Pi/bddlZddlmZddlZddlmZddlmZm Z ddl mZ e Z ejdd+dZejd,d Zejjejd  d-d ed ejde eefde edfde ejedfdede jdejfdZee_ejj d-d ed ejde edfde ejedfdede jdejfdZdZejj d-d ed ejde edfde ejedfdede jdejfdZdZejj d-d ed ejde edfde ejedfdede jdejfdZdZejj d-d ed ejde edfde ejedfdede jdejfdZ dZ!ejj d-d ed ejde edfde ejedfdede jdejfdZ"dZ#ejj d-d ed ejde edfde ejedfdede jdejfdZ$d Z%ejj d-d ed ejde edfde ejedfdede jdejfd!Z d"Z&ejj d-d ed ejde edfde ejedfdede jdejfd#Z'd$Z(ejj d.d ed ejde edfde ejedfde jdejf d%Z)d&Z*ejj d.d ed ejde edfde ejedfde jdejf d'Z+d(Z,ejj d.d ed ejde edfde ejedfde jdejf d)Z-d*Z.dS)/N)util)partial)CallableUnioncdfd S)Nc4||||S)N)op)expr_in tensor_inexpr_outr backendcts b/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/einx/op/reduce.pyz.. s,aa9xBOOONr rrs``rrr $r)tracec||fD]L}|D]5}t|tjjjrt d6Mtj|g|g|g||\}}|d|dfS)NzConcatenation not allowed)rr)all isinstanceeinxexprstage3 Concatenation ValueErrorvmap_with_axis_stage3) r r rr rrootr tensors_out exprs_outs r reduce_stage3r% s (#>>HHJJ > >D$ 0 >?? > !<=== > >"7  I; BK q>9Q< ''rTc  tjj||\}}tjj|}t|dkrtjtj |dd|gd| Dz|dd}td| Dstdfd}tjj||}ntd t|ddkr%td t|dt|ddkr%td t|dtjtj |dd|gtj |ddgzd | Dz|ddd \}}td| DsFd| D tjj| fd}||fS)Nrrcg|]L\}}tj|tj|dtjfddMS.N)depth1depth2rrEquationnpasarraynewaxis.0kvs r zparse..&YAq ""1bjmmCO&DTZ^"__rT)csecse_in_markersc3^K|](}t|tjjjV)dSr)rrrrMarkerr1rs r zparse...s4XX$JtTY%5%<==XXXXXXrz No axes are marked for reductionct|tjjjr*r&tjjddgSgSdS)Nr)rrrrr9Axis)rkeepdimss rreplacezparse..replace2sR$ 0 788  I,11$::;;I   rz(keepdims cannot be given when using '->'z%Expected 1 input expression, but got z&Expected 1 output expression, but got cg|]L\}}tj|tj|dtjfddMSr(r+r0s rr4zparse..Gr5rc3^K|](}tjj|V)dSr)rrr is_markedr:s rr;zparse..Qs5OODI$..t44OOOOOOrcdh|]-}t|tjjj&|j.Sr rrrrr=name)r1axiss r zparse..RsA" 4IYI^8_8_ rc^t|tjjjo|jvSrrE)raxes_names_outs rrzparse..Ws+Zdi.>.CDD4I^3r)rr r!_clean_description_and_parametersrstage1parse_oplensolver,items_anyrr rr?mark) description tensor_shaper>r6 parametersr r r?rrJs ` @rparserVs"glLLZK   " "; / /B 2ww!||)// Y  1a, 7 7 8&,,..  "    XX'++--XXXXX A?@@ @     9#++GW==  GHH H r!u::??QSAZZQQRR R r!u::??Rc"Q%jjRRSS S IOO Y  1a, 7 7 8y!!"Q%(++, -&,,..  ,    1" OOOOOOO &.llnnNi&++4444G H rcdfd S)Nc*||fi|Srr )rStensorrkwargsrrs rrz..`s111QQvYYLL"(LLrrr rs``rrr`rrrSrYr r>rr6rUreturnc t|tj|f||d|\}}t |||||\}}|S)a Applies a reduction operation on the given tensors. The operation reduces all marked axes in the input to a single scalar. It supports the following shorthand notation: * When no brackets are found, brackets are placed implicitly around all axes that do not appear in the output. Example: ``a b c -> a c`` resolves to ``a [b] c -> a c``. * When no output is given, it is determined implicitly by removing marked subexpressions from the input. Example: ``a [b] c`` resolves to ``a [b] c -> a c``. Args: description: Description string for the operation in einx notation. tensor: Input tensor or tensor factory matching the description string. op: Backend reduction operation. Is called with ``op(tensor, axis=...)``. If ``op`` is a string, retrieves the attribute of ``backend`` with the same name. keepdims: Whether to replace marked expressions with 1s instead of dropping them. Must be None when ``description`` already contains an output expression. Defaults to None. 