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86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
import importlib.util
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import sys
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from pathlib import Path
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from types import ModuleType
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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def _optional_dependency_stubs():
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torchaudio = ModuleType("torchaudio")
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torchaudio.__path__ = []
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transforms = ModuleType("torchaudio.transforms")
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torchaudio.transforms = transforms
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unsloth = ModuleType("unsloth")
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unsloth.FastLanguageModel = MagicMock()
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snac = ModuleType("snac")
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snac.SNAC = MagicMock()
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return {
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"torchaudio": torchaudio,
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"torchaudio.transforms": transforms,
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"unsloth": unsloth,
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"snac": snac,
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}
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OPTIONAL_DEPENDENCY_STUBS = _optional_dependency_stubs()
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INFERENCE_PATH = Path(__file__).with_name("inference.py")
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SPEC = importlib.util.spec_from_file_location("orpheus_inference_under_test", INFERENCE_PATH)
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INFERENCE_MODULE = importlib.util.module_from_spec(SPEC)
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# Keep heavyweight optional dependencies local to this import. patch.dict
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# restores every prior sys.modules entry immediately after inference.py loads.
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with patch.dict(sys.modules, OPTIONAL_DEPENDENCY_STUBS):
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SPEC.loader.exec_module(INFERENCE_MODULE)
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OrpheusInference = INFERENCE_MODULE.OrpheusInference
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class DummyInference(OrpheusInference):
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def __init__(self):
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self.snac_model = MagicMock()
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@pytest.mark.parametrize("tail_length", range(7))
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def test_redistribute_codes_discards_trailing_incomplete_frame(tail_length):
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dummy = DummyInference()
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expected_audio = torch.ones(1, 1, 4)
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dummy.snac_model.decode.return_value = expected_audio
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# One valid SNAC frame followed by zero to six incomplete-frame codes.
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valid_frame = [1, 4098, 8195, 12292, 16389, 20486, 24583]
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audio = dummy._redistribute_codes(valid_frame + [999] * tail_length)
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assert audio is expected_audio
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dummy.snac_model.decode.assert_called_once()
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codes = dummy.snac_model.decode.call_args.args[0]
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assert [tensor.tolist() for tensor in codes] == [
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[[1]],
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[[2, 5]],
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[[3, 4, 6, 7]],
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]
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@pytest.mark.parametrize("incomplete_length", range(1, 7))
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def test_redistribute_codes_returns_silence_for_only_incomplete_codes(incomplete_length):
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dummy = DummyInference()
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audio = dummy._redistribute_codes([999] * incomplete_length)
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assert tuple(audio.shape) == (1, 1, 1000)
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assert torch.count_nonzero(audio).item() == 0
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dummy.snac_model.decode.assert_not_called()
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@pytest.mark.parametrize(
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("module_name", "stub"),
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OPTIONAL_DEPENDENCY_STUBS.items(),
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ids=OPTIONAL_DEPENDENCY_STUBS,
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)
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def test_optional_dependency_stubs_are_restored(module_name, stub):
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assert sys.modules.get(module_name) is not stub
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