def test_case_1(self):
# empty fsa
array_size = k2host.IntArray2Size(0, 0)
fsa_in = k2host.Fsa.create_fsa_with_size(array_size)
indexes = torch.IntTensor([0, 1, 3, 6, 7])
data = torch.IntTensor([1, 2, 3, 4, 5, 6, 7])
labels_in = k2host.AuxLabels(indexes, data)
inverter = k2host.FstInverter(fsa_in, labels_in)
fsa_size = k2host.IntArray2Size()
aux_size = k2host.IntArray2Size()
inverter.get_sizes(fsa_size, aux_size)
self.assertEqual(aux_size.size1, 0)
self.assertEqual(aux_size.size2, 0)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
labels_out = k2host.AuxLabels.create_array_with_size(aux_size)
inverter.get_output(fsa_out, labels_out)
self.assertTrue(k2host.is_empty(fsa_out))
self.assertTrue(labels_out.empty())
def test_case_3(self):
# non-top-sorted input FSA
s = r'''
0 1 1 0
0 1 0 0
0 3 2 0
1 2 3 0
1 3 4 0
2 1 5 0
2 5 -1 0
3 1 6 0
4 5 -1 0
5
'''
fsa_in = k2host.str_to_fsa(s)
indexes = torch.IntTensor([0, 2, 3, 3, 6, 6, 7, 8, 10, 11])
data = torch.IntTensor([1, 2, 3, 5, 6, 7, 8, -1, 9, 10, -1])
labels_in = k2host.AuxLabels(indexes, data)
inverter = k2host.FstInverter(fsa_in, labels_in)
fsa_size = k2host.IntArray2Size()
aux_size = k2host.IntArray2Size()
inverter.get_sizes(fsa_size, aux_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
labels_out = k2host.AuxLabels.create_array_with_size(aux_size)
inverter.get_output(fsa_out, labels_out)
expected_arc_indexes = torch.IntTensor(
[0, 3, 4, 5, 7, 8, 9, 11, 12, 13, 13])
expected_arcs = torch.IntTensor([[0, 1, 1, 0], [0, 3, 3, 0],
[0, 7, 0, 0], [1, 3, 2, 0],
[2, 3, 10, 0], [3, 4, 5, 0],
[3, 7, 0, 0], [4, 5, 6, 0],
[5, 6, 7, 0], [6, 3, 8, 0],
[6, 9, -1, 0], [7, 2, 9, 0],
[8, 9, -1, 0]])
self.assertTrue(torch.equal(fsa_out.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(fsa_out.data, expected_arcs))
expected_label_indexes = torch.IntTensor(
[0, 0, 0, 1, 2, 3, 3, 4, 4, 5, 6, 7, 7, 8])
expected_labels = torch.IntTensor([2, 1, 6, 4, 3, 5, -1, -1])
self.assertTrue(torch.equal(labels_out.indexes,
expected_label_indexes))
self.assertTrue(torch.equal(labels_out.data, expected_labels))
def setUp(self):
indexes = torch.IntTensor([0, 1, 3, 6, 7])
data = torch.IntTensor([1, 2, 3, 4, 5, 6, 7])
self.aux_labels_in = k2host.AuxLabels(indexes, data)