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linedance-app/venv/lib/python3.12/site-packages/numba/cuda/device_init.py

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+# Re export
+import sys
+from numba.cuda import cg
+from .stubs import (threadIdx, blockIdx, blockDim, gridDim, laneid, warpsize,
+                    syncwarp, shared, local, const, atomic,
+                    shfl_sync_intrinsic, vote_sync_intrinsic, match_any_sync,
+                    match_all_sync, threadfence_block, threadfence_system,
+                    threadfence, selp, popc, brev, clz, ffs, fma, cbrt,
+                    activemask, lanemask_lt, nanosleep, fp16,
+                    _vector_type_stubs)
+from .intrinsics import (grid, gridsize, syncthreads, syncthreads_and,
+                         syncthreads_count, syncthreads_or)
+from .cudadrv.error import CudaSupportError
+from numba.cuda.cudadrv.driver import (BaseCUDAMemoryManager,
+                                       HostOnlyCUDAMemoryManager,
+                                       GetIpcHandleMixin, MemoryPointer,
+                                       MappedMemory, PinnedMemory, MemoryInfo,
+                                       IpcHandle, set_memory_manager)
+from numba.cuda.cudadrv.runtime import runtime
+from .cudadrv import nvvm
+from numba.cuda import initialize
+from .errors import KernelRuntimeError
+
+from .decorators import jit, declare_device
+from .api import *
+from .api import _auto_device
+from .args import In, Out, InOut
+
+from .intrinsic_wrapper import (all_sync, any_sync, eq_sync, ballot_sync,
+                                shfl_sync, shfl_up_sync, shfl_down_sync,
+                                shfl_xor_sync)
+
+from .kernels import reduction
+
+reduce = Reduce = reduction.Reduce
+
+# Expose vector type constructors and aliases as module level attributes.
+for vector_type_stub in _vector_type_stubs:
+    setattr(sys.modules[__name__], vector_type_stub.__name__, vector_type_stub)
+    for alias in vector_type_stub.aliases:
+        setattr(sys.modules[__name__], alias, vector_type_stub)
+del vector_type_stub, _vector_type_stubs
+
+
+def is_available():
+    """Returns a boolean to indicate the availability of a CUDA GPU.
+
+    This will initialize the driver if it hasn't been initialized.
+    """
+    # whilst `driver.is_available` will init the driver itself,
+    # the driver initialization may raise and as a result break
+    # test discovery/orchestration as `cuda.is_available` is often
+    # used as a guard for whether to run a CUDA test, the try/except
+    # below is to handle this case.
+    driver_is_available = False
+    try:
+        driver_is_available = driver.driver.is_available
+    except CudaSupportError:
+        pass
+
+    return driver_is_available and nvvm.is_available()
+
+
+def is_supported_version():
+    """Returns True if the CUDA Runtime is a supported version.
+
+    Unsupported versions (e.g. newer versions than those known to Numba)
+    may still work; this function provides a facility to check whether the
+    current Numba version is tested and known to work with the current
+    runtime version. If the current version is unsupported, the caller can
+    decide how to act. Options include:
+
+    - Continuing silently,
+    - Emitting a warning,
+    - Generating an error or otherwise preventing the use of CUDA.
+    """
+
+    return runtime.is_supported_version()
+
+
+def cuda_error():
+    """Returns None if there was no error initializing the CUDA driver.
+    If there was an error initializing the driver, a string describing the
+    error is returned.
+    """
+    return driver.driver.initialization_error
+
+
+initialize.initialize_all()