ZЄi<dddlmZddlZddlZddlZddlmZmZmZm Z erddl m Z m Z ddl Z ddlmZmZmZddlmZmZmZddlmZdd lmZdd lmZd Zd ZdadWd ZdXd Z dYdZdZ! d[ d\dZ"GddZ# dY d]dZ$ d[ d^dZ%Gdde&Z'Gdde&Z(ddl)m*Z*d_dZ+GddZ,e,e jZfZ.Gdde,Z/Gdde,Z-d`dad Z0dYdbd!Z1dcd"Z2Gd#d$Z3ddd%Z4ded&Z5dfd'Z6dd(l7m8Z8dd)l9m:Z:e:e,e jZe,jve,`e>d,e>d-e>d.e>d/e>d0e>d1e>d2e>d3e>d4e>d5e>d6e>d7e>d8d9:e>d;d9:e>dd>d =e>d?d =e>d@d =e>dAd =e>dBd =e>dCd =e>dDd =e>dEdFGe>dHdFGe>dId =e>dJddFKe>dLd dFMe>dNd dFMe>dOd dFMe>dPd dFMe>dQd dFMe>dRd dFMe>dSd dFMe>dTdd dFUe>dVd dFMe8e j~jjd dFMZAy)i) annotationsN)AnyOptional TYPE_CHECKINGUnion)CallableSequence) tree_flattentree_maptree_unflatten) _match_levelsDimEntryndim_of_levels)EnableAllLayers)_PyInstDecoder) TensorInfoTc>t|xr|jdS)z4Check if opcode is relevant for variable assignment.STORE_)bool startswith)opcodes `/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/functorch/dim/__init__.py _relevant_oprs 66,,X6 77c|S)z8Handle tensor conversion for torch function integration.)tensors rhandle_from_tensorr!s MrDimc*t|||SdS)zCreate a new Dim object.)r )namesizes r _create_dimr%&s tT-T 662 66rcL d}d  t}||}tj}| td|j} | td|j }|j }t|| tjdk\r# jdk(r j jt jrd n3 jdk(r j j|dk(r dk(r td  } |k7rd d fd }(t|k7rtd |d t|dk(r |d~Sg}t!|D]}|j#||t%|~S#~wxYw)a Create and return one or more Dim objects. Uses bytecode inspection to determine variable names when possible. Args: n (int, optional): The number of dimensions to create. Can be omitted if sizes is specified. sizes (List[Optional[int]], optional): A list the same size as the number of dimensions to be created, specifying each dimensions size, or None to leave the size unset. Returns: Union[Dim, Tuple[Dim, ...]]: Single Dim if n=1, tuple of Dims otherwise. Examples: >>> batch, channel, width, height = dims(4) >>> batch, channel, width, height = dims(sizes=[None, 3, 224, 224]) >>> single_dim = dims(1) r"rUnable to get current frameUnable to get caller frame PRECALLr UNPACK_SEQUENCEzTdims() must be assigned to a sequence of variable names or have argument n specifiedcd}|krj}|sd|}dnj|nd}t||SNdr)r#nextr%)ir#r$decoder found_ndimssizess r genobjectzdims..genobjectnsSD;||~1#w  $058dDtT* *r expected  sizes but found )r2intreturnr )leninspect currentframe RuntimeErrorf_backf_codef_lastirsys version_inforr1roparg SyntaxError ValueErrorrangeappendtuple) nr5specified_ndimsframecodelastir6resultr2r3r4s ` @@rdimsrP+s*OK e*}  "E }899 LLE= =;< <||  u-   w &~~9,    ( )K ^^ !2 2!--/K LLN b a!j*O / )K +  U!