7Th67 SrSSKrSSKrSSKrSSKJr \ SSK J r SSK J r SSK JrJr SSKJrJr SS KJrJr SSKr"S S \5r"S S \5r"SS\ 5r"SS\5rg!\a \"S5rNff=f!\a SrNKf=f)aCustom implementation of multiprocessing.Pool with custom pickler. This module provides efficient ways of working with data stored in shared memory with numpy.memmap arrays without inducing any memory copy between the parent and child processes. This module should not be imported if multiprocessing is not available as it implements subclasses of multiprocessing Pool that uses a custom alternative to SimpleQueue. N)sleep)BytesIO)Pool)HIGHEST_PROTOCOLPickler)TemporaryResourcesManagerget_memmapping_reducers)assert_spawningmpc,\rSrSrSrS\4SjrSrSrg)CustomizablePickler.a}Pickler that accepts custom reducers. TODO python2_drop : can this be simplified ? HIGHEST_PROTOCOL is selected by default as this pickler is used to pickle ephemeral datastructures for interprocess communication hence no backward compatibility is required. `reducers` is expected to be a dictionary with key/values being `(type, callable)` pairs where `callable` is a function that give an instance of `type` will return a tuple `(constructor, tuple_of_objects)` to rebuild an instance out of the pickled `tuple_of_objects` as would return a `__reduce__` method. See the standard library documentation on pickling for more details. NcD[R"XUS9 Uc0n[[S5(a$[RR 5UlO#[ R R 5UlUR5HupEURXE5 M g)N)protocoldispatch) r__init__hasattrrcopycopyregdispatch_tableitemsregister)selfwriterreducersrtype reduce_funcs C/var/www/auris/envauris/lib/python3.13/site-packages/joblib/pool.pyrCustomizablePickler.__init__Fs{9  H 7J ' '$,,113DM#*"8"8"="="?D !)!1 D MM$ ,"2cx^[[S5(aU4SjnX0RU'gTURU'g)z@Attach a reducer function to a given type in the dispatch table.rc:>T"U5nUR"USU06 g)Nobj) save_reduce)rr$reducedrs r dispatcher0CustomizablePickler.register..dispatcherZs %c*  73S3r!N)rrrr)rrrr's ` rrCustomizablePickler.registerUs3 7J ' ' 4#-MM$ (3D   %r!)rr) __name__ __module__ __qualname____firstlineno____doc__rrr__static_attributes__r!rrr.s.)-7G - 4r!rc:\rSrSrSrS SjrSrSrSrSr S r g) CustomizablePicklingQueuecaLocked Pipe implementation that uses a customizable pickler. This class is an alternative to the multiprocessing implementation of SimpleQueue in order to make it possible to pass custom pickling reducers, for instance to avoid memory copy when passing memory mapped datastructures. `reducers` is expected to be a dict with key / values being `(type, callable)` pairs where `callable` is a function that, given an instance of `type`, will return a tuple `(constructor, tuple_of_objects)` to rebuild an instance out of the pickled `tuple_of_objects` as would return a `__reduce__` method. See the standard library documentation on pickling for more details. NcX lURSS9uUlUlUR 5Ul[ RS:XaSUlOUR 5UlUR5 g)NF)duplexwin32) _reducersPipe_reader_writerLock_rlocksysplatform_wlock _make_methods)rcontextrs rr"CustomizablePicklingQueue.__init__tsY!%,\\\%?" dllln <<7 "DK!,,.DK r!c[U5 URURURURUR 4$N)r r9r:r<r?r7rs r __getstate__&CustomizablePicklingQueue.