def test_autoregressive_inputs_packed(self):
x = tf.constant([3, 8, 1, 9, 1, 5, 4, 1, 0, 0])
sequence_id = tf.constant([1, 1, 1, 2, 2, 3, 3, 3, 0, 0])
autoreg_inputs = feature_converters.autoregressive_inputs(
x, sequence_id=sequence_id)
actual = self.evaluate(autoreg_inputs)
expected = [0, 3, 8, 0, 9, 0, 5, 4, 0, 0]
self.assertAllEqual(actual, expected)
def test_autoregressive_inputs_packed_non_eos(self):
# In the correct input format, x[4] should have been 1 (EOS).
x = tf.constant([3, 8, 1, 9, 6, 5, 4, 1, 0, 0])
# sequence_id is correctly formated.
sequence_id = tf.constant([1, 1, 1, 2, 2, 3, 3, 3, 0, 0])
autoreg_inputs = feature_converters.autoregressive_inputs(
x, sequence_id=sequence_id)
actual = self.evaluate(autoreg_inputs)
# The incorrect x[4] should not affect the output as long as the sequence_id
# is correct.
expected = [0, 3, 8, 0, 9, 0, 5, 4, 0, 0]
self.assertAllEqual(actual, expected)
def test_autoregressive_inputs_different_dtypes(self):
x = tf.constant([3, 8, 1, 9, 1, 5, 4, 1, 0, 0])
sequence_id = tf.constant([1, 1, 1, 2, 2, 3, 3, 3, 0, 0], tf.int32)
autoreg_inputs = feature_converters.autoregressive_inputs(
x, sequence_id=sequence_id, output_dtype=tf.int64)
self.assertEqual(autoreg_inputs.dtype, tf.int64)
def test_autoregressive_inputs_unpacked(self):
x = tf.constant([3, 8, 9, 5, 1, 0, 0])
autoreg_inputs = feature_converters.autoregressive_inputs(x)
actual = self.evaluate(autoreg_inputs)
expected = [0, 3, 8, 9, 5, 1, 0]
self.assertAllEqual(actual, expected)