>y(99J3u:,WX X a Q< ' (A MM)A, ' (V} EsD"F +3F F#ceZdZUdZded<ded<ded< d dd Zdd Zdd Zdd Zdd Z ddZ ddZ e ddZ ddZddZddZddZe d d!dZy)"DimLista( A list of first-class dimensions that can be bound to tensor dimensions. A DimList can be in one of two states: 1. Unbound: Created with just a name, no specific dimensions yet 2. Bound: Either created with specific dimensions/sizes, or bound later via bind() or bind_len() Optional[str]_namez list[Dim]_dimsr_boundNc||_g|_d|_t|tr|j |y|g}t |D]l\}}t|tr=|jr|j|nd|}|jt||S|jt|n|j|yy)z Initialize a new DimList object. Args: len_or_dims: Optional length (int) or sequence of dimensions/sizes name: Optional name for the dimension list FNdim) rTrUrV isinstancer9bind_len enumeraterHr _set_dims)self len_or_dimsr#rPr2itemdim_names r__init__zDimList.__init__s   k3 ' MM+ &  $D$[1 +4dC(59ZZ$**aS1s1#YHKKHd 34KKD *  + NN4 %rc d|_||_y)z%Set the dimensions and mark as bound.TN)rVrU)r]rPs rr\zDimList._set_dimss  rc\|jr=t|j|k7r$tdt|jd|yd|_g|_t |D]G}|j r|j |nd|}|jj t|Iy)z Bind this DimList to a specific length. Args: size: Number of dimensions to bind to Raises: DimensionBindError: If already bound to a different size zDimlist has size z but it is being bound to size TrXN)rVr;rUDimensionBindErrorrGrTrHr )r]r$r2r`s rrZzDimList.bind_lens ;;4::$&('DJJ'88WX\W]^' DKDJ4[ 115djj\!-3qc !!#h-0 1rct|dr t|ds tdt|}|j|t |D]"\}}t ||j |_$y)z Bind this DimList to specific sizes. Args: sizes: Sequence of sizes for each dimension Raises: ValueError: If sizes is not a sequence __len__ __getitem__zexpected a sequenceN)hasattrrFr;rZr[r9rUr$)r]r5r$r2dim_sizes rbindz DimList.bindsdui(}0M23 35z d$U+ /KAx!$XDJJqM  /rcZ|js tdt|jS)NDimList not bound)rVrdr;rUr]s r_sizez DimList._sizes#{{$%89 94::rc"|jS)z7Return the size (number of dimensions) of this DimList.rnrms rr$z DimList.sizeszz|rc||_y)z(Set the bound status (for internal use).NrV)r]bs r _set_boundzDimList._set_bounds  rc|jS)z&Property to check if DimList is bound.rrrms ris_boundzDimList.is_bounds{{rc"|jS)z!Return the length of the DimList.)r$rms rrfzDimList.__len__syy{rc|js tdt|tr7|dks|t |j k\r t d|j |St|tre|jt |j \}}}g}t|||D] }|j|j |"t|Std)Nrlrzindex out of boundszexpected an int or a slice) rVrdrYr9r;rU IndexErrorsliceindicesrGrHrIrF)r]keystartstopsteprOr2s rrgzDimList.__getitem__s{{$%89 9 c3 Qw#TZZ0 !677::c? " U # # C O < E4F5$- - djjm, -= 9: :rc|jr&ddjd|jDdS|jd|jSy),Return string representation of the DimList.(z, c32K|]}t|ywN)repr).0rXs r z#DimList.__repr__..s Asc As)*z)rVjoinrUrTrms r__repr__zDimList.__repr__ sL ;;tyy Adjj AAB!D D ZZ #tzzl# #'rc"|jS)r)rrms r__str__zDimList.__str__s}}rc2tj||||Sr)_Tensor__torch_function__)clsfunctypesargskwargss rrzDimList.__torch_function__s))$tVDDrNN)r^zOptional[Union[int, Sequence]]r#rS)rPlistr:None)r$r9r:r)r5z Sequence[int]r:rr:r9)rsrr:rr:r)r|zUnion[int, slice]r:Union[Dim, tuple[Dim, ...]]r:strrN rrrrIrrIrzOptional[dict]r:r)__name__ __module__ __qualname____doc____annotations__rar\rZrjrnr$rtpropertyrvrfrgrr classmethodrrrrrRrRs   L7;"!3!!: 1,/& ;" ' !% EEE E  E EErrRct|}|gt|tr|j||S|jt |t |D]\}}| ||j |_|S)z>Create a DimList object with the given name and optional size.)r#)rRrYr9rZr;r[rUr$)r#r$dimlistr2ss r_create_dimlistr(s{4 G  dC   T " N   SY '!