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 reduction operation if ``graph=False``, otherwise the graph representation of the operation. Examples: Compute mean along rows of a matrix: >>> x = np.random.uniform(size=(16, 20)) >>> einx.mean("a b -> b", x).shape (20,) >>> einx.mean("[a] b -> b", x).shape (20,) >>> einx.mean("[a] b", x).shape (20,) Compute sum along rows of a matrix and broadcast to the original shape: >>> x = np.random.uniform(size=(16, 20)) >>> einx.sum("[a] b -> a b", x).shape (16, 20,) Sum pooling with kernel size 2: >>> x = np.random.uniform(size=(4, 16, 16, 3)) >>> einx.sum("b (s [s2])... c", x, s2=2).shape (4, 8, 8, 3) Compute variance per channel over an image: >>> x = np.random.uniform(size=(256, 256, 3)) >>> einx.var("[...] c", x).shape (3,) )r>r6)r r)rVrtracer get_shaper%) rSrYr r>rr6rUr rs rreducer_^si\T[**622=E3R\GX%Wfh2wWWWFH Mrc (t||fd|||d|S)z7Specialization of :func:`einx.reduce` with ``op="sum"``sumr r>rr6r_rSrYr>rr6rUs rrara6 V  %'s  V`  rct|ddi|S)Nr rar%argsrZs r sum_stage3rj $ 35 3F 3 33rc (t||fd|||d|S)z8Specialization of :func:`einx.reduce` with ``op="mean"``meanrbrcrds rrmrm7 V  &7PS  Wa  rct|ddi|S)Nr rmrgrhs r mean_stage3rp $ 46 4V 4 44rc (t||fd|||d|S)z7Specialization of :func:`einx.reduce` with ``op="var"``varrbrcrds rrsrsrerct|ddi|S)Nr rsrgrhs r var_stage3rurkrc (t||fd|||d|S)z7Specialization of :func:`einx.reduce` with ``op="std"``stdrbrcrds rrwrwrerct|ddi|S)Nr rwrgrhs r std_stage3ryrkrc (t||fd|||d|S)z8Specialization of :func:`einx.reduce` with ``op="prod"``prodrbrcrds rr{r{rnrct|ddi|S)Nr r{rgrhs r prod_stage3r}rqrc (t||fd|||d|S)zASpecialization of :func:`einx.reduce` with ``op="count_nonzero"`` count_nonzerorbrcrds rrrs>          rct|ddi|S)Nr rrgrhs rcount_nonzero_stage3r*s $ =? =f = ==rc (t||fd|||d|S)z7Specialization of :func:`einx.reduce` with ``op="any"``anyrbrcrds rrr.rerct|ddi|S)Nr rrgrhs r any_stage3r=rkrc (t||fd|||d|S)z7Specialization of :func:`einx.reduce` with ``op="all"``rrbrcrds rrrArerct|ddi|S)Nr rrgrhs r all_stage3rPrkrc &t||fd||d|S)z7Specialization of :func:`einx.reduce` with ``op="max"``maxr r>rrcrSrYr>rrUs rrrT& +v b%(G b bWa b bbrct|ddi|S)Nr rrgrhs r max_stage3r`rkrc &t||fd||d|S)z7Specialization of :func:`einx.reduce` with ``op="min"``minrrcrs rrrdrrct|ddi|S)Nr rrgrhs r min_stage3rprkrc &t||fd||d|S)z=Specialization of :func:`einx.reduce` with ``op="logsumexp"`` logsumexprrcrs rrrts4 V  +h  S]  rct|ddi|S)Nr rrgrhs rlogsumexp_stage3rs $ 9; 9& 9 99rr)NT)NNT)NN)/rrnumpyr- functoolsrtypingrr numpy.typingnptrrQjitr% lru_cacherVtraceback_utilfilterstrTensorboolBackend ArrayLiker_rarjrmrprsrurwryr{r}rrrrrrrrrrrr rrrs """"""""     (((  (????D   #'.2 LLL KL hmLD$J L 4<d* + L  L-L [LLL  L^ #'.2    K D$J 4<d* +    -  [    444#'.2    K D$J 4<d* +    -  [    555#'.2    K D$J 4<d* +    -  [    444#'.2    K D$J 4<d* +    -  [    444#'.2    K D$J 4<d* +    -  [    555#'.2  KD$J4<d* +    -  [(>>>#'.2    K D$J 4<d* +    -  [    444#'.2    K D$J 4<d* +    -  [    444#'.2 ccc KcD$Jc4<d* + c - c  [ cccc444#'.2 ccc KcD$Jc4<d* + c - c  [ cccc444#'.2    K D$J 4<d* +  -   [    :::::r