chore: import upstream snapshot with attribution
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# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from unittest import mock
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from google.adk.evaluation.custom_metric_evaluator import _CustomMetricEvaluator
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from google.adk.evaluation.custom_metric_evaluator import _get_metric_function
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from google.adk.evaluation.eval_case import ConversationScenario
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from google.adk.evaluation.eval_case import Invocation
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from google.adk.evaluation.eval_metrics import EvalMetric
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from google.adk.evaluation.evaluator import EvaluationResult
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import pytest
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def my_sync_metric_function(
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eval_metric: EvalMetric,
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actual_invocations: list[Invocation],
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expected_invocations: list[Invocation] | None,
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conversation_scenario: ConversationScenario | None,
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) -> EvaluationResult:
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"""Sync metric function for testing."""
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return EvaluationResult(overall_score=1.0)
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async def my_async_metric_function(
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eval_metric: EvalMetric,
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actual_invocations: list[Invocation],
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expected_invocations: list[Invocation] | None,
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conversation_scenario: ConversationScenario | None,
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) -> EvaluationResult:
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"""Async metric function for testing."""
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return EvaluationResult(overall_score=0.5)
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@mock.patch("importlib.import_module")
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def test_get_metric_function_success(mock_import_module):
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"""Tests that _get_metric_function successfully returns a function."""
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mock_module = mock.MagicMock()
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mock_module.my_sync_metric_function = my_sync_metric_function
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mock_import_module.return_value = mock_module
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func = _get_metric_function(
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"test_custom_metric_evaluator.my_sync_metric_function"
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)
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assert func == my_sync_metric_function
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@mock.patch("importlib.import_module", side_effect=ImportError)
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def test_get_metric_function_module_not_found(mock_import_module):
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"""Tests that _get_metric_function raises ImportError for non-existent module."""
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with pytest.raises(ImportError):
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_get_metric_function("non_existent_module.my_sync_metric_function")
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@mock.patch("importlib.import_module")
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def test_get_metric_function_function_not_found(mock_import_module):
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"""Tests that _get_metric_function raises ImportError for non-existent function."""
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mock_import_module.return_value = object()
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with pytest.raises(ImportError):
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_get_metric_function(
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"google.adk.tests.unittests.evaluation.test_custom_metric_evaluator.non_existent_function"
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)
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def test_get_metric_function_malformed_path():
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"""Tests that _get_metric_function raises ImportError for malformed path."""
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with pytest.raises(ImportError):
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_get_metric_function("malformed_path")
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@mock.patch(
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"google.adk.evaluation.custom_metric_evaluator._get_metric_function",
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return_value=my_sync_metric_function,
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)
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@pytest.mark.asyncio
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async def test_custom_metric_evaluator_sync_function(mock_get_metric_function):
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"""Tests that _CustomMetricEvaluator works with a sync metric function."""
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eval_metric = EvalMetric(metric_name="sync_metric")
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evaluator = _CustomMetricEvaluator(
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eval_metric=eval_metric,
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custom_function_path="google.adk.tests.unittests.evaluation.test_custom_metric_evaluator.my_sync_metric_function",
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)
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result = await evaluator.evaluate_invocations([], None)
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assert result.overall_score == 1.0
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@mock.patch(
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"google.adk.evaluation.custom_metric_evaluator._get_metric_function",
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return_value=my_async_metric_function,
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)
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@pytest.mark.asyncio
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async def test_custom_metric_evaluator_async_function(mock_get_metric_function):
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"""Tests that _CustomMetricEvaluator works with an async metric function."""
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eval_metric = EvalMetric(metric_name="async_metric")
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evaluator = _CustomMetricEvaluator(
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eval_metric=eval_metric,
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custom_function_path="google.adk.tests.unittests.evaluation.test_custom_metric_evaluator.my_async_metric_function",
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)
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result = await evaluator.evaluate_invocations([], None)
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assert result.overall_score == 0.5
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