$ .1=,-GMM!$) . Nrc d}d | t|}||}tj}| td|j} | td|j }|j }t|| tjdk\r# jdk(r j jt jrd n3 jd k(r j j|dk(r dk(r td  } |k7rd d fd }|(t||k7rtd |d t||dk(r!|d}t!|| |d~Sd~Sg}t#|D].} || }||| nd} |j%t!|| 0t'|~S#~wxYw)zt Create and return one or more DimList objects. Similar to dims() but creates DimList objects instead. r"rNr'r(r)r,r r-zXdimlists() must be assigned to a sequence of variable names or have argument n specifiedcld}|krj}|s d|}d|Sj|Sr/)r#r1)r2r#r3r4s rr6zdimlists..genobjectosDD;||~1#w K Krr7r8)r2r9r:r)r;r<r=r>r?r@rArrBrCrr1rrDrErFrrGrHrI) rJr5rKrLrMrNr6r#rOr2r$r3r4s @@rdimlistsr9sOK e*}  "E }899 LLEA =;< <||  u-   w &~~9,    ( )K ^^ !2 2!--/K LLN b a!n*O / )K   U!>y(99J3u:,WX X a Qfile)rBrhrtyperHrprintstderr)rrrB safe_argsargs r _safe_printrsI' 3 $CS N)B   qc!3!3 4A6 7   SX & '  900SZZ0rceZdZd dZddZeddZeddZddZe d ddZ e ddZ dd Z dd Z dd Zdd Zy)rctd)Nz+_get_levels must be implemented by subclassNotImplementedErrorrms r _get_levelsz_Tensor._get_levels!"OPPrctd)Nz+_get_tensor must be implemented by subclassrrms r _get_tensorz_Tensor._get_tensorrrctd)Nz$ndim must be implemented by subclassrrms rndimz _Tensor.ndims!"HIIrcBtd|jDS)Nc3^K|]%}|jr|j'ywr) is_positionalrXrls rrz_Tensor.dims..sR@QQUUWRs--)rIrrms rrPz _Tensor.dimssRd&6&6&8RRRrc|jSr)rrms rrXz _Tensor.dims yyrNc|i}tr|tjjurt |dk(r|st |dt tjfrht |dt tjfrDtj|ddd}tj|ddd}|r |r|j|j|jjdk(r|jjdk(r|jjr|jjr|jxs |j}g}|jD]} | |vs|j| |jD]} | |vs|j| tj||||S|tjj urddlm} | |||||S|tjj&ur?ddlm} t |dk(r| |d|d|dyt+d t ||tjj,ur|dj/dS|tj0ur t1|i|S|tj2ur t3|i|S|tjj4usJ|tj6j4us.|tj6j8us|tj4ur t5|i|St j;||||S) Nrr Fensure_batchedensure_present)getitemsetitemr*z%Expected 3 args for __setitem__, got ) DOT_OPTIMIZEDrTensor__mul__r;rYrrcreaterrXis_floating_point has_devicelevelsrHcreate_delayedrgfunctorch.dim._getsetitemr __setitem__rrFrfr$softmaxstacksplit_VFsplit_with_sizes_torch_function_fallback) rrrrrlhs_inforhs_inforrlevelrrs rrz_Tensor.__torch_function__s >F TU\\%9%99D QtAw%,,(?@tAw%,,(?@&,,GE%&,,GE%   3 3 ++-2 ++-2!99;$OO==?&.%8%8%OH.wrap_tensorCs8c5<<0!11],A,M rcdt|tjrj|duS|Sr)rYrr from_batched)rrguards r unwrap_tensorz7_Tensor._torch_function_fallback..unwrap_tensor_s0c5<<0 --c3HPT3TUU r)rrr:r)POINTWISE_OPTIMIZEr pointwiser rrrH batchedtensorrrrrYrr[todevicerrr rrr rinplace_update_layers)rrrr is_pointwise flat_argsspecinfosfinforr2rml unflat_args unflat_kwargsrOrbatchedrrrrs @@@rrz _Tensor._torch_function_fallbacks *Mdm6M6M.M &f~6 4 $"$(*  4A$$QL(8%@D LL #t'9'9'EEE(0T__,0KK)![[4E%eX666M1%,,U34 4 $U+ :4DKK3![[F,8!'+@+G+G!H&vt{{MJB#5b#9IaL  :*8 4)H &K;8-8F &%,,/"6** V44] + 7u$U+ ?4D..:"00G,8")**-B-I-I"J//E#5g#>IaL  ?*8 4)H &K;8-8F  &%,,/$V,) 7 7, v6- 7 7 7s J5 JB J, JJ c"ddlm}||||S)z2Set values in tensor using first-class dimensions.rr)rr)r]indexvaluers rrz_Tensor.