__getstate__~s/ dllDKKdnnUUr!cfUuUlUlUlUlUlUR 5 grD)r9r:r<r?r7r@)rstates r __setstate__&CustomizablePicklingQueue.__setstate__s(QVNt|T[$+t~ r!c@URR5(+$rD)r9pollrEs remptyCustomizablePicklingQueue.emptys<<$$&&&r!c^^^^^^^TRR=TlmTRRTRR smmUUU4SjnUTlTR(aU4SjmTTlOTRR=TlmTRcTTl gTRRTRR smmUUU4SjnUTl g)NcH>T"5 T"5T"5 $!T"5 f=frDr0)racquirerecvrreleasesrget4CustomizablePicklingQueue._make_methods..gets J v  s !c>[5n[UTR5RU5 TRR UR 55 grD)rrr7dumpr: send_bytesgetvalue)r$bufferrs rsend5CustomizablePicklingQueue._make_methods..sends< #FDNN;@@E ''(9:r!cJ>T"5 T"U5T"5 $!T"5 f=frDr0)r$r\ wlock_acquire wlock_releases rput4CustomizablePicklingQueue._make_methods..puts$9!OMOs ") r9rS_recvr<acquirereleaserUr7_sendr:r\r?ra) rrUrarRrSrTr\r_r`s ` @@@@@@rr@'CustomizablePicklingQueue._make_methodss LL--- T![[00$++2E2E(  >> ; DJ $ 1 1 1DJ ;; DH,0KK,?,?ATAT (M= $DHr!) r9rcr7r<rfr?r:rUrarD) r*r+r,r-r.rrFrJrNr@r/r0r!rr2r2cs! V'$r!r2c8^\rSrSrSrSU4SjjrSrSrU=r$) PicklingPoolaPool implementation with customizable pickling reducers. This is useful to control how data is shipped between processes and makes it possible to use shared memory without useless copies induces by the default pickling methods of the original objects passed as arguments to dispatch. `forward_reducers` and `backward_reducers` are expected to be dictionaries with key/values being `(type, callable)` pairs where `callable` is a function that, given an instance of `type`, will return a tuple `(constructor, tuple_of_objects)` to rebuild an instance out of the pickled `tuple_of_objects` as would return a `__reduce__` method. See the standard library documentation about pickling for more details. c >Uc [5nUc [5nX lX0l[US9nURU5 [[ U]"S0UD6 g)N) processesr0)dict_forward_reducers_backward_reducersupdatesuperrir)rrlforward_reducersbackward_reducerskwargspoolargs __class__s rrPicklingPool.__init__sU  ##v   $ $ !1"3), lD*6X6r!c[US[5n[XR5Ul[XR 5UlURRUlUR RUl g)N_ctx) getattrr r2rn_inqueuero _outqueuerf _quick_putrc _quick_get)rrAs r _setup_queuesPicklingPool._setup_queuessV$+1';Q;QR 27[U5n Xl[U RUUUUUSUS9upV[ UUUS9n U R U 5 [ [U]""S0U D6 g)NF)temp_folder_resolver max_nbytes mmap_moderrrsverboseunlink_on_gc_collectprewarm)rlrrrsr0) r _temp_folder_managerr resolve_temp_folder_namermrprqrr) rrl temp_folderrrrrrsrrrtmanagerrurvs rrMemmappingPool.__init__#sy,K8$+! /F!(!A!A!-/!& / +-/   nd,8x8r!cT>Sn[U5Hn[[U]5 O URR5 g![aYn[ U[ 5(a9[S5 US-U:Xa[R"SU-5 SnAMSnAMSnAMSnAff=f)N g?rz@Failed to terminate worker processes in multiprocessing pool: %r) rangerqr terminateOSError isinstance WindowsErrorrwarningswarnr_clean_temporary_resources)r n_retriesiervs rrMemmappingPool.terminateIs y!A nd57" !!<<> a..#J1u ) 8:;<* / sA B'AB""B'c[USS5b UR$URR5UlUR$)N_cached_temp_folder)rzrrrrEs r _temp_folderMemmappingPool._temp_folder]sJ 4. 5 A++ +))BBD  $++ +r!)rr)NNg.ArNNrF) r*r+r,r-r.rrpropertyrr/rrs@rrrsCJ\$9L?( , ,r!r)r.rr=rtimerr NameErrorriormultiprocessing.poolrpicklerr_memmapping_reducerr r _multiprocessing_helpersr r numpynp ImportErrorrobjectr2rirr0r!rrs  &,S924'24jJJZ#/4#/LT,\T,:L Bs"A.B.A?>A?B  B