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https://github.com/snapotter-hq/SnapOtter.git
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SnapOtterandGitHub
991c981529
* fix: make OCR portable and reliable * fix: harden OCR installation portability * fix: pin OCR partials across downloads * fix: make OCR execution reliably asynchronous * fix: harden OCR portability and docs routes * fix: preserve decoder and docs safeguards
84 lines
3.5 KiB
Python
84 lines
3.5 KiB
Python
"""Framework-specific CUDA probes must never depend on obsolete Paddle OCR."""
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import os
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import sys
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import types
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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import gpu # noqa: E402
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# --- gpu_available() orchestration -----------------------------------------
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def _patch_probes(monkeypatch, torch, onnx, smi):
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"""Stub the three existing probes and reset the lru_cache for one call."""
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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monkeypatch.setattr(gpu, "_try_torch_cuda", lambda: torch)
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monkeypatch.setattr(gpu, "_try_onnx_cuda", lambda: onnx)
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monkeypatch.setattr(gpu, "_nvidia_smi_gpu_name", lambda: smi)
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gpu.gpu_available.cache_clear()
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def test_gpu_available_does_not_treat_hardware_presence_as_framework_support(monkeypatch):
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_patch_probes(monkeypatch, torch=False, onnx=False, smi="NVIDIA GeForce RTX 4070")
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assert gpu.gpu_available() is False
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def test_gpu_module_has_no_paddle_probe():
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assert not hasattr(gpu, "_try_paddle_cuda_subprocess")
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# --- Per-framework detection (torch, ctranslate2) --------------------------
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#
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# Torch and CTranslate2 tools must gate on their OWN framework, not the general
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# gpu_available(), which can report True based on ONNX Runtime while
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# torch is a CPU-only build. Consuming the shared boolean would make those tools
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# route to CUDA on a device their framework cannot use.
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def test_torch_gpu_available_true_when_torch_can_use_cuda(monkeypatch):
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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monkeypatch.setattr(gpu, "_try_torch_cuda", lambda: True)
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assert gpu.torch_gpu_available() is True
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def test_torch_gpu_available_false_when_override_disables_gpu(monkeypatch):
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monkeypatch.setenv("SNAPOTTER_GPU", "0")
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monkeypatch.setattr(gpu, "_try_torch_cuda", lambda: True)
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assert gpu.torch_gpu_available() is False
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def test_torch_gpu_available_false_when_torch_is_cpu_only(monkeypatch):
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# The crux: gpu_available() may be True via ONNX on a GPU box, but a
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# CPU-only torch build must report no GPU so torch tools do not touch CUDA.
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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monkeypatch.setattr(gpu, "_try_torch_cuda", lambda: False)
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assert gpu.torch_gpu_available() is False
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def test_ctranslate2_gpu_available_true_when_cuda_device_present(monkeypatch):
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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fake = types.SimpleNamespace(get_cuda_device_count=lambda: 1)
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monkeypatch.setitem(sys.modules, "ctranslate2", fake)
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assert gpu.ctranslate2_gpu_available() is True
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def test_ctranslate2_gpu_available_false_when_no_cuda_device(monkeypatch):
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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fake = types.SimpleNamespace(get_cuda_device_count=lambda: 0)
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monkeypatch.setitem(sys.modules, "ctranslate2", fake)
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assert gpu.ctranslate2_gpu_available() is False
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def test_ctranslate2_gpu_available_false_when_override_disables_gpu(monkeypatch):
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monkeypatch.setenv("SNAPOTTER_GPU", "0")
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fake = types.SimpleNamespace(get_cuda_device_count=lambda: 4)
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monkeypatch.setitem(sys.modules, "ctranslate2", fake)
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assert gpu.ctranslate2_gpu_available() is False
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def test_ctranslate2_gpu_available_false_when_not_installed(monkeypatch):
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# A None entry in sys.modules makes `import ctranslate2` raise ImportError,
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# which models the framework being absent (e.g. no transcription bundle).
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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monkeypatch.setitem(sys.modules, "ctranslate2", None)
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assert gpu.ctranslate2_gpu_available() is False
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