__setitem__is5tUE**rcftj|dd}|D]}t|trt|tj r2t|t s"t j j|g|cS|jtj jt|f|f|zcS|s9|jtj jt|f|f|zS|j}|9|jtj jt|f|f|zS|j}g}g}g}|D]} |D]4} | jr| j| us'td| d|D]4} | jr| j| us'td| d|jt!| |j| j"|jd|j%|t'|j#} t'|j)} |j%| |j%| |j+|||j-}t j/|||j0}|S)aq Expand tensor by adding new dimensions or expanding existing dimensions. If all arguments are Dim objects, adds new named dimensions. Otherwise, falls back to regular tensor expansion behavior. Args: args: Either Dim objects for new dimensions or sizes for regular expansion Returns: New tensor with expanded dimensions Example: >>> i, j = dims() >>> t = torch.randn(3, 4) >>> expanded = t[i].expand(j, k) # Add j, k dimensions >>> expanded2 = t[i].expand(2, 4) # Regular expand with sizes Frzexpanding dimension z# already exists in tensor with dimsr)rrrYr rrrexpandrrrrrrXrdrHrr$extendrstride as_stridedstorage_offsetrr)r]rrrdatar new_levels new_sizes new_stridesr0r new_level orig_sizes orig_strides expanded_datarOs rr z_Tensor.expandrst&  eER Cc3'dELL1*T7:S <<..t;d;;22 ++d4j]TGdN  ** ##d4j]TGdN  {{ <** ##d4j]TGdN %'    "A **,1A,.qc1TU  (   ..0Y]]_5I,.qc1TU    hqk *   QVV $   q ! "$ &!$))+& DKKM* $<( ;@S@S@UV '' z4??S rc%ddlm}ddlm}m}ddlm%ddd}d%fd }dfd }g} g} t|ttf} t|ttf} | rd| rb|} |}t| t|k7r$td t| d t|d | j| | j|n"| j|| j|tjdd}g}g}g}t!t| D]}|| |d \}}|rgt|d k(r|jt#?||d }|j|t|dk(rjt|d k(r|j|j$g}t!dt|D]`}|||}d}t!t|D]}|||k(s |j'|d}n|s|||j|bd}t!t|D]}|||k(s |}n| ||@t)|D]\}}|j+|dz|z||j|~|j|| |t|d kDr|j,Jd||j,|j$|}|j/}g} g}!t!t|D]e}|||vr4t| d k(r| j||-| dxx||zcc<>| j|||!j||g|j1| |_|!|_d}"| D]}#t|#ttfsd}"n||g|| |"}$||$S)z< Index tensor using first-class dimensions. r )r)getsetitem_flatinvoke_getitem _wrap_dimFcJt|ttfr t|dfSy)NT)NF)rYrIr)r check_firsts r maybe_dimpackz$_Tensor.index..maybe_dimpacks!#t}-Cy$&rc~|jd}|jrtdt||S)NFz)expected a dimension specifyer but found )ris_none TypeErrorr)rr0rr]s rparse_dim_entryz&_Tensor.index..parse_dim_entrys;!TYY.Ayy{"KDQRG9 UVVHrc|jr7td|jjzdjdtdt |j d)N dimension z not in tensor of z dimensionsz not in tensor)rr!positionrrrX)r0r]s rdim_not_presentz&_Tensor.index..dim_not_presentsb   !9 ::LTYYKWbc *T!%%']O> JKKrzdims (z) and indices (z) must have the same length)rrTNz2Cannot perform dimension flattening on None tensorr")F)rrrrr:ztuple[Any, bool])rrr:r)r0rr:r) _dim_entryr _getsetitemrr_wraprrYrIrr;r!r rHrrrGrrpopr[insertrr$reshape)&r]rPr{rrrrr"r& dims_list indices_listlhs_listrhs_listdims_seq indices_seq self_infor to_flattendims_list_flatr2m is_dimpackfirstrestjr0removedk first_idxr rearrangedr5rreshape_levels has_dimpacksidxrrs&` @rrz _Tensor.indexs .@$    L,. >@ dUDM2gt}5 H!K8}K 00S]O?3{;K:LLgh   X &    ,   T "    (%%dE59 " " s9~&, EA))A,EJMAzq6Q;"))(*5'!-%%e,q6Q;z?a'%%i&6&67q#a&) #A'!-A#G"3z?3"%a=A-&NN1-&*G! " #'*KKN #! s:/A!!