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vllm-project--vllm/tests/transformers_utils/processors/test_voxtral.py
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chore: import upstream snapshot with attribution
2026-07-13 12:55:37 +08:00

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Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Unit tests for ``MistralCommonFeatureExtractor.fetch_audio``.
``transformers>=5.10`` adds a ``ProcessorMixin.prepare_inputs_layout`` helper
that calls ``self.feature_extractor.fetch_audio(...)`` unconditionally. The
duck-typed :class:`MistralCommonFeatureExtractor` previously did not implement
that method, so loading any voxtral model under transformers 5.10.x raised
``AttributeError: 'MistralCommonFeatureExtractor' object has no attribute
'fetch_audio'``. These tests pin the new ``fetch_audio`` method to the same
contract as ``transformers.SequenceFeatureExtractor.fetch_audio``.
"""
import numpy as np
import pytest
import torch
from vllm.tokenizers.mistral import MistralTokenizer
from vllm.transformers_utils.processors.voxtral import (
MistralCommonFeatureExtractor,
)
@pytest.fixture(scope="module")
def feature_extractor() -> MistralCommonFeatureExtractor:
tokenizer = MistralTokenizer.from_pretrained("mistralai/Voxtral-Mini-3B-2507")
return MistralCommonFeatureExtractor(tokenizer.instruct.audio_encoder)
@pytest.mark.parametrize(
"audio",
[
np.zeros(1024, dtype=np.float32),
torch.zeros(1024),
[0.0, 1.0, 2.0],
],
ids=["numpy_array", "torch_tensor", "list_of_floats"],
)
def test_fetch_audio_passes_through(
feature_extractor: MistralCommonFeatureExtractor, audio
):
result = feature_extractor.fetch_audio(audio)
assert result is audio
def test_fetch_audio_recurses_over_list_of_arrays(
feature_extractor: MistralCommonFeatureExtractor,
):
a = np.zeros(8, dtype=np.float32)
b = np.ones(8, dtype=np.float32)
result = feature_extractor.fetch_audio([a, b])
assert isinstance(result, list)
assert len(result) == 2
assert result[0] is a
assert result[1] is b
def test_fetch_audio_uses_self_sampling_rate_when_none(
monkeypatch, feature_extractor: MistralCommonFeatureExtractor
):
"""If ``sampling_rate`` is None, ``self.sampling_rate`` must be used.
Verified indirectly via the recursion path: when we pass a list of arrays
without sampling_rate, recursive calls receive the resolved rate.
"""
captured: list[int | None] = []
original = feature_extractor.fetch_audio
def spy(audio, sampling_rate=None):
captured.append(sampling_rate)
return original(audio, sampling_rate=sampling_rate)
monkeypatch.setattr(feature_extractor, "fetch_audio", spy)
feature_extractor.fetch_audio([np.zeros(4, dtype=np.float32)])
# Top-level call has sampling_rate=None; inner recursive call sees the
# resolved rate from self.sampling_rate.
assert captured[0] is None
assert captured[1] == 16000
def test_fetch_audio_explicit_sampling_rate_propagates(
monkeypatch, feature_extractor: MistralCommonFeatureExtractor
):
captured: list[int | None] = []
original = feature_extractor.fetch_audio
def spy(audio, sampling_rate=None):
captured.append(sampling_rate)
return original(audio, sampling_rate=sampling_rate)
monkeypatch.setattr(feature_extractor, "fetch_audio", spy)
feature_extractor.fetch_audio([np.zeros(4, dtype=np.float32)], sampling_rate=8000)
assert captured[0] == 8000
assert captured[1] == 8000
def test_fetch_audio_rejects_unsupported_type(
feature_extractor: MistralCommonFeatureExtractor,
):
with pytest.raises(TypeError, match="only a numpy array"):
feature_extractor.fetch_audio(42) # type: ignore[arg-type]
def test_fetch_audio_str_delegates_to_load_audio(
monkeypatch, feature_extractor: MistralCommonFeatureExtractor
):
"""A str input must round-trip through ``transformers.audio_utils.load_audio``.
We monkey-patch ``load_audio`` so the test stays offline (no real URL/path
fetched) and still asserts the delegation contract.
"""
sentinel = np.array([0.5, -0.5], dtype=np.float32)
received: dict[str, object] = {}
def fake_load_audio(path, sampling_rate=None):
received["path"] = path
received["sampling_rate"] = sampling_rate
return sentinel
import transformers.audio_utils
monkeypatch.setattr(transformers.audio_utils, "load_audio", fake_load_audio)
result = feature_extractor.fetch_audio("/tmp/fake.wav")
assert result is sentinel
assert received["path"] == "/tmp/fake.wav"
assert received["sampling_rate"] == 16000