L iUddlZddlmZmZmZmZmZmZddlm Z m Z ddl Z ddl m Z m Z ddlmZddlmZddlmZmZddlmZerdd lmZdd lmZeeZe ed <n eZeeed fZgZe e!ed <edZ"e dZ#dee#e"fdee#e"ffdZ$dZ%de!de fdZ&dededeed ffdZ'de defdZ(de dede)fdZ*de de d e de fd!Z+ dPd"e d#eed fde)d$eede f d%Z, dPd"e d#eed fde)d$eede f d&Z-de de d e de fd'Z.de de d e de fd(Z/dee efde fd)Z0de de fd*Z1deee fde fd+Z2e$ dPd,ddd-dee efdedee)d$eedee de f d.Z3e$ dPd,ddd-dee efdedee)d$eedee de f d/Z4e$ddd0de ded$eedee de f d1Z5e$ddd0de ded$eedee de f d2Z6e$ dPd,ddd-dee efdedee)d$eedee de f d3Z7e$ dPd,ddd-dee efdedee)d$eedee de f d4Z8e$ dPd,ddd-dee efdeedee)d$eedee de f d5Z9e$ dPd,ddd-dee efdeedee)d$eedee de f d6Z:e$ dPd,ddd-dee efdedee)d$eedee de f d7Z;e$ dQd,ddd-dee efdede)d$eedee de f d8ZZ>e$ dRd,ddd-dee efd?ee?dedee)d$eedee de fd@Z@dee efdedAee)dBeeee?fdee)d$eedee dCee)de fdDZAe$ dSdd,dddEdee efdedAee)dFeeee?fdee)d$eedee de fdGZBe$ dSdd,dddEdee efdedAee)dFeedee)d$eedee de fdHZCe$ddd0dee efded$eedee de f dIZDe$ddd0dee efded$eedee de f dJZEe$ddd0dee efded$eedee de f dKZFe$dLdddMdee efd?e?dedNe?d$eedee de fdOZGy)TN)AnyCallableOptional TYPE_CHECKINGTypeVarUnion) ParamSpec TypeAlias) sym_floatTensor)corresponding_real_dtype)_docs)is_masked_tensor MaskedTensor)as_masked_tensor)DimsType)_dtype DimOrDims.__all___T_Pfuncreturnctt|jdd}|$tjd|jdn||_t j|j|S)zlDecorator that applies docstring templates to function docstring and returns the function instance. _docstringNz&No documentation string available for zb. PyTorch team should run `python tools/update_masked_docs.py` to generate the missing docstrings.)getattrr__name__warningswarn__doc__rappend)r doc_strings W/mnt/ssd/data/python-lab/Trading/venv/lib/python3.12/site-packages/torch/masked/_ops.py_apply_docstring_templatesr$$se 4==/ gd?g} |jd@vr%dA|f} | j tj B} nI|jdCvr|dDf} n6|jdk(r$|f} | j tj B} n|f} ||j\} } | Dcgc]G}dEj|j||jdFd;dGdHjI}}| Dcgc]}}dEj|j|jdId;dG|jdFd;dGdHj|jdId;d;Jj}}|j|vrdK}gdL}n7|j|vr'dM}gdN}| j tj B} nJ|| g| dO| i}|j|jdPz|jz||jdQjdR| DdQjdS| DdTjt| jdQjtt| dTjt| jdEjt| jdEjt| jdEjt|jdU |j|vrjt|jtjdGtj Bt|jtjdGtj"Bt|jtjdGtj BV|jdk(rjt|jtjdGtj Bt%dWXn0|j|vr j||jYnJjdEj|j'ZjdEj|j'[|j)Dcic](\}}|j+|r||j'*}}}|jfd\j)D|j,'d]j|Dcgc] }d^|d_|d` c}}n |j,}|j'|Scc}wcc}wcc}}wcc}w)azuA utility function called from tools/update_masked_docs.py script to update the module torch.masked._docs.py zI{function_name}(input, {operation_args}, *, {operation_kwargs}) -> TensorzReturns {operation name} of all the elements in the :attr:`input` tensor along the given dimension(s) :attr:`dim` while the :attr:`input` elements are masked out according to the boolean tensor :attr:`mask`.aIf :attr:`keepdim` is ``True``, the output tensor is of the same size as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the output tensor having 1 (or ``len(dim)``) fewer dimension(s). The boolean tensor :attr:`mask` defines the "validity" of :attr:`input` tensor elements: if :attr:`mask` element is True then the corresponding element in :attr:`input` tensor will be included in {operation name} computation, otherwise the element is ignored. When all elements of :attr:`input` along the given dimension :attr:`dim` are ignored (fully masked-out), the