}-$% $#E*%dOTUUrr: list[Any]r:Optional[torch.Tensor]r)r:ztuple[Any, ...]rr) rrrrIrrIrdictr:r)rrrrr:r)rr r:r)rPzCUnion[int, Dim, tuple[Union[int, Dim], ...], list[Union[int, Dim]]]r{zsUnion[int, slice, torch.Tensor, tuple[Union[int, slice, torch.Tensor], ...], list[Union[int, slice, torch.Tensor]]]r:rr)rrrrrrrrPrXrr staticmethodrrr rrrrrrrsQQJJSS !% ZKZKZK ZK  ZK ZKZKxK7K7$K7,1K7;?K7 K7K7Z+SjX$QX$ X$ X$t VrrceZdZUded<ded<ded<ded<ded<ddd Zedd Zedd Zedd Z e jdd Z eddZ ddZ ddZ ddZej Zy)r r9_levelrrTrnrH_range _batchtensorcd||_||_t|_tdz ad|_d|_yNr )rTrn_n_dims_createdrLrMrN)r]r#rs rraz Dim.__init__{s0  % 1  rcyrPrrms rrzDim.ndimsrct||uSrr)rrs r check_exactzDim.check_exactsCyCrcj|jdk(rtd|jd|jS)Nr"r$z is unbound)rnrFrTrms rr$zDim.sizes0 :: z$**[AB Bzzrc|jdk(r||_y|j|k7r'tdt|d|jd|y)Nr"zDim 'z*' previously bound to a dimension of size z$ cannot bind to a dimension of size )rnrdr)r]vs rr$zDim.sizesX :: DJ ZZ1_$T |#Mdjj\Z667S: rc |jdk7S)z1Return True if this dimension is bound to a size.r"rprms rrvz Dim.is_boundszzRrcz|j$tj|j|_|jS)z Get a tensor representing the range [0, size) for this dimension. Returns: A 1D tensor with values [0, 1, 2, ..., size-1] )rMraranger$rms r _get_rangezDim._get_ranges, ;; ,,tyy1DK{{rc|jHtjjj |j d|j |_|jS)z Get a batched tensor representation of this dimension. Returns: A batched tensor created from the range tensor r)rNr_C _functorch_add_batch_dimr\rLrms r_get_batchtensorzDim._get_batchtensorsM    $ % 3 3 B B!1dkk!D    rc|jS)z&String representation of a Dim object.)rTrms rrz Dim.__repr__s zzrN)r")r#rrr9r:rr)rrr:r)rXr9r:rrr: torch.Tensorr)rrrrrarrrrUr$setterrvr\rarobject __format__rrrr r ts K J J ""((!    [[    ! ""JrceZdZUded<ded<ded<ded<ded <d ed <d ed <eddZeddZe ddZe ddZ ddZ ddZ ddZ ddZ d dZd!dZy)"rrH_tensorrNlist[DimEntry]_levelsr _has_devicez$Optional[Callable[[], torch.Tensor]]_delayedzOptional[Callable] _delayed_origzOptional[tuple] _delayed_argsc:td|jDS)Nc3DK|]}|jrdndyw)r rN)rrs rrzTensor.ndim..sGQ)1q0Gs )sumrkrms rrz Tensor.ndimsG$,,GGGrct||uSrrT)rothers rrUzTensor.check_exactsE{c!!rcd}d}t|D]a\}}|jrG|dk(s1|dz|jk(sJd|d|j|j}]|dz }c|dk(s|dk(s Jd||s|S|}||_||_||_d|_d|_d|_d|_ |jt|k(s%Jd|jd t|d |S) a0 Create a functorch Tensor from a regular PyTorch tensor with specified dimension levels. This is the primary way to create Tensor objects with first-class dimensions. Args: tensor: The underlying PyTorch tensor levels: List of DimEntry objects specifying the dimension structure has_device: Whether the tensor is on a device (not CPU) Returns: A new Tensor instance with the specified dimensions, or a regular torch.Tensor if there are no named dimensions rr z/Positional dimensions must be consecutive, got z then r"z0Final positional dimension must be 0 or -1, got Nz Tensor has z dimensions but z levels provided) r[rr%rirkrlrNrmrnrorXr;) rrrr seen_dimslastr2rrOs rrzTensor.from_positionals<$ f% DAq qyD1H $<EdV6RSR\R\R^Q_`<zz|Q  qyDBJ >tf E &M'"##zz|s6{* &**,'7F }DT U * rc|}d|_||_||_d|_|_|_dfd }||_|S)zP Create a delayed tensor that defers the operation until later. Ncg}D]?