Pi<lddlZddlZddlmZddlZddlmZmZm Z ddl mZ ej dddddddd Z ejd d d Zejjej d ddd iddedejdededeejedfdede de jfdZdS)N)util)CallableUnionMappingcfdS)Nc2|fd|D|fi|S)Nc&g|] }|Sr .0xts `/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/einx/op/vmap.py z..... s!,,,A11Q44,,,r )exprs_in tensors_in exprs_outkwargscrs rz.. s@,,,,,,,iJJ;AJJrr rrs``rrr r)traceF)flatbackendoprverbosec   !"itt|kr/tdtdt|tt|zD]L}|D]5} t | t jjjrtd6Mfdt|D} |}rCtdtddDtdd |Dtj | \} }tj |} td | DsJtd | DsJrCtd tdd| Dtdd| DdDd|Dd| Dd| D rytds4tddDtddDtddDtdd Dt jdD|d f d }d| D} d | Dfd!!d"} g}t| D]5}|D]0"| "r#"j|vr|"j16rtd#|t| t| zD]@}|D];""j|vp !"| "krtd$"jd%|D?}r,t||D]\}} td@|j| fdAt||| D}rStdBt|| D]3\}} tdCt j|| 4tj| || }rtdDdE|D||fS)FN Expected z input tensor(s), got zConcatenation not allowedcdg|],\}}tj||j-S)r)einxtracer call_factoryshape)r tensorexprrs rrzvmap_stage3..#sF FD   W EErz Expressions:z IN:c,g|]}t|Sr strr es rrzvmap_stage3..+s333Q#a&&333rz OUT:c,g|]}t|Sr r+r-s rrzvmap_stage3..,s555a3q66555rr#c3^K|](}tjj|V)dSNr$r)stage3is_flatr r)s r zvmap_stage3..1s5HH$ty''--HHHHHHrc3^K|](}tjj|V)dSr1r2r5s rr6zvmap_stage3..2s5II$ty''--IIIIIIrzFlat expressions:c,g|]}t|Sr r+r-s rrzvmap_stage3..6s888Q#a&&888rc,g|]}t|Sr r+r-s rrzvmap_stage3..7s:::a3q66:::rcVg|]&}tjj|'Sr r$r)r3 get_markedr5s rrzvmap_stage3..:s+PPPt)44T::PPPrcVg|]&}tjj|'Sr r;r5s rrzvmap_stage3..;s+RRR$)*55d;;RRRrcVg|]&}tjj|'Sr r;r5s rrzvmap_stage3..<s+ZZZDdi.99$??ZZZrcVg|]&}tjj|'Sr r;r5s rrzvmap_stage3..=s+\\\Tty/::4@@\\\rzExpressions used in op:c,g|]}t|Sr r+r-s rrzvmap_stage3..Bs@@@c!ff@@@rc,g|]}t|Sr r+r-s rrzvmap_stage3..CsBBB!s1vvBBBrz IN_FLAT:c,g|]}t|Sr r+r-s rrzvmap_stage3..DsFFF!s1vvFFFrz OUT_FLAT:c,g|]}t|Sr r+r-s rrzvmap_stage3..EsHHH1AHHHrcVg|]&}tj|j'Sr r$r%Tensorr'r5s rrzvmap_stage3..Gs*XXXdk((44XXXr)args)rc   r4tdd|Dtdd|Dt|t ksJsWtj | }rtdd|Dt|t ksJn|}sn t |t rt |}n't |tjj s| fd}||i}t |ttfs|f}t|t kr/td t d t|td |Drtd d |Dr;tdt |D]\}}td||jt!t |D]j\}\}}tj||jkr:td|jd|dtj|ks`tj| \}}rtdd|D|ksJd|DddDrtdd|Dt|S)Nz&Flat input tensors that arrived in op:cfg|].}ttj|/Sr r,r$r% get_shaper as rrz+vmap_stage3..op..Ls0HHH1T[**1--..HHHrz Input types to vmapped function:c,g|]}t|Sr typer rs rrz+vmap_stage3..op..Ns6X6X6X1tAww6X6X6Xrr#z Unflattened input tensors in op:cfg|].}ttj|/Sr rJrLs rrz+vmap_stage3..op..Xs0GGGqS..q1122GGGrc tj||tdkr dDn)tjdjS)NrcVg|]&}tj|j'Sr rEr5s rrz=vmap_stage3..op....gs*VVV4 **4:66VVVrr)rGroutput)r$r%applylenrFr')rGr concrete_opexprs_out_expecteds rrz)vmap_stage3..op..cso):):)**Q..WVCUVVVV[''(:1(=(CDD *;**rr!z1 output tensor(s) from vmapped function, but got c3VK|]$}t|tjj V%dSr1) isinstancer$r%rFrQs rr6z*vmap_stage3..op..us4JJ:a!3444JJJJJJrz0Expected tensors from vmapped function, but got c,g|]}t|Sr rOrQs rrz+vmap_stage3..op..xsCaCaCaPQDGGCaCaCarz!Unflattened output tensors in op: zExpected output shape z from z5-th (zero-based) output of vmapped function, but got zFlattened output tensors in op:cfg|].}ttj|/Sr rJrLs rrz+vmap_stage3..op..s0HHHqS..q1122HHHrc,g|]}t|Sr r+r ss rrz+vmap_stage3..op..s<<.op..s8881A888rz&Returning types from vmapped function:c,g|]}t|Sr rOrQs rrz+vmap_stage3..op..s.opGs  Z 8HHHHH    46X6X6X6X6X Y Y Y?