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# Copyright 2022 The MT3 Authors.
#
# 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 vocabularies."""
from absl.testing import absltest
from mt3 import vocabularies
import numpy as np
import tensorflow.compat.v2 as tf
tf.compat.v1.enable_eager_execution()
class VocabulariesTest(absltest.TestCase):
def test_velocity_quantization(self):
self.assertEqual(0, vocabularies.velocity_to_bin(0, num_velocity_bins=1))
self.assertEqual(0, vocabularies.velocity_to_bin(0, num_velocity_bins=127))
self.assertEqual(0, vocabularies.bin_to_velocity(0, num_velocity_bins=1))
self.assertEqual(0, vocabularies.bin_to_velocity(0, num_velocity_bins=127))
self.assertEqual(
1,
vocabularies.velocity_to_bin(
vocabularies.bin_to_velocity(1, num_velocity_bins=1),
num_velocity_bins=1))
for velocity_bin in range(1, 128):
self.assertEqual(
velocity_bin,
vocabularies.velocity_to_bin(
vocabularies.bin_to_velocity(velocity_bin, num_velocity_bins=127),
num_velocity_bins=127))
def test_encode_decode(self):
vocab = vocabularies.GenericTokenVocabulary(32)
input_tokens = [1, 2, 3]
expected_encoded = [4, 5, 6]
# Encode
self.assertSequenceEqual(vocab.encode(input_tokens), expected_encoded)
np.testing.assert_array_equal(
vocab.encode_tf(tf.convert_to_tensor(input_tokens)).numpy(),
expected_encoded)
# Decode
self.assertSequenceEqual(vocab.decode(expected_encoded), input_tokens)
np.testing.assert_array_equal(
vocab.decode_tf(tf.convert_to_tensor(expected_encoded)).numpy(),
input_tokens)
def test_decode_invalid_ids(self):
vocab = vocabularies.GenericTokenVocabulary(32, extra_ids=4)
encoded = [0, 2, 3, 4, 34, 35]
expected_decoded = [-2, -2, 0, 1, 31, -2]
self.assertSequenceEqual(vocab.decode(encoded), expected_decoded)
np.testing.assert_array_equal(
vocab.decode_tf(tf.convert_to_tensor(encoded)).numpy(),
expected_decoded)
def test_decode_eos(self):
vocab = vocabularies.GenericTokenVocabulary(32)
encoded = [0, 2, 3, 4, 1, 0, 1, 0]
# Python decode function truncates everything after first EOS.
expected_decoded = [-2, -2, 0, 1, -1]
self.assertSequenceEqual(vocab.decode(encoded), expected_decoded)
# TF decode function preserves array length.
expected_decoded_tf = [-2, -2, 0, 1, -1, -1, -1, -1]
np.testing.assert_array_equal(
vocab.decode_tf(tf.convert_to_tensor(encoded)).numpy(),
expected_decoded_tf)
def test_encode_invalid_id(self):
vocab = vocabularies.GenericTokenVocabulary(32)
inputs = [0, 15, 31]
# No exception expected.
vocab.encode(inputs)
vocab.encode_tf(tf.convert_to_tensor(inputs))
inputs_too_low = [-1, 15, 31]
with self.assertRaises(ValueError):
vocab.encode(inputs_too_low)
with self.assertRaises(tf.errors.InvalidArgumentError):
vocab.encode_tf(tf.convert_to_tensor(inputs_too_low))
inputs_too_high = [0, 15, 32]
with self.assertRaises(ValueError):
vocab.encode(inputs_too_high)
with self.assertRaises(tf.errors.InvalidArgumentError):
vocab.encode_tf(tf.convert_to_tensor(inputs_too_high))
def test_encode_dtypes(self):
vocab = vocabularies.GenericTokenVocabulary(32)
inputs = [0, 15, 31]
encoded32 = vocab.encode_tf(tf.convert_to_tensor(inputs, tf.int32))
self.assertEqual(tf.int32, encoded32.dtype)
encoded64 = vocab.encode_tf(tf.convert_to_tensor(inputs, tf.int64))
self.assertEqual(tf.int64, encoded64.dtype)
if __name__ == '__main__':
absltest.main()
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