)
assert And({Or({a, ~b})}).is_CNF()
assert And({Or({a, ~b})}).is_CNF(strict=True)
assert And({Or({a, ~b}), Or({c, ~c})}).is_CNF()
assert not And({Or({a, ~b}), Or({c, ~c})}).is_CNF(strict=True)
@given(DNF())
def test_is_DNF(sentence: nnf.NNF):
assert sentence.is_DNF()
assert sentence.is_DNF(strict=True)
assert not sentence.is_CNF()
def test_is_DNF_examples():
assert Or().is_DNF()
assert Or().is_DNF(strict=True)
assert Or({And()}).is_DNF()
assert Or({And()}).is_DNF(strict=True)
assert Or({And({a, ~b})}).is_DNF()
assert Or({And({a, ~b})}).is_DNF(strict=True)
assert Or({And({a, ~b}), And({c, ~c})}).is_DNF()
assert not Or({And({a, ~b}), And({c, ~c})}).is_DNF(strict=True)
@given(NNF())
def test_to_MODS(sentence: nnf.NNF):
assume(len(sentence.vars()) <= 5)
mods = sentence.to_MODS()
assert mods.is_MODS()
assert mods.is_DNF()
assert mods.is_DNF(strict=True)
assert mods.smooth()
assert isinstance(mods, Or)
assert mods.model_count() == len(mods.children)
@given(MODS())
def test_is_MODS(sentence: nnf.NNF):
assert sentence.is_MODS()
@given(NNF())
def test_pairwise(sentence: nnf.NNF):
new = sentence.make_pairwise()
assert new.equivalent(sentence)
if new not in {nnf.true, nnf.false}:
assert all(len(node.children) == 2
for node in new.walk()
if isinstance(node, nnf.Internal))
@given(NNF())
def test_implicates(sentence: nnf.NNF):
implicates = sentence.implicates()
assert implicates.equivalent(sentence)
assert implicates.is_CNF(strict=True)
assert not any(a.children < b.children
for a in implicates.children
for b in implicates.children)
@given(NNF())
def test_implicants(sentence: nnf.NNF):
implicants = sentence.implicants()
assert implicants.equivalent(sentence)
assert implicants.is_DNF()
assert not any(a.children < b.children
for a in implicants.children
for b in implicants.children)
@given(NNF())
def test_implicants_idempotent(sentence: nnf.NNF):
assume(len(sentence.vars()) <= 6)
implicants = sentence.implicants()
implicates = sentence.implicates()
assert implicants.implicants() == implicants
assert implicates.implicants() == implicants
@given(NNF())
def test_implicates_implicants_negation_rule(sentence: nnf.NNF):
"""Any implicate is also a negated implicant of the negated sentence.
.implicates() gives some implicates, and .implicants() gives all
implicants.
So sentence.negate().implicants().negate() gives all implicates,
and sentence.negate().implicates().negate() gives some implicants.
"""
assume(sentence.size() <= 30)
assert (
sentence.negate().implicants().negate().children
>= sentence.implicates().children
)
assert (
sentence.negate().implicates().negate().children
<= sentence.implicants().children
)
def test_implicates_implicants_negation_rule_example():
"""These failed an old version of the previous test. See issue #3."""
