g^(h@ ddlZddlZddlZddlZddlmZmZddlmZm Z m Z ddl Z ddl m cmZddl mZmZddlmZd0dZej*GddZGd d Zd ed efd Zded eeeffdZded efdZded efdZded efdZ ded efdZ!ded efdZ"defdZ#dZ$dejJdeedfde&eefd eeejNeffdZ(dZ)ded efdZ*d efd Z+d!Z,dejJfd"Z-dejJfd#Z.dejJd efd$Z/dejJd e edffd%Z0 d1deede&eefd&ed ee jbfd'Z2d(fd)Z3Gd*d+Z4d,e jbd e jbfd-Z5de jlj&d efd.Z7dejJd ee8ee8effd/Z9y)2N)IterableIterator)AnyCallableUnion)_C_utils_internal) OpOverloadc<tjdt|y)Nz}Python torch.library APIs do nothing under torch::deploy (multipy). Please instead use C++ custom operator registration APIs. stacklevel)warningswarnRuntimeWarningr s R/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/_library/utils.py warn_deployrs MM D c,eZdZUdZeed<eed<dZy)Kernelz$Models a (function, source location)funcsourcec&|j|i|SN)r)selfargskwargss r__call__zKernel.__call__styy$)&))rN)__name__ __module__ __qualname____doc__r__annotations__strrrrrrs. 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N)r>rwr~rrI)rUrrrrs r zip_schemars v CIF $; ;; ; 3v''( ) " ??yyF"F499---  D >??tyyF':F499--- DGm  sCCc \ddlm}|j}t|tj j s tdd}g}|jD]}t|tjjtjjjfr|j||ct|tjjjtt fr&|j|Dcgc] }|| c}tdt#|t%j&|j(j*|}||}|j-|j.t!|j0j3t|fScc}w)Nr)FunctionSchemaGenzfx_node's target must be a hop.c|jjdd}|;|jdk(sJt|jj |j }|S)Nvalget_attr)metarlrNrEgraph owning_moduletarget)nodemeta_vals r_collect_example_valz5hop_schema_from_fx_node.._collect_example_valsM99==-  77j( ((tzz77EHrzUnsupported arg type )torchgen.gen_schema_utilsrrrSrF_opsHigherOrderOperator RuntimeErrorrfxNoderrimmutable_collectionsimmutable_listlistrtyper1 signaturerbind from_example_namerwitems) rrhoprexample_inputsr{x bound_argsexample_outputs rhop_schema_from_fx_nodersW; ++C c5::99 :<==NyyD cEHHMM588==+=+=> ?  ! !"6s"; <  %((00??uM   ! !C"Hq#7#:"H I!6tCykBC CD*N):):3<<)H)M)M *J *$/N  ) ) 5--3356n9M8O #Is>F) ct|tsJt|ry|j}|jsyt |j dkDryy)NFrT)rSr rTrsr^r>r_)rNrUs rcan_generate_trivial_fake_implrsJ b* %% %"~ ZZF    6>>Q rc$ttddS)zIf an op was defined in C++ and extended from Python using the torch.library APIs, returns if we require that there have been a m.set_python_module("mylib.ops") call from C++ that associates the C++ op with a python module. 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If it does not exist, returns None. deviceN) enumeraterwrr DeviceObjTyperlrI)rUindexr{s rget_device_arg_indexrDsR   0 01 s 88r''++- -#((h2FL rallowed_nestingc#Kfd}|D]}||Ed{|jD]}||Ed{y7,7 w)Nc3Kt|tjr|ydkDr9t|ttfr"t t|idz Ed{yyy7w)Nrr<)rSrFrrr iter_tensors)r{rs rcheckziter_tensors..checkRsV c5<< (I q ZeT]%C#E#JOa4GH H H&D HsAA$A"A$)r)rrrrr{kwargs ` rrrOsVI : <  sA A$A AA A cy)Nz???r$r$rrr^src|Dchc]6}t|tjst|j 8}}|}t|t s|f}t |iD]]}t|j }t|j |vrt|d|d|j|_ycc}w)zO custom operators' outputs must not alias any inputs or other outputs. z (with implementation in a): The output of this custom operator (1) must not also be an input to this custom operator and (2) may not alias any inputs to this custom operator or other returns. The most common way to trigger this error is if we have y = custom_op(x) and y and x are the same Tensor. 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Please make the schema and operator behavior consistent. You can specify that an operator mutates a Tensor by e.g. changing its schema type from 'Tensor name' to 'Tensor(a!) name'(use different identifiers (a, b, c, ...) for different Tensors))rZrrNrrIrs)r was_mutatedrs r check_onez(MutationChecker.check..check_onesi==K/"ww}}o%5dii[@Yww'($15IDU#VWWX  rF)rrSrFrrziprequalisnanallpytreetree_unflattenrrrNrsrqrror`) rrreal_post_hashesprepostrwas_mutated_argswas_mutated_kwargsrrwas_any_mutateds ` rrzMutationChecker.checksk^^ )ELL9KNt C  !!5!57GH  T#u||,D%,,1O C& & ?YY[__&=4::<+;+;+=>   06/D/D 0 ,,", GGOO-/A"  1 D+ #499-$ ,(3+6+>%C DT$0- 1  s .FBFN)rrr r!r+rr$rrrrxs %1rrrcZ|jjjS)zNSome inexpensive hash. Used as a quick and dirty indicator for tensor mutation)detachfloatmean)rs rrrs 88:    " " $$rct|ry|j}tjj |drytj j j|}|tjj |drytj jjj|}|jjytjj |dryy|jyy)zIf an operator (that stays alive until FakeTensorMode) has a Fake kernel. Don't use this if the operator decomposes before FakeTensorMode. 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