def test_minimal_reference_with_simple_attn():
tokens = [
'[CLS]', 'for', 'the', '_MATH_', '-', 'th', 'disc', 're', 'pan', 'cy',
'have', '_MATHDISP_', ',', 'where', '_MATH_', ',', '_MATH_', ',',
'_MATH_'
]
words = [
'[CLS]', 'For', 'the', '_MATH_-th', 'discrepancy', 'have',
'_MATHDISP_', ',', 'where', '_MATH_', ',', '_MATH_', ',', '_MATH_'
]
word_ends = [
'[CLS]', 'for', 'the', 'th', 'cy', 'have', '_MATHDISP_', ',', 'where',
'_MATH_', ',', '_MATH_', ',', '_MATH_'
]
saliency = np.ones((len(tokens)), dtype=np.float32)
attention = [saliency for _ in range(len(tokens))]
attention = np.array(attention, dtype=np.float32)
merged = reference.merge(attention, tokens, words, word_ends)
expected = np.array([[1, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1]
for _ in range(len(words))],
dtype=np.float32)
np.testing.assert_allclose(merged, expected)
for row1, row2 in zip(merged, merged[1:]):
np.testing.assert_allclose(row1, row2)
def test_simple():
tokens = ["AB"]
words = ["A", "B"]
word_ends = ["AB", "AB"]
attention = np.array([[1]], dtype=np.float32)
merged = merge(attention, tokens, words, word_ends)
expected = np.array([[1, 0], [0, 0]], dtype=np.float32)
np.testing.assert_allclose(merged, expected)
def test_3x3():
tokens = ["A", "B", "C"]
words = ["A", "B", "C"]
word_ends = ["A", "B", "C"]
attention = np.ones((3, 3), dtype=np.float32)
merged = reference.merge(attention, tokens, words, word_ends)
np.testing.assert_allclose(merged, attention)
merged = merge(attention, tokens, words, word_ends)
np.testing.assert_allclose(merged, attention)
def test_minimal_example_with_repeat():
tokens = ['a', 'b', 'a']
words = ['ab', 'a']
word_ends = ['b', 'a']
attention = np.ones((len(tokens), len(tokens)), dtype=np.float32)
merged = reference.merge(attention, tokens, words, word_ends, verbosity=2)
expected = np.array([[2, 1] for _ in range(len(words))], dtype=np.float32)
np.testing.assert_allclose(merged, expected)
def test_unbalanced():
tokens = ["straw", "##berries"]
words = ["strawberries"]
word_ends = ["##berries"]
attention = np.array([[0.2, 0.8], [0.2, 0.8]], dtype=np.float32)
merged = merge(attention, tokens, words, word_ends)
expected = np.array([[1.0]])
np.testing.assert_allclose(merged, expected)
merged = reference.merge(attention, tokens, words, word_ends)
expected = np.array([[1.0]])
np.testing.assert_allclose(merged, expected)
def test_near_zero():
tokens = ["A", "B", "C"]
words = ["A", "B", "C"]
word_ends = ["A", "B", "C"]
attention = np.array(
[[1e-16, 1e-16, 1e-16], [1e-16, 1e-16, 1e-16], [1e-16, 1e-16, 1e-16]],
dtype=np.float32,
)
merged = reference.merge(attention, tokens, words, word_ends)
np.testing.assert_allclose(merged, attention)
merged = merge(attention, tokens, words, word_ends)
np.testing.assert_allclose(merged, attention)
def test_the_smallest_so_far_reference():
tokens = ['_MATH_', 'th', '_MATH_']
words = ['_MATH_th', '_MATH_']
word_ends = ['th', '_MATH_']
attention = np.ones((len(tokens), len(tokens)), dtype=np.float32)
merged = reference.merge(attention, tokens, words, word_ends)
expected = np.array([[2, 1] for _ in range(len(words))], dtype=np.float32)
np.testing.assert_allclose(merged, expected)
for row1, row2 in zip(merged, merged[1:]):
np.testing.assert_allclose(row1, row2)
def test_against_reference(args):
attn, tokens, word_ends = args
np.testing.assert_allclose(merge(attn, tokens, word_ends, word_ends),
reference.merge(attn, tokens, word_ends,
word_ends),
atol=1e-16)
