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2f86d9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | # Copyright 2026 DeepMind Technologies Limited.
#
# 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 text evaluation metrics."""
from unittest import mock
from absl.testing import absltest
from gemma.diffusion.hackable_diffusion_adapter.eval import text_metric
import jax.numpy as jnp
class DetokenizePromptAndResponseTest(absltest.TestCase):
@mock.patch("seqio.vocabularies.SentencePieceVocabulary")
def test_detokenize_prompt_and_response(self, mock_vocab):
mock_instance = mock_vocab.return_value
mock_instance.decode.side_effect = lambda t: f"d_{t}"
metric = text_metric.DetokenizePromptAndResponse(
prompt="batch.prompt",
response="batch.response",
tokenizer_path="fake_path",
num_texts=2,
separator=" | ",
)
prompt = jnp.array([[[1]], [[2]], [[3]]])
response = jnp.array([[[4]], [[5]], [[6]]])
state = metric.get_state(prompt=prompt, response=response)
state = state.finalize()
self.assertEqual(state.prompt.shape, (2, 1))
self.assertEqual(state.response.shape, (2, 1))
res = state.compute()
self.assertEqual(res, ["d_[1] | d_[4]", "d_[2] | d_[5]"])
@mock.patch("seqio.vocabularies.SentencePieceVocabulary")
def test_default_tokenizer_path(self, mock_vocab):
"""Default constructor should work without explicit tokenizer_path."""
mock_instance = mock_vocab.return_value
mock_instance.decode.side_effect = lambda t: f"d_{t}"
metric = text_metric.DetokenizePromptAndResponse(
prompt="batch.prompt",
response="batch.response",
num_texts=3,
separator=" ## ",
)
# Verify default tokenizer path is used.
self.assertIn("gemma4", metric.tokenizer_path)
self.assertEqual(metric.num_texts, 3)
self.assertEqual(metric.separator, " ## ")
if __name__ == "__main__":
absltest.main()
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