Pin1pddlZddlmZddlZddlmZddlmZm Z m Z m Z ddl mZ ddgZejd dd Zejd d d Zejjejd dd iddedejdede ejedfdede de jfdZee_ejj ddedejde ejedfdede jf dZejj ddedejde ee efde ejedfdede jf dZejj ddedejde ejedfdede jf dZejj ddedejde ejedfdede jf dZdS) N)util)partial)CallableMappingUnionTuplerollflipcidffd S)Nc:|fd|D|||S)Nc&g|] }|Sr.0xts j/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/einx/op/vmap_with_axis.py z..... s!,,,A11Q44,,,r)exprs_in tensors_in exprs_outopkwargsbackendcrs rz.. s7\]\],,,,,,,iV]]rrrrs``rrr s)"VZr)tracec  |i}t|t|kr/tdt|dt|tt|dkrtdt|t|zD]L}|D]5}t |t jjj rtd6Mt|dkrtdt d|Drtdi|}fd t||D} |}tj || \}}d |Dfd tj |}fd |Dt|dk} dfdt|tzD} t|tzD]5}|D]0} | j| v| krtd| jd16d|DdD} | rtd| rx| krtdfdt||D} d| D}d| D}td|DdksJd|Dn0t|dksJ|dfdfdD| r/t!fdt#dD}nHd|Dt!fd t%tdD}t|dkr t|dkr|n|d|d!<t |t&rt)|}n't |t jjs|fd"}||i|}t |t tfs|f}t|tkr/tdtd#t|fd$t||D}tj||| }||fS)%N Expected z input tensor(s), got rz'All output expressions must be the samezConcatenation not allowedzOnly one output tensor allowedc3TK|]#}tj|V$dSN)einxtracer is_scalarrtensors r z(vmap_with_axis_stage3..s2 B BV4;  ( ( B B B B B Brz.At least one input tensor must be a non-scalarcdg|],\}}tj||j-S)r)r%r& call_factoryshape)rr)exprrs rrz)vmap_with_axis_stage3.."sF FD   W EErr,c&h|]}|D] }|j Srname)rr/axiss r z(vmap_with_axis_stage3..*s'EEE4EETYEEEErc^t|tjjjo|jvSr$ isinstancer%r/stage3Axisr2)r/ in_axis_namess ris_broadcast_axisz0vmap_with_axis_stage3..is_broadcast_axis,s'$ 0 566Y49M;YYrcZg|]'}tjj|(Sr)r%r/r8remove)rr/r;s rrz)vmap_with_axis_stage3..0s<(((=A &788(((rcLtjj| Sr$)r%r/r8 is_marked)r/s r is_vmappedz)vmap_with_axis_stage3..is_vmapped7s9#--d3333rc>h|]}|D]}| |jSrr1)rrootvr@s rr4z(vmap_with_axis_stage3..:sP   :a==  rzAxis z( appears both as vmapped and non-vmappedch|]h}|D]Q}t|tjjj&tjj|J|jRiSrallr7r%r/r8r9r?r2rexpr_inr3s rr4z(vmap_with_axis_stage3..Es KKMM  dDI,1 2 2 8.Ks LLNN  dDI,1 2 2 8.Zsw #{$$Y// Y )03  rcg|] }|d Srrrrs rrz)vmap_with_axis_stage3..fs&&&qad&&&rcg|] }|d S)rrrRs rrz)vmap_with_axis_stage3..gs$$$QAaD$$$rcRh|]$}t|dkt|%SrQ)lenrr/s rr4z(vmap_with_axis_stage3..hs)DDD$c$ii!mmCIImmmrch|]h}|D]Q}t|tjjj&tjj|J|jRiSrrErGs rr4z(vmap_with_axis_stage3..is      $ 0 566  <@9;K;U;UVZ;[;[  I    rrcd|D}g}D]t}t|tjjjrN|j|vrEt|jtjjjr|j}| |utjjj |S)Ncdh|]-}t|tjjj&|j.Srr6)rr3s rr4z>vmap_with_axis_stage3..to_op_output..usBdDI$4$9:: r) rFr7r%r/r8r9r2parentMarkerappendListmaybe)expr_out_flat_wb axis_namesnew_axesr3rHs r to_op_outputz+vmap_with_axis_stage3..to_op_outputts,0022J H  * *dDI$4$9::*tyJ?V?V!$+ty/?/FGG+#{OOD)))9#(..x88 8rc&g|] }|Srr)rr_rbs rrz)vmap_with_axis_stage3..s1   /?LL) * *   rc34K|]\}}|jv|VdSr$r1)rir3marked_input_axess rr*z(vmap_with_axis_stage3..sA  4y--- ----  rc,g|]}t|Sr)listrVs rrz)vmap_with_axis_stage3..s333$4::333rc3VK|]"tfdDV#dS)c36K|]}|jvVdSr$r1)raxes_inrerfs rr*z2vmap_with_axis_stage3...s/OOG71:?&77OOOOOOrN)any)rrerkrfs @rr*z(vmap_with_axis_stage3..s^  OOOOOwOOOOO       rr3c tj||tdkr dDn)tjdjS)NrcVg|]&}tj|j'Sr)r%r&Tensorr.rVs rrz;vmap_with_axis_stage3....s*OOOtDK&&tz22OOOrr)argsroutput)r%r&applyrUror.)rpr concrete_opexprs_op_outputs rrz'vmap_with_axis_stage3..slT[%6%6 ?##a''POOOOO##OA$6$<== &7& & rz output tensor(s), got cTg|]$\}}}tj|||d%S)r,r)rrM)rrH tensor_outrJrs rrz)vmap_with_axis_stage3..sI )GZ  *hPPPQRSr)rU ValueErrorsetrhrFr7r%r/r8 Concatenationzip all_to_tensorrflattenr2 differencetuple enumeraterangestrgetattrr&Tracer unflatten)rrrrrrrBr/exprs_out_flattranspose_firstvmapped_axis_namesrCmarked_output_axesr axis_indices tensors_outrkrsrHrtrOr:r;r@rfrbs ` @@@@@@@@@@rvmap_with_axis_stage3r s ~ 8}}J''[S]][[#j//[[\\\ 3y>>aBCCCXi0>>HHJJ > >D$ 0 >?? > !