def test_supported_fingerprinting(cls, monkeypatch):
# patch so we can instantiate various solvers without the proper libraries
monkeypatch.setitem(sys.modules, "scipy", Mock())
monkeypatch.setitem(sys.modules, "scipy.optimize", Mock())
monkeypatch.setitem(sys.modules, "sklearn", Mock())
monkeypatch.setitem(sys.modules, "sklearn.linear_model", Mock())
monkeypatch.setitem(sys.modules, "sklearn.utils", Mock())
monkeypatch.setitem(sys.modules, "sklearn.utils.extmath", Mock())
obj = cls()
assert Fingerprint.supports(obj)
# check fingerprint is created without error and is a valid sha1 hash
fp = str(Fingerprint(obj))
assert len(fp) == 40 and set(fp).issubset("0123456789abcdef")
def test_supported_fingerprinting(cls, monkeypatch):
# patch so we can instantiate various solvers without the proper libraries
monkeypatch.setitem(sys.modules, "scipy", Mock())
monkeypatch.setitem(sys.modules, "scipy.optimize", Mock())
monkeypatch.setitem(sys.modules, "sklearn", Mock())
monkeypatch.setitem(sys.modules, "sklearn.linear_model", Mock())
monkeypatch.setitem(sys.modules, "sklearn.utils", Mock())
monkeypatch.setitem(sys.modules, "sklearn.utils.extmath", Mock())
args = []
if issubclass(cls, nengo.neurons.RatesToSpikesNeuronType):
# spiking types require a `base_type` argument, so provide one
args.append(nengo.neurons.RectifiedLinear())
obj = cls(*args)
assert Fingerprint.supports(obj)
# check fingerprint is created without error and is a valid sha1 hash
fp = str(Fingerprint(obj))
assert len(fp) == 40 and set(fp).issubset("0123456789abcdef")