LjddlmZddlmZmZddlZddlmZ gdZ de fdZ ede fdZ ed ed ede fd Zedd ede fd Zedefd ZedefdZdS)) lru_cache)Optional TYPE_CHECKINGN)Library)get_core_countget_nameis_built is_availableis_macos13_or_neweris_macos_or_newerreturnc$tjjS)zReturn whether PyTorch is built with MPS support. Note that this doesn't necessarily mean MPS is available; just that if this PyTorch binary were run a machine with working MPS drivers and devices, we would be able to use it. )torch_C_has_mpsIC:\Projects\DSCons\.venv\Lib\site-packages\torch\backends\mps\__init__.pyr r s 8 rc>tjS)z7Return a bool indicating if MPS is currently available.)rr_mps_is_availablerrrr r s 8 % % ' ''rmajorminorcBtj||S)zHReturn a bool indicating whether MPS is running on given MacOS or newer.rr_mps_is_on_macos_or_newer)rrs rr r "s 8 - -eU ; ;;rcBtjd|S)zEReturn a bool indicating whether MPS is running on MacOS 13 or newer. r)rs rr r (s 8 - -b% 8 88rc>tjS)zReturn Metal device name)rr _mps_get_namerrrrr.s 8 ! ! # ##rc>tjS)zReturn GPU core count. According to the documentation, one core is comprised of 16 Execution Units. One execution Unit has 8 ALUs. And one ALU can run 24 threads, i.e. one core is capable of executing 3072 threads concurrently. )rr_mps_get_core_countrrrrr4s 8 ' ' ) ))r)r) functoolsr _lru_cachetypingrrr torch.libraryr_Library__all__boolr r intr r strrrrrrr+s]------******** ------   $ (d((( (