##s+A'B'BBBBB )&9JT[J 6GGJGGGz??c(mm33333(J7;X//AX b#   "%%BBB 233 KBb*/// +t}55 )&.K {  s#566 6 68C 23388%(%5%588  JJkJJJ J J cCaCaU`CaCaCacc   : 5 6 6 6(+,> (L(L : :$*fh (89999)237I;3W3W)X)X   %A%*{$$Z00HNBB _X^__1__;?;;P;PQ[;\;\__C  ;48L"K555 1 $k 5HHKHHH ),CCCC<<#;<<<BBBDCC88 7888 : :  \ :..s+++af+++rr r roots rrzvmap_stage3..s'FFF++d+++FFFrc&h|]}|D] }|j Sr r{)r r~rMs r zvmap_stage3..s'HHHD4HHaHHHHrct|tjjjo-|jvo$tjj| Sr1)r[r$r)r3Axisr| is_marked)r)axes_names_in_sets ris_broadcast_axisz&vmap_stage3..is_broadcast_axissK tTY-2 3 3 5 !22 5I$..t444 rcLtjj| Sr1)r$r)r3r)r)s r is_vmappedzvmap_stage3..is_vmappeds9#--d3333rzVmapping the following axes: zAxis z( appears both as vmapped and non-vmappedcZg|]'}tjj|(Sr )r$r)r3remove)r r)rs rrzvmap_stage3..s<(((=A &788(((rc&g|]}d|DS)cg|] }|j Sr r{rLs rrz*vmap_stage3...sArr r}s rrzvmap_stage3..s4(((#'(((rz*Flat output expressions without broadcast:c,g|]}t|Sr r+r-s rrzvmap_stage3..s > > >SVV > > >rzGot input axis names:z:Got output axis names (excluding broadcasted output axes):c3$K|] }|jV dSr1r'r5s rr6zvmap_stage3..s$>>>>>>>>rc3$K|] }|jV dSr1rr5s rr6zvmap_stage3..s$RR$*RRRRRRrc3NK|]}|vr|ndV dSr1indexr axes_namesvs rr6zvmap_stage3..sO  AK1 ??J  Q         rc3NK|]}|vr|ndV dSr1rrs rr6zvmap_stage3..sR  $% ??J  Q         rzApplying backend.vmap to axis z, with input axis indices z and output axis indices z.drop_axiss,| UdU|eD1HJJ&777rc36K|]\}}||VdSr1r r r'rrs rr6zvmap_stage3..s3bb tYYud33bbbbbbrc36K|]\}}||VdSr1r rs rr6zvmap_stage3..sD  '2udIIeT " "      rzNow has remaining input axes z and output axes )in_axesout_axes input_shapes output_shapesz Sending shapes to backend.vmap:c6g|]}t|jSr )r,r'rLs rrzvmap_stage3..s 3U3U3UQCLL3U3U3Urc3^K|](}tj|jV)dSr1rEr5s rr6zvmap_stage3..s4aaT['' 33aaaaaar)rGrUzGot overall flat tensor_out:cTg|]$\}}}tj|||d%S)r#r)rtranspose_broadcast)r expr_out_wbr(rqrs rrzvmap_stage3.. sI )K  fhPPPQRSrz/Got overall transposed+broadcasted tensors_out:r]z$Got overall unflattened tensors_out:cfg|].}ttj|/Sr rJrLs rrzvmap_stage3..s14d4d4dWXS9N9Nq9Q9Q5R5R4d4d4dr)rWrjriallr[r$r)r3 Concatenationrl all_to_tensorrdrrnrr|appendrhreversedrvmapr%rVr'rKre)#rrrrrrrrr~r) exprs_in_flatexprs_out_flat axes_names_inr vmapped_axes exprs_out_flat_without_broadcast axes_names_out_without_broadcastvmapsrrrrout_axisrqrtensorsr(rrrurvrwrxrrs#` `` `` @@@@@@@@r vmap_stage3r s. ~ 8}}J''[S]][[#j//[[\\\Xi0>>HHJJ > >D$ 0 >?? > !<=== > >  H55J&&z22J7 n i33(333444 j559555666!% Xz7 S S SM:\),,N HH-HHH H HHH H II.III I III I< !""" i88-888999 j::>:::;;;QPxPPPRR RRRZZMZZZ\\^\\\J '((( D )@@.?@@@ A A A *BB/ABBB C C C nFF/EFFFGGG oHH0GHHHIII ZXXAWXXXYYY "P"P"P"P"P"P"P"P"P"P"P"P"P"P"ZYP"dGF FFFMHHMHHH     444L]##,, , ,Az!}} ,|!;!;##AF+++ ,> &:&::[[ [ [A,&>*;*;A*>*>::a==PP !Y!Y!Y!YZZZQ [ ((((ES((($((+K((($   8 > >= > > >    %}555 H ,   E>> >>>>>LRR1QRRRRRM l # #**    O\         >        @@@@@5=@@   #&h"?"?   