Sync from v0.13

tests/models/multimodal/processing/test_audioflamingo3.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 NVIDIA CORPORATION and the HuggingFace Inc. team. 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.
+
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+import torch
+from transformers import PretrainedConfig
+
+from tests.models.registry import HF_EXAMPLE_MODELS
+
+
+class MockAudioFlamingo3Config(PretrainedConfig):
+    model_type = "audioflamingo3"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.audio_config = PretrainedConfig()
+        self.text_config = PretrainedConfig()
+
+
+class MockAudioFlamingo3Processor:
+    def __init__(self):
+        self.audio_token = "<sound>"
+        self.audio_token_id = 12345
+        self.feature_extractor = MockFeatureExtractor()
+
+    def __call__(self, text=None, audios=None, **kwargs):
+        return {"input_ids": [1, 2, 3], "input_features": [np.zeros((3000, 80))]}
+
+
+class MockFeatureExtractor:
+    def __init__(self):
+        self.sampling_rate = 16000
+        self.chunk_length = 30
+
+
+@pytest.fixture
+def mock_ctx():
+    config = MockAudioFlamingo3Config()
+
+    ctx = MagicMock()
+    ctx.get_hf_config.return_value = config
+    ctx.get_hf_processor.return_value = MockAudioFlamingo3Processor()
+    ctx.model_config.hf_config = config
+    return ctx
+
+
+@pytest.fixture(autouse=True)
+def check_transformers_version():
+    # Check if the model is supported by the current transformers version
+    model_info = HF_EXAMPLE_MODELS.get_hf_info("AudioFlamingo3ForConditionalGeneration")
+    model_info.check_transformers_version(on_fail="skip")
+
+
+def test_audio_chunk_counting(mock_ctx):
+    from vllm.model_executor.models.audioflamingo3 import (
+        AudioFlamingo3DummyInputsBuilder,
+        AudioFlamingo3MultiModalProcessor,
+        AudioFlamingo3ProcessingInfo,
+    )
+
+    info = AudioFlamingo3ProcessingInfo(mock_ctx)
+    processor = AudioFlamingo3MultiModalProcessor(
+        info, AudioFlamingo3DummyInputsBuilder(info)
+    )
+
+    sr = 16000
+    audio_1 = np.zeros(30 * sr)
+    audio_2 = np.zeros(45 * sr)
+
+    mm_data = {"audio": [audio_1, audio_2]}
+    prompt = "<|user|>Listen.<|end|>"
+
+    from vllm.multimodal.processing import BaseMultiModalProcessor
+
+    def mock_base_call(self, prompt, mm_data, mm_kwargs, tok_kwargs):
+        return {"input_ids": [1, 2, 3], "input_features": torch.randn(1, 80, 3000)}
+
+    with pytest.MonkeyPatch.context() as mp:
+        mp.setattr(BaseMultiModalProcessor, "_call_hf_processor", mock_base_call)
+
+        processed = processor._call_hf_processor(prompt, mm_data, {}, {})
+
+        chunk_counts = processed["chunk_counts"]
+
+        assert chunk_counts[0].item() == 1
+        assert chunk_counts[1].item() == 2
+        assert len(chunk_counts) == 2
+
+
+def test_dummy_data_generation(mock_ctx):
+    from vllm.model_executor.models.audioflamingo3 import (
+        AudioFlamingo3DummyInputsBuilder,
+        AudioFlamingo3ProcessingInfo,
+    )
+
+    info = AudioFlamingo3ProcessingInfo(mock_ctx)
+    builder = AudioFlamingo3DummyInputsBuilder(info)
+
+    mm_counts = {"audio": 2}
+    dummy_data = builder.get_dummy_mm_data(100, mm_counts, None)
+
+    assert "audio" in dummy_data
+    assert len(dummy_data["audio"]) == 2
+
+    expected_len = 600 * 16000
+    assert len(dummy_data["audio"][0]) == expected_len