corresponding element of the output tensor will have undefined value: it may or may not correspond to the identity value of {operation name} operation; the choice may correspond to the value that leads to the most efficient storage of :attr:`output` tensor. The mask of the output tensor can be computed as ``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim, dtype=torch.bool)``. The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need to match, but they must be :ref:`broadcastable ` and the dimensionality of the :attr:`mask` tensor must not be greater than of the :attr:`input` tensor. Args: input (Tensor): the input tensor {args_declarations} Keyword args: {kwargs_declarations}zExample:: >>> input = {example_input} >>> input {indent_example_input} >>> mask = {example_mask} >>> mask {indent_example_mask} >>> {full_function_name}(input, {example_args}, mask=mask) {indent_example_output} zpThe identity value of {operation name} operation, which is used to start the reduction, is ``{identity_int32}``.aThe identity value of {operation name} operation, which is used to start the reduction, depends on input dtype. For instance, for float32, uint8, and int32 dtypes, the identity values are ``{identity_float32}``, ``{identity_uint8}``, and ``{identity_int32}``, respectively.zReturns {operation name} of all the slices in the :attr:`input` tensor along :attr:`dim` while the :attr:`input` elements are masked out according to the boolean tensor :attr:`mask`. {definition}aThe boolean tensor :attr:`mask` defines the "validity" of :attr:`input` tensor elements: if :attr:`mask` element is True then the corresponding element in :attr:`input` tensor will be included in {operation name} computation, otherwise the element is ignored. The values of masked-out elements of the output tensor have undefined value: it may or may not be set to zero or nan; the choice may correspond to the value that leads to the most efficient storage of :attr:`output` tensor. The mask of the {operation name} output tensor can be computed as ``torch.broadcast_to(mask, input.shape)``. The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need to match, but they must be :ref:`broadcastable ` and the dimensionality of the :attr:`mask` tensor must not be greater than of the :attr:`input` tensor. Args: input (Tensor): the input tensor {args_declarations} Keyword args: {kwargs_declarations}) reduction_signaturereduction_descrreduction_argsreduction_examplereduction_identityreduction_identity_dtypenormalization_signaturenormalization_descrnormalization_argsnormalization_examplesum)dimz keepdim=False dtype=None mask=Noneprodcumsum) dim__as_int)r5r6cumprodaminamaxargmin)r9r4argmaxmeanmediannorm))ordr3r4var))r3unbiasedr4std logsumexpsoftmax log_softmaxsoftmin normalize)) ord__requiredr:)z eps=1e-12r5r6z dim (int or tuple of ints, optional): the dimension or dimensions to reduce. Default: None that is equivalent to ``tuple(range(input.ndim))``.zF dim (int): the dimension along which {operation name} is computed.z ord (int, float, optional): the order of vector norm. Default: 2. See :func:`torch.linalg.vector_norm` for a