def test_registering_a_Random_is_idempotent():
gc_on_pypy()
n_registered = len(entropy.RANDOMS_TO_MANAGE)
r = random.Random()
register_random(r)
register_random(r)
assert len(entropy.RANDOMS_TO_MANAGE) == n_registered + 1
del r
gc_on_pypy()
assert len(entropy.RANDOMS_TO_MANAGE) == n_registered
def test_registered_Random_is_seeded_by_random_module_strategy():
r = random.Random()
register_random(r)
state = r.getstate()
results = set()
count = [0]
@given(st.integers())
def inner(x):
results.add(r.random())
count[0] += 1
inner()
assert count[0] > len(results) * 0.9, "too few unique random numbers"
assert state == r.getstate()
def test_manages_registered_Random_instance():
r = random.Random()
register_random(r)
state = r.getstate()
result = []
@given(st.integers())
def inner(x):
v = r.random()
if result:
assert v == result[0]
else:
result.append(v)
inner()
assert state == r.getstate()
def test_registered_Random_is_seeded_by_random_module_strategy():
r = random.Random()
register_random(r)
state = r.getstate()
results = set()
count = [0]
@given(st.integers())
def inner(x):
results.add(r.random())
count[0] += 1
inner()
assert count[0] > len(results) * 0.9, "too few unique random numbers"
assert state == r.getstate()
entropy.RANDOMS_TO_MANAGE.remove(r)
assert r not in entropy.RANDOMS_TO_MANAGE
def test_manages_registered_Random_instance():
r = random.Random()
register_random(r)
state = r.getstate()
result = []
@given(st.integers())
def inner(x):
v = r.random()
if result:
assert v == result[0]
else:
result.append(v)
inner()
assert state == r.getstate()
entropy.RANDOMS_TO_MANAGE.remove(r)
assert r not in entropy.RANDOMS_TO_MANAGE
def test_manages_registered_Random_instance():
r = random.Random()
register_random(r)
state = r.getstate()
result = []
@given(st.integers())
def inner(x):
v = r.random()
if result:
assert v == result[0]
else:
result.append(v)
inner()
assert state == r.getstate()
entropy.RANDOMS_TO_MANAGE.remove(r)
assert r not in entropy.RANDOMS_TO_MANAGE
def test_evil_prng_registration_nonsense():
gc_on_pypy()
n_registered = len(entropy.RANDOMS_TO_MANAGE)
r1, r2, r3 = random.Random(1), random.Random(2), random.Random(3)
s2 = r2.getstate()
# We're going to be totally evil here: register two randoms, then
# drop one and add another, and finally check that we reset only
# the states that we collected before we started
register_random(r1)
k = max(entropy.RANDOMS_TO_MANAGE) # get a handle to check if r1 still exists
register_random(r2)
assert len(entropy.RANDOMS_TO_MANAGE) == n_registered + 2
with deterministic_PRNG(0):
del r1
gc_on_pypy()
assert k not in entropy.RANDOMS_TO_MANAGE, "r1 has been garbage-collected"
assert len(entropy.RANDOMS_TO_MANAGE) == n_registered + 1
r2.seed(4)
register_random(r3)
r3.seed(4)
s4 = r3.getstate()
# Implicit check, no exception was raised in __exit__
assert r2.getstate() == s2, "reset previously registered random state"
assert r3.getstate() == s4, "retained state when registered within the context"
from pathlib import Path
import hypothesis
import numpy as np
import torch
class TorchRandomState:
def getstate(self) -> np.ndarray:
return torch.get_rng_state().numpy()
def setstate(self, state: np.ndarray) -> None:
return torch.set_rng_state(torch.from_numpy(state))
def seed(self, seed) -> None:
torch.manual_seed(seed)
hypothesis.register_random(TorchRandomState())
_here = Path(__file__).parent
TEST_DATA_ROOT = _here / "data"
def test_registering_a_Random_is_idempotent():
r = random.Random()
register_random(r)
register_random(r)
assert entropy.RANDOMS_TO_MANAGE.pop() is r
assert r not in entropy.RANDOMS_TO_MANAGE
def test_cannot_register_non_Random():
with pytest.raises(InvalidArgument):
register_random("not a Random instance")
if sys.version_info >= (3, 6):
ORDERED_DICTS = True
else:
# Ordered dict (and **kwargs) not available with Python<3.6
ORDERED_DICTS = False
try:
from hypothesis import register_random
except ImportError: # pragma: no cover
pass
else:
# On recent versions of Hypothesis, make the Trio scheduler deterministic
# even though it uses a module-scoped Random instance. This works
# regardless of whether or not the random_module strategy is used.
register_random(trio._core._run._r)
# We also have to enable determinism, which is disabled by default
# due to a small performance impact - but fine to enable in testing.
# See https://github.com/python-trio/trio/pull/890/ for details.
trio._core._run._ALLOW_DETERMINISTIC_SCHEDULING = True
def pytest_addoption(parser):
parser.addini(
"trio_mode",
"should pytest-trio handle all async functions?",
type="bool",
default=False,
)
def test_registering_a_Random_is_idempotent():
r = random.Random()
register_random(r)
register_random(r)
assert entropy.RANDOMS_TO_MANAGE.pop() is r
assert r not in entropy.RANDOMS_TO_MANAGE
def test_cannot_register_non_Random():
with pytest.raises(InvalidArgument):
register_random("not a Random instance")