sentence = Or({And({~Var(1), Var(2)}), And({~Var(3), Var(1)})})
assert (
sentence.negate().implicants().negate().children
>= sentence.implicates().children
)
assert (
sentence.negate().implicates().negate().children
<= sentence.implicants().children
)
@given(NNF(), NNF())
def test_implies(a: nnf.NNF, b: nnf.NNF):
if a.implies(b):
event("Implication")
for model in a.models():
assert b.condition(model).valid()
else:
event("No implication")
assert any(not b.condition(model).valid()
for model in a.models())
def test_uf20_cnf_sat():
for sentence in uf20_cnf:
assert sentence.is_CNF()
assert sentence.satisfiable()
# It would be nice to compare .models() output to another algorithm
# But even 20 variables is too much
# So let's just hope that test_cnf_sat does enough
at_least_one = False
for model in sentence.models():
assert sentence.satisfied_by(model)
at_least_one = True
assert at_least_one
@given(NNF(), NNF())
def test_xor(a: nnf.NNF, b: nnf.NNF):
c = operators.xor(a, b)
for model in nnf.all_models(c.vars()):
assert (a.satisfied_by(model) ^ b.satisfied_by(model) ==
c.satisfied_by(model))
@given(NNF(), NNF())
def test_nand(a: nnf.NNF, b: nnf.NNF):
c = operators.nand(a, b)
for model in nnf.all_models(c.vars()):
assert ((a.satisfied_by(model) and b.satisfied_by(model)) !=
c.satisfied_by(model))
@given(NNF(), NNF())
def test_nor(a: nnf.NNF, b: nnf.NNF):
c = operators.nor(a, b)
for model in nnf.all_models(c.vars()):
assert ((a.satisfied_by(model) or b.satisfied_by(model)) !=
c.satisfied_by(model))
@given(NNF(), NNF())
def test_implies2(a: nnf.NNF, b: nnf.NNF):
c = operators.implies(a, b)
for model in nnf.all_models(c.vars()):
assert ((a.satisfied_by(model) and not b.satisfied_by(model)) !=
c.satisfied_by(model))
@given(NNF(), NNF())
def test_implied_by(a: nnf.NNF, b: nnf.NNF):
c = operators.implied_by(a, b)
for model in nnf.all_models(c.vars()):
assert ((b.satisfied_by(model) and not a.satisfied_by(model)) !=
c.satisfied_by(model))
@given(NNF(), NNF())
def test_iff(a: nnf.NNF, b: nnf.NNF):
c = operators.iff(a, b)
for model in nnf.all_models(c.vars()):
assert ((a.satisfied_by(model) == b.satisfied_by(model)) ==
c.satisfied_by(model))
@given(NNF())
def test_forget(sentence: nnf.NNF):
# Assumption to reduce the time in testing
assume(sentence.size() <= 15)
# Test that forgetting a backbone variable doesn't change the theory
T = sentence & Var('added_var')
assert sentence.equivalent(T.forget({'added_var'}))
# Test the tseitin projection
assert sentence.equivalent(sentence.to_CNF().forget_aux())
# Test that models of a projected theory are consistent with the original
names = list(sentence.vars())[:2]
T = sentence.forget(names)
assert not any([v in T.vars() for v in names])
for m in T.models():
assert sentence.condition(m).satisfiable()
@given(NNF())
def test_project(sentence: nnf.NNF):
# Test that we get the same as projecting and forgetting
assume(len(sentence.vars()) > 3)
vars1 = list(sentence.vars())[:2]
vars2 = list(sentence.vars())[2:]
assert sentence.forget(vars1).equivalent(sentence.project(vars2))
@given(NNF())
def test_pickling(sentence: nnf.NNF):
new = pickle.loads(pickle.dumps(sentence))
assert sentence == new
assert sentence is not new
assert sentence.object_count() == new.object_count()
@given(NNF())
def test_copying_does_not_copy(sentence: nnf.NNF):
assert sentence is copy.copy(sentence) is copy.deepcopy(sentence)
assert copy.deepcopy([sentence])[0] is sentence
if shutil.which('dsharp') is not None:
def test_dsharp_compile_uf20():
sentence = uf20_cnf[0]
compiled = dsharp.compile(sentence)
compiled_smooth = dsharp.compile(sentence, smooth=True)
assert sentence.equivalent(compiled)
assert sentence.equivalent(compiled_smooth)
assert compiled.decomposable()
assert compiled_smooth.decomposable()
assert compiled_smooth.smooth()
@given(CNF())
def test_dsharp_compile(sentence: And[Or[Var]]):
compiled = dsharp.compile(sentence)
compiled_smooth = dsharp.compile(sentence, smooth=True)
assert compiled.decomposable()
assert compiled_smooth.decomposable()
assert compiled_smooth.smooth()
if sentence.satisfiable(): # See nnf.dsharp.__doc__
assert sentence.equivalent(compiled)
assert sentence.equivalent(compiled_smooth)
@given(CNF())
def test_dsharp_compile_converting_names(sentence: And[Or[Var]]):
sentence = And(Or(Var(str(var.name), var.true) for var in clause)
for clause in sentence)
compiled = dsharp.compile(sentence)