Hh AAA5=AAA  gx}EFFF 8 8 8 bbbbs > > >n^WiQQQG  24d4d\c4d4d4d    I rT)csectjj||\}}tjj|}t|dkrDtjj|d|d g}t|dt|kr5tdt|ddt|tj dt|d|Dd|dDzd| Dz|d dt|dt|dz}|dt|d|t|dd}}||fS) Nrrr!z input tensors, but got cTg|]%\}}tj||&Sr r$r)Equation)r expr_in tensor_shapes rrzparse..4s>   % I  w 5 5   rcLg|]!}tj|"Sr r)r rqs rrzparse..8s( > > >H49  h ' ' > > >rcg|]L\}}tj|tj|dtjfddMS).N)depth1depth2)r$r)rnpasarraynewaxis)r krs rrzparse..9sY   1 I  q"*Q--RZ"@VZ  [ [   rF)r cse_concat)r$rr!_clean_description_and_parametersr)stage1parse_oprWOp __deepcopy__rjsolverlitems) descriptionr tensor_shapes parametersrexprsrrs rparser!s"glLLZK   " "; / /B 2ww!|| Y  qE qE   "     2a5zzS'''']SAZZ]]]I[I[]]^^^ IOO  ),RUM)B)B    ? >1 > > >  ?   "((**         BqE SAZZ !E #be** -uSAZZ\\/BiH Y rcfdS)Nc4|gfd|DRi|S)Nc&g|] }|Sr r r s rrz.....Hs!---qqtt---rr )rrrrrs rrz..GsJqq@----W---@@@17@@rr rs``rrrGrr)rrrrrrrrrrrrc t|gd|DRd|i|\}} t||| ||||\}} t| dkr|dn|S)aY Vectorizes and applies a function to the input tensors using automatic vectorization. The function ``op`` must accept input tensors and yield output tensors as specified in ``description`` with shapes matching the subexpressions that are marked with ``[]``-brackets. Args: description: Description string for the operation in einx notation. tensors: Input tensors or tensor factories matching the description string. op: Function that will be vectorized. If ``op`` is a string, retrieves the attribute of ``backend`` with the same name. flat: Whether to pass the tensors to ``op`` in flattened form or matching the nested layout in the input expressions. Defaults to False. kwargs: Additional keyword arguments that are passed to ``op``. Defaults to ``{}``. backend: Backend to use for all operations. If None, determines the backend from the input tensors. Defaults to None. cse: Whether to apply common subexpression elimination to the expressions. Defaults to True. graph: Whether to return the graph representation of the operation instead of computing the result. Defaults to False. **parameters: Additional parameters that specify values for single axes, e.g. ``a=4``. Returns: The result of the vectorized operation if `graph=False`, otherwise the graph representation of the operation. Examples: Compute the mean along rows of a matrix: >>> x = np.random.uniform(size=(10, 8)) >>> einx.vmap("a [b] -> a", x, op=np.mean) (10,) Vectorize a custom function: >>> x, y = ( ... np.random.uniform(size=(10, 13, 4)), ... np.random.uniform( ... size=( ... 4, ... 9, ... ) ... ), ... ) >>> def op(x, y): # c, d -> 2 >>> return np.stack([np.mean(x), np.max(y)]) >>> einx.vmap("b1 [c] b2, b2 [d] -> b2 [2] b1", x, y, op=op).shape (4, 2, 10) Compute a matrix-matrix multiplication >>> x, y = ( ... np.random.uniform(size=(5, 10)), ... np.random.uniform(size=(10, 3)), ... ) >>> einx.vmap("a [b], [b] c -> a c", x, y, op=np.dot).shape (5, 3) cLg|]!}tj|"Sr )r$r%rK)r r(s rrzvmap..s(KKKt{,,V44KKKrr)rrrrrr)rrrW) rrrrrrrrrrs rrrEsT KK7KKKQTXbHi%'94RPVGYY1,,71::'9r)r$ functoolsrnumpyrtypingrrr numpy.typingnptjitr lru_cachertraceback_utilfilterr,rFboolBackend ArrayLikerr rrrs ++++++++++       PPPP  Pf+/     F   .2J:J:J:J:kJ: J:  J: 4<d* + J:  J: J:-J:J:J:  J:J:J:r