<=== > > 9~~9::: B Bz B B BBBKIJJJ ZF H55J&&z22J <*gNNNHjEE8EEEMZZZZZ\),,N((((ES((($(mma'O444NNT*J%K%KK X&F!G!GG[[ [ [A,,A>> !Y!Y!Y!YZZZ? [ 8 $$%677USTTT0  2 2 2_  '*(J&?&? '&A&&& $$!$$$DD(DDDEEJJJJ  #    ;8}}!!!!1+ 9 9 9 9 9    Cc          $%Ea%HII     43(333     3wqz??++      <1),\):):Q)>)>LQROv"c  Wb ! ! DK. / /        "j+F++K kE4= 1 1%"n  ;3?@@@@ &<== & &{## & &   -0+~-^-^K .iQXYYYK  !!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||&Srr%r/Equation)rrH tensor_shapes rrzparse..s>   % I  w 5 5   rcLg|]!}tj|"Srr)rrJs rrzparse..s( > > >H49  h ' ' > > >rcg|]L\}}tj|tj|dtjfddMS).N)depth1depth2)r%r/rnpasarraynewaxis)rkrCs rrzparse..sY   1 I  q"*Q--RZ"@VZ  [ [   rF)r cse_concat)r%rr!_clean_description_and_parametersr/stage1parse_oprUOp __deepcopy__rwsolverzitems) descriptionr tensor_shapes parametersrexprsrrs rparsers"glLLZK   " "; / /B 2ww!|| Y  qE qE   "     2a5zzS'''']SAZZ]]]I[I[]]^^^ IOO  ),RUM)B)B    ? >1 > > >  ?   "((**         BqE SAZZ !E #be** -uSAZZ\\/BiH Y rcddfd S)Nr,c4|gfd|DRi|S)Nc&g|] }|Srrrs rrz.....s!---qqtt---rr)rrtensorsrrrs rrz..sJQQN----W---NNN17NNrrrs``rrrs+Tr)rrrrrrrrrrct|gd|DRd|i|\}}t||||||\}}t|dkr|dn|S)aApplies a function to the marked axes of the input tensors by passing the ``axis`` argument and relying on implicit broadcasting rules. The function ``op`` must accept input tensors and an ``axis`` argument specifying the indices of the axes along which the operation is applied. When the function is applied on scalars, the ``axis`` argument is not passed. For multiple input tensors, the function must follow `Numpy broadcasting rules `_. Args: description: Description string for the operation in einx notation. tensors: Input tensors or tensor factories matching the description string. op: Backend operation. Is called with ``op(tensor, axis=...)``. If ``op`` is a string, retrieves the attribute of ``backend`` with the same name. kwargs: Additional keyword arguments that are passed to ``op``. 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 operation if ``graph=False``, otherwise the graph representation of the operation. Examples: Reverse order of elements along an axis: >>> x = np.random.uniform(size=(16, 20)) >>> einx.vmap_with_axis("a [b] -> a [b]", x, op=np.flip).shape (16, 20) Roll elements along two axes: >>> x = np.random.uniform(size=(16, 20)) >>> einx.vmap_with_axis( ... "a ([b c]) -> a ([b c])", ... x, ... op=partial(np.roll, shift=(2, 2)), ... b=2, ... ).shape (16, 20) Compute sum along axis: >>> x = np.random.uniform(size=(16, 20)) >>> einx.vmap_with_axis("a ([b] c) -> c a", x, op=np.sum, b=2).shape (16, 20) cLg|]!}tj|"Sr)r%r& get_shaper(s rrz"vmap_with_axis..!s(KKKt{,,V44KKKrr)rrrrr)rrrU) rrrrrrrrrs rvmap_with_axisrsB KK7KKKQTXbHi/'9FGGYY1,,71::'9rr)c &t||fd||d|S)zASpecialization of :func:`einx.vmap_with_axis` with ``op="flip"``.r rrrrrr)rrrs rr r ,s& +v a&'s a aV` a aarshiftc ,t||fd|d|i|d|S)zjSpecialization of :func:`einx.vmap_with_axis` with ``op="roll"`` and ``kwargs={"shift": shift}``. r r)rrrrr)rr)rrrrs rr r 8sC          rc &t||fd||d|S)zCSpecialization of :func:`einx.vmap_with_axis` with ``op="softmax"``softmaxrrrs rrrOs' +v d)WRU d dYc d ddrc &t||fd||d|S)zGSpecialization of :func:`einx.vmap_with_axis` with ``op="log_softmax"`` log_softmaxrrrs rrr[s4 V  -wC  KU  r)NN)NT) r%rnumpyr functoolsrtypingrrrr numpy.typingnpt _op_namesjitr lru_cachertraceback_utilfilterrroBackendbool ArrayLikerr intr rrrrrrs 222222222222 V      h"h"h"  h"V+/     F   /3 A:A:A:A:kA: A:4<d* + A:  A:  A:-A:A:A:  A:H/3 bbb Kb4<d* +b  b - bbbb /3  K eCj !4<d* +    - ,/3 eee Ke4<d* +e  e - eeee/3    K 4<d* +    -      r