.Ph)dZddlZddlZddlZddlZddlZddlZddlZejZ Gdde Z Gdde Z Gdde Z Gd d e Zdd ZdS)zb Fast cryptographic hash of Python objects, with a special case for fast hashing of numpy arrays. NceZdZdZdZdS)_ConsistentSetz\Class used to ensure the hash of Sets is preserved whatever the order of its items. c t||_dS#ttjf$r"td|D|_YdSwxYw)Nc34K|]}t|VdSNhash).0es N/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/joblib/hashing.py z*_ConsistentSet.__init__..%s($C$CT!WW$C$C$C$C$C$C)sorted _sequence TypeErrordecimalInvalidOperation)self set_sequences r __init__z_ConsistentSet.__init__sj E $L11DNNN734 E E E$$C$Cl$C$C$CDDDNNNN Es4AAN__name__ __module__ __qualname____doc__rrr rrs2 E E E E ErrceZdZdZdZdS)_MyHashz5Class used to hash objects that won't normally picklec||_dSr)args)rr s r rz_MyHash.__init__+s  rNrrrr rr(s)??rrc(eZdZdZd dZd dZdZdZdej fd Z e j Z e e ee<e e ee<e e ee <e e eej<d Zd Zee ee<dS)HasherzA subclass of pickler, to do cryptographic hashing, rather than pickling. This is used to produce a unique hash of the given Python object that is not necessarily cryptographically secure. md5ctj|_d}t||j|t j|d|_dS)N)protocolF)usedforsecurity)ioBytesIOstreamPicklerrhashlibnew_hash)r hash_namer&s r rzHasher.__init__5sMjll t{X>>>[EBBB rTc. ||n/#tj$r}|xjd|d|fz c_d}~wwxYw|j}|j||r|jSdS)NzPicklingError while hashing z: ) dumppickle PicklingErrorr r*getvaluer.update hexdigest)robj return_digestr dumpss r r z Hasher.hash>s  IIcNNNN#    FFFsssAAFH HFF   $$&& %    *:'')) ) * *sA?Act|tjtijfrt |dr |jj}n|j}|j}t|tturt||j}n0|t||}n|jj }t|||}t ||dS)N__func__) isinstancetypes MethodTypetypepophasattrr;r__self__r2r __class__r+save)rr7 func_nameinstclss r rDz Hasher.saveIs cE,d26ll; < < 4sJ'' )L1 L K|]\}}t||fVdSrr)r kvs r r z)Hasher._batch_setitems..s0!A!A41a477A,!A!A!A!A!A!Ar)r+_batch_setitemsiterrr)ritemsr s r r^zHasher._batch_setitemss   #D$ve}}*=*= E E E E E E E     #d6!A!A5!A!A!AAABB EI        s-1AA65A6cVt|t|dSr)r+rDr)r set_itemss r save_setzHasher.save_sets$ T>)4455555r)r#)T)rrrrrr rDrKstructrNrQr+dispatchcopyr?lenobjectr2r1r^rcsetrrr r"r"/s CCCC * * * *   &###%)v{////*$$&&H%HTT#YY(HTT&\\)HTT']]"-HTT&+     666%HTT##%%[[rr"c eZdZdZddZdZdS) NumpyHasherz1Special case the hasher for when numpy is loaded.r#Fc||_t||ddl}||_t |dr|j|_dSt|_dS)z Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. r/rN getbuffer) coerce_mmapr"rnumpynprArn _getbuffer memoryview)rr/rorqs r rzNumpyHasher.__init__s_' 222 2{ # # ) lDOOO(DOOOrcNt||jjr|jjs|jdkr|}n7|jjr|}n(|jj r|j }n|}|j | ||jj|jr't||jjr |jj}n|j}|d|j|j|jff}nut||jjr[|j dd|j t+j|dSt.||dS)zSubclass the save method, to hash ndarray subclass, rather than pickling them. Off course, this is a total abuse of the Pickler class. rHASHED _HASHED_DTYPEzutf-8N)r<rqndarraydtype hasobjectshapeflattenflags c_contiguous f_contiguousTr.r5rrviewuint8romemmaprCstridesencoder2r9r"rD)rr7obj_c_contiguousklasss r rDzNumpyHasher.saves c47? + +8 CI4G8 yB$';;==  ' 1#&  ' 1#&5  $';;== J  doo.>.C.CDGM.R.RSS T T T  &JsDGN$C$C &  8SY 3;GHCC TW] + +  J  o44W== > > > J  fl3// 0 0 0 F D#rNr#F)rrrrrrDrrr rkrks=;;))))*>>>>>rrkr#Fcd}||vr#td||dtjvrt ||}nt |}||S)aIQuick calculation of a hash to identify uniquely Python objects containing numpy arrays. Parameters ---------- hash_name: 'md5' or 'sha1' Hashing algorithm used. sha1 is supposedly safer, but md5 is faster. coerce_mmap: boolean Make no difference between np.memmap and np.ndarray )r#sha1zAValid options for 'hash_name' are {}. Got hash_name={!r} instead.rp)r/rorm) ValueErrorformatrSrTrkr"r )r7r/rovalid_hash_nameshashers r r r s'((( O V V )     #+ykJJJ),,, ;;s  rr)rrr,r(r2rdrSr=_Picklerr+rhrrr"rkr rrr rs7 /EEEEEVEEE&fl%l%l%l%l%Wl%l%l%^VVVVV&VVVrr