def test_happy_path(self): decoder = DFS(MockSampler()) results = list(decoder("x0")) assert results == [ Result("0", 3.0, True, 1), Result("10", 5.0, True, 1), Result("110", 7.0, True, 1), Result("20", 4.0, True, 1), Result("210", 6.0, True, 1) ]
def __call__(self, input: Dict[str, Any], n_required_output=None): for i, value in enumerate(self.values): time.sleep(2) yield Result(value, 1.0 / (i + 1), True, 1)
def __call__(self, input, n_required_output=None): n_required_output = n_required_output or 1 for _ in range(n_required_output): yield Result(input["value"], 0, True, 1)
def test_abort(self): decoder = MockBeamSearch(3, 2) results = list(decoder("".join([" "] * 100))) assert [Result("0", -1.0, True, 1)] == results
def test_not_finished_output(self): decoder = MockBeamSearch(3, 2, False) results = list(decoder("x0")) assert [Result("0", -1.0, False, 1), Result("x0", 0.0, True, 1), Result("00", -1.0, True, 1), Result("10", -2.0, True, 1) ] == results
def test_happy_path(self): decoder = MockBeamSearch(3, 100) results = list(decoder("x0")) assert [Result("0", -1.0, True, 1), Result("x0", 0.0, True, 1), Result("10", -2.0, True, 1)] == results
def __call__(self, input: Environment, n_required_output=None): y = self.model(input)["y"] for _ in range(n_required_output): out = int(torch.normal(mean=y, std=5).item()) yield Result(out, torch.abs(out - y).item(), True, 1)
def __call__(self, input: Environment, n_required_output=None): for i, ast in enumerate(self.asts): yield Result(ast, 1.0 / (i + 1), True, 1)