assert all(isinstance(name, str) for name in compiled.vars())
if sentence.satisfiable():
assert sentence.equivalent(compiled)
def test_mark_deterministic():
s = And()
t = And()
assert not s.marked_deterministic()
assert not t.marked_deterministic()
s.mark_deterministic()
assert s.marked_deterministic()
assert not t.marked_deterministic()
t.mark_deterministic()
assert s.marked_deterministic()
assert t.marked_deterministic()
del s
assert t.marked_deterministic()
@given(NNF())
def test_tseitin(sentence: nnf.NNF):
# Assumption to reduce the time in testing
assume(sentence.size() <= 10)
T = tseitin.to_CNF(sentence)
assert T.is_CNF()
assert T.is_CNF(strict=True)
assert tseitin.to_CNF(T) == T
assert T.forget_aux().equivalent(sentence)
models = list(complete_models(T.models(), sentence.vars() | T.vars()))
for mt in models:
assert sentence.satisfied_by(mt)
assert len(models) == sentence.model_count()
@given(CNF())
def test_tseitin_preserves_CNF(sentence: And[Or[Var]]):
assert sentence.to_CNF() == sentence
def test_tseitin_required_detection():
assert a.to_CNF() == And({Or({a})})
assert And().to_CNF() == And()
assert Or().to_CNF() == And({Or()})
assert (a | b).to_CNF() == And({a | b})
assert And({a | b, b | c}).to_CNF() == And({a | b, b | c})
assert And({And({Or({And({~a})})})}).to_CNF() == And({Or({~a})})
@given(models())
def test_complete_models(model: nnf.And[nnf.Var]):
m = {v.name: v.true for v in model}
neg = {v.name: not v.true for v in model}
zero = list(complete_models([m], model.vars()))
assert len(zero) == 1
one = list(complete_models([m], model.vars() | {"test1"}))
assert len(one) == 2
two = list(complete_models([m], model.vars() | {"test1", "test2"}))
assert len(two) == 4
assert all(x.keys() == m.keys() | {"test1", "test2"} for x in two)
if m:
multi = list(
complete_models([m, neg], model.vars() | {"test1", "test2"})
)
assert len(multi) == 8
assert len({frozenset(x.items()) for x in multi}) == 8 # all unique
assert all(x.keys() == m.keys() | {"test1", "test2"} for x in multi)
if (platform.uname().system, platform.uname().machine) == ('Linux', 'x86_64'):
@config(sat_backend="kissat")
def test_kissat_uf20():
for sentence in uf20_cnf:
assert sentence.satisfiable()
@config(sat_backend="kissat")
@given(CNF())
def test_kissat_cnf(sentence: And[Or[Var]]):
assert sentence.satisfiable() == sentence._cnf_satisfiable_native()
@config(sat_backend="kissat")
@given(NNF())
def test_kissat_nnf(sentence: And[Or[Var]]):
assert (
sentence.satisfiable()
== tseitin.to_CNF(sentence)._cnf_satisfiable_native()
)
@config(sat_backend="auto")
def test_config():
assert config.sat_backend == "auto"
# Imperative style works
config.sat_backend = "native"
assert config.sat_backend == "native"
# Context manager works
with config(sat_backend="kissat"):
assert config.sat_backend == "kissat"
assert config.sat_backend == "native"
# Bad values are caught
with pytest.raises(ValueError):
config.sat_backend = "invalid"
# In context managers too, before we enter
with pytest.raises(ValueError):
with config(sat_backend="invalid"):
assert False
config.sat_backend = "kissat"
assert config.sat_backend == "kissat"
# Old value is restored when we leave, even if changed inside
# (this may or may not be desirable behavior, but if it changes
# we should know)
with config(sat_backend="native"):
assert config.sat_backend == "native"
config.sat_backend = "auto"
assert config.sat_backend == "auto"
assert config.sat_backend == "kissat"
# Bad settings are caught
with pytest.raises(AttributeError):
config.invalid = "somevalue"
# Decorator works
@config(sat_backend="native")
def somefunc(recurse=False):
assert config.sat_backend == "native"
if recurse:
# Even if we call it again while it's in progress
config.sat_backend = "auto"
somefunc(recurse=False)
assert config.sat_backend == "auto"
somefunc()
assert config.sat_backend == "kissat"
somefunc(recurse=True)
assert config.sat_backend == "kissat"
# Context managers can be reused and nested without getting confused
reentrant_cm = config(sat_backend="auto")
assert config.sat_backend == "kissat"
with reentrant_cm:
assert config.sat_backend == "auto"
config.sat_backend = "native"
with reentrant_cm:
assert config.sat_backend == "auto"
assert config.sat_backend == "native"
assert config.sat_backend == "kissat"
@config(sat_backend="auto")
def test_config_multithreading():
# Settings from one thread don't affect another
config.sat_backend = "native"