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# Copyright 2022 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for transformer.encoder."""
from absl.testing import absltest
from absl.testing import parameterized
from tracr.craft import bases
from tracr.transformer import encoder
_BOS_TOKEN = "bos_encoder_test"
_PAD_TOKEN = "pad_encoder_test"
class CategoricalEncoderTest(parameterized.TestCase):
def test_encode_raises_value_error_if_input_doesnt_start_with_bos(self):
vs = bases.VectorSpaceWithBasis.from_values("input", {1, 2, 3, _BOS_TOKEN})
basic_encoder = encoder.CategoricalEncoder(
vs.basis, enforce_bos=True, bos_token=_BOS_TOKEN)
with self.assertRaisesRegex(ValueError,
r"^.*First input token must be BOS token.*$"):
basic_encoder.encode([1, 1, 1])
def test_encode_raises_value_error_if_input_not_in_vocab(self):
vs = bases.VectorSpaceWithBasis.from_values("input", {1, 2, 3, _BOS_TOKEN})
basic_encoder = encoder.CategoricalEncoder(
vs.basis, enforce_bos=True, bos_token=_BOS_TOKEN)
with self.assertRaisesRegex(ValueError,
r"^.*Inputs .* not found in encoding.*$"):
basic_encoder.encode([_BOS_TOKEN, 1, 2, 3, 4])
def test_decode_raises_value_error_if_id_outside_of_vocab_size(self):
vs = bases.VectorSpaceWithBasis.from_values("input", {1, 2, _BOS_TOKEN})
basic_encoder = encoder.CategoricalEncoder(
vs.basis, enforce_bos=True, bos_token=_BOS_TOKEN)
with self.assertRaisesRegex(ValueError,
r"^.*Inputs .* not found in decoding map.*$"):
basic_encoder.decode([0, 1, 2, 3])
def test_encoder_raises_value_error_if_bos_not_in_basis(self):
vs = bases.VectorSpaceWithBasis.from_values("input", {1, 2, 3})
with self.assertRaisesRegex(ValueError,
r"^.*BOS token missing in encoding.*$"):
unused_basic_encoder = encoder.CategoricalEncoder(
vs.basis, bos_token=_BOS_TOKEN)
def test_encoder_raises_value_error_if_pad_not_in_basis(self):
vs = bases.VectorSpaceWithBasis.from_values("input", {1, 2, 3})
with self.assertRaisesRegex(ValueError,
r"^.*PAD token missing in encoding.*$"):
unused_basic_encoder = encoder.CategoricalEncoder(
vs.basis, pad_token=_PAD_TOKEN)
def test_encoder_encodes_bos_and_pad_tokens_as_expected(self):
vs = bases.VectorSpaceWithBasis.from_values(
"input", {1, 2, 3, _BOS_TOKEN, _PAD_TOKEN})
basic_encoder = encoder.CategoricalEncoder(
vs.basis, bos_token=_BOS_TOKEN, pad_token=_PAD_TOKEN)
self.assertEqual(
basic_encoder.encode([_BOS_TOKEN, _PAD_TOKEN]),
[basic_encoder.bos_encoding, basic_encoder.pad_encoding])
@parameterized.parameters([
dict(
vocab={1, 2, 3, _BOS_TOKEN}, # lexicographic order
inputs=[_BOS_TOKEN, 3, 2, 1],
expected=[3, 2, 1, 0]),
dict(
vocab={"a", "b", _BOS_TOKEN, "c"}, # lexicographic order
inputs=[_BOS_TOKEN, "b", "b", "c"],
expected=[2, 1, 1, 3]),
])
def test_tokens_are_encoded_in_lexicographic_order(self, vocab, inputs,
expected):
# Expect encodings to be assigned to ids according to a lexicographic
# ordering of the vocab
vs = bases.VectorSpaceWithBasis.from_values("input", vocab)
basic_encoder = encoder.CategoricalEncoder(
vs.basis, enforce_bos=True, bos_token=_BOS_TOKEN)
encodings = basic_encoder.encode(inputs)
self.assertEqual(encodings, expected)
@parameterized.parameters([
dict(vocab={_BOS_TOKEN, _PAD_TOKEN, 1, 2, 3}, expected=5),
dict(vocab={_BOS_TOKEN, _PAD_TOKEN, "a", "b"}, expected=4),
])
def test_vocab_size_has_expected_value(self, vocab, expected):
vs = bases.VectorSpaceWithBasis.from_values("input", vocab)
basic_encoder = encoder.CategoricalEncoder(
vs.basis, enforce_bos=True, bos_token=_BOS_TOKEN, pad_token=_PAD_TOKEN)
self.assertEqual(basic_encoder.vocab_size, expected)
@parameterized.parameters([
dict(
vocab={_BOS_TOKEN, _PAD_TOKEN, 1, 2, 3}, inputs=[_BOS_TOKEN, 3, 2,
1]),
dict(
vocab={_BOS_TOKEN, _PAD_TOKEN, "a", "b", "c"},
inputs=[_BOS_TOKEN, "b", "b", "c"]),
])
def test_decode_inverts_encode(self, vocab, inputs):
vs = bases.VectorSpaceWithBasis.from_values("input", vocab)
basic_encoder = encoder.CategoricalEncoder(
vs.basis, enforce_bos=True, bos_token=_BOS_TOKEN, pad_token=_PAD_TOKEN)
encodings = basic_encoder.encode(inputs)
recovered = basic_encoder.decode(encodings)
self.assertEqual(recovered, inputs)
if __name__ == "__main__":
absltest.main()
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