3j;5SrSSKJr SSKrSSKrSSKJrJrJr SSK J r SSK J r J r JrJr \"S5r\"S 5r\ "5(GaSSKrSS KJrJrJrJr S S S S S S .r\R2R4\R6R4S\R8R4SS .r\R2R<\R6R<SSSS .r\R2R@\R6R@\R@\R8R@\R@S .r!\R2RD\#"\R6SS5SSSS .r$\R2RJ\#"\R6SS5SSSS .r&\R2RN\#"\R6SS5SSSS .r(\R2RR\#"\R6SS5SSSS .r*\ RV"\,5r-SSK.J/r0 S*Sjr3S+Sjr4S,Sjr5S,Sjr6S,Sjr7S,Sjr8S,Sjr9S,Sjr:S,Sjr;S-S.SjjrS0S!jr?S1S"jr@S#rA\RS$5rCS2S3S%jjrDS2S3S&jjrES'rFS(rG\R"\R5S4S)j5rI\ "5(a\C"5rJgg!\1\24a Sr0Nf=f)5z1 PyTorch utilities: Utilities related to PyTorch ) annotationsN)Callable ParamSpecTypeVar)logging)is_torch_availableis_torch_mlu_availableis_torch_npu_availableis_torch_versionTP)fftnfftshiftifftn ifftshiftTF)cudaxpucpumpsdefaultcgNrrU/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/utils/torch_utils.pyr/qrcgrrrrrrr0rrreset_peak_memory_statsmax_memory_allocated synchronize)allow_in_graphcU$Nr)clss rmaybe_allow_in_graphr'\s rc^X;a US"U0UD6$Xn[U5(dU$U"U0UD6$)Nr)callable)devicedispatch_tableargskwargsfns r_device_agnostic_dispatchr/asE #i($9&99  B B<< t v rc$[U[U5$r%)r/BACKEND_MANUAL_SEED)r*seeds rbackend_manual_seedr3ps $V-@$ GGrc"[U[5$r%)r/BACKEND_SYNCHRONIZEr*s rbackend_synchronizer7t $V-@ AArc"[U[5$r%)r/BACKEND_EMPTY_CACHEr6s rbackend_empty_cacher;xr8rc"[U[5$r%)r/BACKEND_DEVICE_COUNTr6s rbackend_device_countr>|s $V-A BBrc"[U[5$r%)r/BACKEND_RESET_PEAK_MEMORY_STATSr6s rbackend_reset_peak_memory_statsrAs $V-L MMrc"[U[5$r%)r/"BACKEND_RESET_MAX_MEMORY_ALLOCATEDr6s r"backend_reset_max_memory_allocatedrDs $V-O PPrc"[U[5$r%)r/BACKEND_MAX_MEMORY_ALLOCATEDr6s rbackend_max_memory_allocatedrGs $V-I JJrcL[5(dgU[;aSn[U$)NFr)r BACKEND_SUPPORTS_TRAININGr6s rbackend_supports_trainingrJs'    .. $V ,,rc [U[5(a[R"U5nUnUSnU=(d [RnU=(d [R"S5nUb[U[ 5(dURR OUSRR nXrR :wa.US:Xa(SnUS:wa[RSUSUSUS35 O'XrR :waUS :Xa[S US US 35e[U[ 5(a[U5S :XaUSn[U[ 5(a`SUS S-n[U5Vs/sHn[R"XUXSUS9PM n n[R"U SS9RU5n U $[R"XXSUS9RU5n U $s snf)zA helper function to create random tensors on the desired `device` with the desired `dtype`. 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