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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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # 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.
"""Unit tests for base evaluation metrics."""
import dataclasses
from absl.testing import absltest
from gemma.diffusion.hackable_diffusion_adapter.eval import base_metric
import jax.numpy as jnp
class MockTokenizer:
def decode(self, tokens: list[int]) -> str:
# Decode by converting positive tokens to strings, skipping pad/eos (-1)
return " ".join(str(t) for t in tokens if t >= 0)
@dataclasses.dataclass(kw_only=True, frozen=True)
class DummySimpleMetric(base_metric.BaseSimpleTextMetric):
# Override tokenizer to avoid loading heavy SentencePiece model in test.
@property
def tokenizer(self) -> MockTokenizer:
return MockTokenizer()
def score_example(self, generated_text: str, ground_truth_text: str) -> float:
# Score is 1.0 if generated matches ground truth, else 0.0
return 1.0 if generated_text == ground_truth_text else 0.0
class BaseMetricTest(absltest.TestCase):
def test_simple_text_metric_2d(self):
"""Test BaseSimpleTextMetric with 2D token inputs."""
metric = DummySimpleMetric(tokens="samples", ground_truth="batch.response")
# Batches of shape [B, L]
tokens = jnp.array([
[1, 2, 3, -1], # "1 2 3"
[4, 5, -1, -1], # "4 5"
])
ground_truth = jnp.array([
[1, 2, 3, -1], # "1 2 3" (Match)
[4, 6, -1, -1], # "4 6" (Mismatch)
])
state = metric.get_state(tokens=tokens, ground_truth=ground_truth)
computed = state.compute()
# Match gives 1.0, mismatch gives 0.0 -> average is 0.5
self.assertEqual(computed, 0.5)
def test_simple_text_metric_3d_squeezing(self):
"""Test that BaseSimpleTextMetric squeezes 3D [B, L, 1] inputs to 2D."""
metric = DummySimpleMetric(tokens="samples", ground_truth="batch.response")
# Batches of shape [B, L, 1]
tokens = jnp.array([
[[1], [2], [-1]], # "1 2"
[[3], [4], [-1]], # "3 4"
])
ground_truth = jnp.array([
[[1], [2], [-1]], # "1 2" (Match)
[[3], [4], [-1]], # "3 4" (Match)
])
state = metric.get_state(tokens=tokens, ground_truth=ground_truth)
computed = state.compute()
# Both match -> average is 1.0
self.assertEqual(computed, 1.0)
if __name__ == "__main__":
absltest.main()
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