}t|dr |j|j/|j|A|S)Nr)rhrHr)unwrapped_argsrrorigs revaluate_delayedz/Tensor.create_delayed..evaluate_delayedsQN /3 ."))#//*;<"))#.  / ( (rrc)rirkrlrNrnrorm)rr{rrrrOr|s `` rrzTensor.create_delayedsM'"## )+ rct|drB|j6|j*|j|_d|_d|_d|_|jS)zAGet the underlying tensor, handling delayed operations if needed.rmN)rhrmrirnrorms rrzTensor._get_tensor,sR D* % ) $ ==?DL DM!%D !%D ||rc|jS)zGet the dimension levels.)rkrms rrzTensor._get_levels;s ||rc|jS)z/Get whether this tensor has device information.)rlrms r_get_has_devicezTensor._get_has_device?src|j3|j|j|j|_|jS)zDGet the batched tensor representation, creating it lazily if needed.)rN_add_batch_dimsrrrms rrazTensor._get_batchtensorCsF    $ $ 4 4  "D$4$4$6!D    rct|} d}d}td}d}d}t|D]f\}} | jr| j s;| j j |kr| j j }|}|}|dz }h|dk(r|S|Jtjjj||t|}t||<)Nr"infrr ) rfloatr[r rrXrLrr^r_r`r9r) r]tlevels_rmin_real_index min_index min_valuer2r>rs rrzTensor._add_batch_dimsKsgNIe IAA!&) 1yy{??,)1K$%EEGNN $% )*FA  B= =##221iYPA%-ZF> "+rc$ddlm}||g|}|S)z&Reorder the dimensions of this tensor.r )order)_orderr)r]rPrrOs rrz Tensor.orderhs!t#d# rNr)rtrr:r)rrdrrjrrr:Union[_Tensor, torch.Tensor]) r{rrrIrrjrrr:rrGrEr)rrHrrFr:rH)rPrr:r)rrrrrrrrUrrrrrrarrrrrrrs ##(( 22%%"" HH""6!6+96GK6 %66p#(2@NR 8  !'2; :rrcl|s tdt|tstj||}|Sg}g}|D]R}t j |dd}|j||jD]}||vs|j|Tt||_ g} |D]G}|jJdt|j|j|} | j| It|} d} |9t|tr|dk(s$dd lm} | || d} |j#|}|} tj| | }|j'| t)|t*j-|||r|dj.}|Sd }|S#t$rt%d |d dwxYw) a$ Stack tensors along a new dimension. Args: tensors: Sequence of tensors to stack new_dim: The new Dim to create for stacking dim: The dimension position to insert the new dimension (default: 0) Returns: Stacked tensor with the new dimension z-stack expects a non-empty sequence of tensors)rXFrNz*Cannot stack tensors with None tensor datarr rz Dimension z does not exist in inputsT)rFrYr rrrrrHrr;r$rrrr9r)rrr!r+rrrr)tensorsnew_dimrXrOrrrrrinputsmatched_tensorrrawdimrr0rB tensor_results rrrps HII gs #W#. M E ,  5O T[[ ,EM)$$U+ ,,w> D TQYEFF,/?Ic4 '$E Ci../5 E>C   { ;Cs$& rc|jj||js.|xj|j j zc_yy)z#Add a dimension entry to this part.N)rPrHrrrXr$)r] dim_entrys rrHzDotPart.appendBs? #&&( OOy}}33 3O)rN)r:r)rrr:r)rrrrrarHrrrrr9s4rrcXg}d}|D]7}t|jdk7rd}|j|j9|j t dt |j|j |}|s|S|Dcgc]}|j}}|j|Scc}w)zJ Prepare tensor for dot product by matching levels and reshaping. Fr Tz)Cannot perform dot product on None tensor) r;rPr rr>rrrr,)parts tensor_infor needs_reshapepartrOviews r dot_preparerIsJM% tyy>Q  M$))$% !FGG ;--{/A/A: NF  ). .DOO .D . >>$  /sB'c>g}d}|D]7}t|jdk7rd}|j|j9|rCg}|D]+}|j|j j -|j |}tj||d}|S)zE Finish dot product by reshaping result and creating Tensor. Fr T) r;rPr rHrXr$r,rr)rrrrrnew_sizerrs r dot_finishrasMM( tyy>Q  MTYY'( " .E OOEIIK,, - .