list of supported norms.z ord (int, float): the order of vector norm. Default: 2. See :func:`torch.linalg.vector_norm` for a list of supported norms.zp unbiased (bool): when True, use Bessel's correction, otherwise, compute the uncorrected sample variance.zU eps (float, optional): small value to avoid division by zero. Default: {default}.zp keepdim (bool, optional): whether the output tensor has :attr:`dim` retained or not. Default: {default}.z dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. Default: {default}.z mask (:class:`torch.Tensor`, optional): the boolean tensor containing the binary mask of validity of input tensor elements. Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.) r3r:rCrLrEepskeepdimdtypemaskz Let ``x`` be a sequence of unmasked elements of one-dimensional slice of the :attr:`input` tensor. Softmax of i-th element in ``x`` is defined as ``exp(x[i])/sum(exp(x))``.z Let ``x`` be a sequence of unmasked elements of one-dimensional slice of the :attr:`input` tensor. LogSoftmax of i-th element in ``x`` is defined as ``log(exp(x[i])/sum(exp(x)))``.z Let ``x`` be a sequence of unmasked elements of one-dimensional slice of the :attr:`input` tensor. Softmin of i-th element in ``x`` is defined as ``exp(-x[i])/sum(exp(-x))``.z Let ``x`` be a sequence of unmasked elements of one-dimensional slice of the :attr:`input` tensor. Normalize of i-th element in ``x`` is defined as ``x[i]/max(norm(x, p), eps)``.z Let ``x`` be a sequence of unmasked elements of one-dimensional slice of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is defined as ``sum(x[:i])``.z Let ``x`` be a sequence of unmasked elements of one-dimensional slice of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is defined as ``prod(x[:i])``.)rHrIrJrKr8r;productmaximumminimumvariancestandard_deviation) r1r7r=r<r?r>r@rArBrDrFrGcumulative_sumcumulative_prod))rrX)TFT)FFF>rBrK@rO>rFrDFz __rz: TBD.=)default reduction) signaturedescridentityargsexample normalization)rcrdrfrgrP., c3FK|]}|jdddywr_rXrNsplit.0as r# z&_generate_docstring..Vs #PAAGGD!$4Q$7#P!c3FK|]}|jdddywrlrmros r#rrz&_generate_docstring..Ws %TaaggdA&6q&9%Trs ) function_namefull_function_namezoperation nameoperation_argsoperation_kwargs example_input example_args example_maskindent_example_inputindent_example_maskindent_example_output)identity_uint8identity_int32identity_float32-inf)identity_ord_ninf) definition)args_declarations)kwargs_declarationsc3nK|],\}}|t|tr|jn|f.ywN) isinstancestr format_map)rpkv template_datas r#rrz&_generate_docstring..s6 Aq :a+=ALL '1Es25z {_})dictupdatetorchtensorrtofloat32joingetrn splitlinesformat __module__rmap_reduction_identityuint8int32floatritems startswithr )rdocstring_templatesargs_and_kwargsargument_declarations definitionsreduction_namesnormalization_namesoperation_names example_dimrzr|r{rxryrqarg_declarationskwarg_declarationsop_kind doc_sectionsexample_outputrr templatessec doc_templaters @r#_generate_docstringr9sM "F t"C!M 2 }jX! G! H ! A ! B ! H! H! R! R! H! R!  !( R)!* R+!, M-!. B/!0 F1!2 B3!4  5!OHIJKK(Y8& Z3B-2/1"#+K8#  O $ "$ O?+./KLL, !:;M<JJ  (--/ Aq <<  1<< &&I !'')  ||{{l#Ssb 3%r$:#ST ||  " "9 --w L$Ts+A Y=BY--Y Y%op_nameinputc|j}|j}|jddd}|dvrtjd||S|dvrtjd||S|dvrtj |r'tjtj ||Stj|s|tjk(rtjtj|j||S|d vrtj |r'tjtj ||Stj|r*tjtj d z||Stj|s|tjk(rtjtj|j||S|d vrtj |r&tjtj ||Stj|s|tjk(rtjtj|j||S|d k(ry |dk(r{|r|dnd}|tdk(rLtj |sJ|jtjtj ||Stjd||S|dk(rWtj |r |jntj}tjtj||S|dvry td|d|d)aFReturn identity value as scalar tensor of a reduction operation on given input, or None, if the identity value cannot be uniquely defined for the given input. The identity value of the operation is defined as the initial value to reduction operation that has a property ``op(op_identity, value) == value`` for any value in the domain of the operation. Or put it another way, including or excluding the identity value in a list of operands will not change the reduction result. See https://github.com/pytorch/rfcs/pull/27 for more information. rirXr[>r1r8rrOdevice>r7r;>r=r? logaddexp>rGy>r<r>r@NrBr\rrA>rFrDz identity of z on z input)rOrrsplitrris_floating_pointinf is_signedriinfomin is_complexmaxrnanNotImplementedError)rrrfrOrrCs r#rrs;;E \\FnnS!$R(G##||AU6:: ' '||AU6:: 3 3  " "5 )<< %G G __U #u ';<< E 2 6 6eFS S M !  " "5 )<< %G G   e $<< RuVL L __U #u ';<< E 2 6 6eFS S & &  " "5 )<< vF F __U #u ';<< E 2 6 6eFS S F   F d1g1 %- **51 >5;; >1<< vF F||AU6:: H  %66u= 5;;||EIIU6BB N "  WIT%G HHr%r3ndimc ng}|dk(rd}|tt|St|d}t|tt j fr|fn|}|D]M}||vrtd|d||k\s|| krtd| d|dz d|d |j||zOtt|S) z4Return dim argument as a tuple of sorted dim values.NrXzdim=z+ appears multiple times in the list of dimsz4Dimension out of range (expected to be in range of [rjz ], but got )) tuplerangerrintrSymInt RuntimeError IndexErrorr!sorted)r3rdimsdim_ds r#_canonical_dimrsD by  {U4[!! tQ.intersection-sB 2r(+222F vekk&))A,///r%ctj||gjd\}}|tj|j d|Sr)rrrreq)rrrrrs r#minusz _sparse_coo_where..minus1sJ 2r(+222F vE%++fiil";._apply5sQ1v r%Tr2)layoutr sparse_coor dense_dimcoalescerr sparse_dimrvaluesrrnonzeroslicerrsparse_coo_tensor _coalesced_rr)rPrrinput_flat_indicesmask_flat_indices mask_valuesmaskin_flat_indicesrrmaskin_input_flat_indicesrrwhere_input_indiceswhere_input_valuesw1where_mask_valuesmaskin_zero_flat_indiceswhere_zero_indicesnresult where_indices where_valuesrs @r#_sparse_coo_wherers2 <<5++ ++ + ;;%,, && & :: $$ $ >> u0 00 0 NN E5 %9u'7'7'9:4  #6T__%67  ~~! KKM5q%*:*:*.s% '.2dA " sc32K|]\}}|vs |ywrrrs r#rrz7_sparse_coo_scatter_reduction_helper..s  ArQd]B s rXrr1r>r=r<r2r^T)return_inverser3r[F)reduce include_selfr)&r ValueErrorr_values_indicesr3rrOrrrrr!lenboolNotImplementedclonerrr unsqueeze to_sparseset index_selectrrrnumelrlistrexpandbfloat16float16r new_emptyscatter_reduce_r)oprrrNrOr valid_reductions output_dtyperr input_dimsnum_sparse_dimsreduced_sparse_dimsretained_sparse_dimsreduced_dense_dims output_shaper new_valuesr new_indicesrinverse_indices out_shapescatter_indicesouts ` r#$_sparse_coo_scatter_reduction_helperrCks[[F6 %% *:!; ? " U?JJ,>W VJ" !\\^  ?2 % %*//!*<*A-VZ@JJA.J ?+ 5'11!4  5}}<}0::<<mmo 34K+Q. /&'!+#?3($7 88($( *66u||$89< %% *:!; B   'r*G)33G> zzU[[ 99uzz !l  ;;%-- '%%djjlEKK@CC%**CUD [[E,, ,--dEKK@D;;%"2"22 22-- %++mo   {{ell" <<5== (==?D \\U-- -{{emm+~~e&6&6&89~~'<<5#3#33 33%%'D {{e&&&}} 777 $D Kr%ct|r|jdv}|jdv}|r{|jdk(r|r|dd}|r|dn|jd}t|g|i|}|jdd }t ||j } t || t|S|rt|g|i|Std |jd |jd td t|jd)zBReturn output mask of masked operation applied to given arguments.