def test_batch_tensors_does_not_modify_list(self):
field = ProductionRuleField("S -> [NP, VP]", is_global_rule=True)
field.index(self.vocab)
padding_lengths = field.get_padding_lengths()
tensor_dict1 = field.as_tensor(padding_lengths)
field = ProductionRuleField("NP -> test", is_global_rule=True)
field.index(self.vocab)
padding_lengths = field.get_padding_lengths()
tensor_dict2 = field.as_tensor(padding_lengths)
tensor_list = [tensor_dict1, tensor_dict2]
assert field.batch_tensors(tensor_list) == tensor_list
def test_as_tensor_produces_correct_output(self):
field = ProductionRuleField("S -> [NP, VP]", is_global_rule=True)
field.index(self.vocab)
tensor_tuple = field.as_tensor(field.get_padding_lengths())
assert isinstance(tensor_tuple, tuple)
assert len(tensor_tuple) == 4
assert tensor_tuple[0] == "S -> [NP, VP]"
assert tensor_tuple[1] is True
assert_almost_equal(tensor_tuple[2].detach().cpu().numpy(),
[self.s_rule_index])
field = ProductionRuleField("S -> [NP, VP]", is_global_rule=False)
field.index(self.vocab)
tensor_tuple = field.as_tensor(field.get_padding_lengths())
assert isinstance(tensor_tuple, tuple)
assert len(tensor_tuple) == 4
assert tensor_tuple[0] == "S -> [NP, VP]"
assert tensor_tuple[1] is False
assert tensor_tuple[2] is None
def test_padding_lengths_are_computed_correctly(self):
field = ProductionRuleField("S -> [NP, VP]", is_global_rule=True)
field.index(self.vocab)
assert field.get_padding_lengths() == {}
def test_index_converts_field_correctly(self):
field = ProductionRuleField("S -> [NP, VP]", is_global_rule=True)
field.index(self.vocab)
assert field._rule_id == self.s_rule_index