%--h7 **=-NM rctj|dd}tj|dd}|r|stj||S|j |jJd|jj |jj t t t t d fd }dgt|jz}t|jD]<\}}d} t|jD]\} } || k(s | } d|| <n|||| >t|jD]\}} ||r || d|tjtk7r2D]-} | |jvs| |jvs!td| tjdk7rCtg|} tg|}tj| |}tg|Stg|} tg|}tj| |}tg|S) z Perform dot product between two tensors along specified dimensions. Args: lhs: Left-hand side tensor rhs: Right-hand side tensor sum_dims: Dimensions to sum over (contract) Returns: Result of dot product FrNz*Cannot perform dot product on None tensorsc| v}||nd}| |nd}|rj|y|dk(|dk(k(r j|y|dk7rj|y j|y)z?Insert dimension into appropriate part based on stride pattern.Nr)rH) r0lhs_idxrhs_idxreduced lhs_stride rhs_stride lhs_strideslo_dimslr_dimslro_dims rhs_stridesro_dimssum_dimss r insert_dimzdot..insert_dimsxx--4-@[)a -4-@[)a  NN1 aZ1_5"qq!q!rTz$summing over non-existent dimension r)r0rrrrrr:r)rrrmatmulrr rr;rr[rPrFrbmmrmm)lhsrhsrrrrrhs_seenr2 lhs_levelrr: rhs_levelr0 lhs_tensor rhs_tensorrOrrrrrrs ` @@@@@@rdotrwsW  U5QH  U5QH ||C%% ?? &8??+F4 F//((*K//((*KyHiGiGiG"""wX__--H"(//2* 9%hoo6 LAyI%"    9a)*"(//2+ 9{ y$ *+  7<<CM) MA'AX__,D #Gs!KLL M  8==Q (GW!=xH  (GW!=xH :z28Wg6??!'7!3X>  '7!3X> *j17G,f55r)r)) wrap_typec>t|trtj|||Stj|dd}|j Jdt j|j |j|j}tj|||S)a Index a regular tensor by binding specified positions to dims. This converts a regular tensor to a first-class tensor by binding the specified positional dimensions to Dim objects. Args: positions: Tuple of dimension positions to bind dims: Dim objects or tuple of Dim objects to bind to Returns: First-class tensor with specified dimensions bound FrzCannot index None tensor) rYrrrrrrrrr)r] positionsrPrrOs rrrs$ }}T9d33   T% ND ;; ">$>> "  # #DKKdoo NF ==D 11rc tttj|}tt|t |g|i|yr)getattrrrsetattrrr))r#rrr{s r_defrs- 5<< &D GT57778rmeanrrallamaxaminaminmaxany count_nonzero logsumexpnanmeannansumprodstdr)keepdim_offsetvarmax) single_dimrargmaxargminkthvaluemedian nanmedianmodesortF)reduceargsortunbindchunk) dim_offsetrcummax)rrcummincumprodcumprod_cumsumcumsum_ logcumsumexprenorm)rrrr)rrSr:r)rrdr:rdr)r#rr$ Optional[int]r:r r)rJrr5Optional[list[Optional[int]]]r:r)r#rr$z)Optional[Union[int, list[Optional[int]]]]r:rR)rJrr5rr:z#Union[DimList, tuple[DimList, ...]])rrrrr:r)r)rrrrrXr9r:r)rrrrrXrr:rI)rrrXrrrr:r)r list[DotPart]rrr:rd)rrrrdr:r)rrrrrrr:r)r]rrrrPrr:r)r#rrrrrr:r)B __future__rdisr<rBtypingrrrrcollections.abcrr rtorch.utils._pytreer r r r'rrr_enable_all_layersr_py_inst_decoderr _tensor_inforrrrQrrr%rPrRrr Exceptionrrdrrrr TensorLiker rrrrrrrfunctorch.dim._wrapr)functorch.dim.wrap_typerrrrrnn functionalrrrrrs "  662 FF??/,$ 8  7 EI``$A` `FWEWEvBF > $EIVV$AV(Vr Y    1BVBVJu|| $ K#'K#bhWhVAH}@; 4 4  0,V6r&- '5<<!;!;< L2<9 V U U V V YU _[YXV U1U1UtUtX$X$ZD!X$[T"VVEYuX$W5)X$u-X$u-Y4.ZD/X$u-Y4.^U3X!U;Y4. ##++U Kr