> rFr1rDr=r<r@rBr7r?r>rArG>r8r;rHrJrKrIrBrXNrr3rNFz5_output_mask expected masked operation (got callable rirz,_output_mask expected masked operation (got  object)) callablerrrbrrrr&r"rtype) r4rrfra is_reductionis_normalizationr3outmaskrNrs r# _output_maskrjs|{{ '  ;;+   {{f$8D!$q'vzz%'8C!%9$9&9GjjE2G!#uzz2DtG}5 5 u6t6v6 6G VWXZXcXcWddef :48;L;L:MX V  r%cfdGfddtjj}t|r.|j |j |j S||S)Nc||St||}tr$tj|g}t |||St dt jd)NrPz7_combine_input_and_mask expected masked operation (got rd)rbrerrr\r"rf)rrPcanonical_maskrrfr4s r#helperz'_combine_input_and_mask..helpersi <L$U6 B<,R[[%G$GJ.%< <I$r(J[J[I\\de r%c2eZdZefdZedZy)(_combine_input_and_mask..Combinec^|j|||j|||S)zJReturn input with masked-out elements eliminated for the given operations.)save_for_backwardmark_non_differentiable)ctxrrPros r#forwardz0_combine_input_and_mask..Combine.forwards4  ! !$ '++D1%& &r%cx|j\}t|r|jn|}t||}|dfSr) saved_tensorsrget_datar)ru grad_outputrP grad_datars r#backwardz1_combine_input_and_mask..Combine.backwardsA''GT*:;*G $$&[ &i6F4< r%N)rr __qualname__ staticmethodrvr|)rosr#Combinerqs)  '  '    r%r)rautogradFunctionrapplyryget_mask)r4rrPrfrros` ` @r#_combine_input_and_maskrs\  %..)) , E "  enn&(89E4 r%FrNrOrPc|5|jtjk(r|jtjtj tj tjtjhvr=|jjtjj}n|j}nu|j}|jtjtj tj tjtjhvrtj}t||j}tt ||}|jtj"k(r"tj ||t ||S|jtj$k(r&t'tj ||t ||S|jtjk(r+tj(|t+|t ||St-d|jd)Nr^r3rNrOzAmasked sum expects strided, sparse_coo or sparse_csr tensor (got  tensor))rrr[rOrr&int8int16rrUrint64rVrrrr1rZrrC_sparse_csr_sumr.r"rr3rNrOrPrrs r#r1r1s } <<5++ +{{     ++-00u{{0CQQS KKE{{       #uzz *D(eT:JEMM)yyT4=FF   e.. .3 IIz4g     e.. .$$ DJW U  OPZPaPaObbj k  r%c|5|jtjk(r|jtjtj tj tjtjhvr=|jjtjj}n|j}nu|j}|jtjtj tj tjtjhvrtj}t||j}tt ||}|jtj"k(rB|}|j|}t%|D]}|j!|t |} |S|jtj&k(r3| t)dt+tj ||t ||S|jtjk(r8| t)dtj,|t/|t ||St)d|jd)Nr^rz=masked prod expects explicit mask for sparse_coo tensor inputz=masked prod expects explicit mask for sparse_csr tensor inputrzBmasked prod expects strided, sparse_coo or sparse_csr tensor (got r)rrr[rOrr&rrrrUrrrVrrrr7rZrrr"rC_sparse_csr_prodr.) rr3rNrOrPrrrrs r#r7r7Gs } <<5++ +{{     ++-00u{{0CQQS KKE{{       #uzz *D(ud;JEMM)'$ ?A[[QW [>F ?   e.. . <O 4 JJ D$w-     e.. . <O %% DJW U  PQ[QbQbPcck l  r%)rOrPc.| |j}t||jd}tt||}|j t jk(r(t j|||j|Std|j d)Nrr^z*masked cumsum expects strided tensor (got r) rOrrrr1rrrZr8rr"rr3rOrPrrs r#r8r8s }  #uzz *1 -D(eT:JEMM)||JE:==E=JJ89J9J8K8 T  r%c.| |j}t||jd}tt||}|j t jk(r(t j|||j|Std|j d)Nrr^z+masked cumprod expects strided tensor (got r) rOrrrr7rrrZr;rr"rs r#r;r;s }  #uzz *1 -D(ud;JEMM)}}ZU;>>U>KK9*:K:K9LH U  r%cx| |j}tt||}t||j}|j t jk(r0t j||t|j|S|j t jk(r3| tdtt j||t||S|j t jk(r3| tdtt j||t||Std|j d)k{reduction_signature} {reduction_descr} {reduction_identity_dtype} {reduction_args} {reduction_example}r^=masked amax expects explicit mask for sparse_coo tensor inputz=masked amax expects explicit mask for sparse_csr tensor inputzBmasked amax expects strided, sparse_coo or sparse_csr tensor (got r)rOrr=rrrrrZr&rrr"rCr[rSrr3rNrOrPrrs r#r=r=%& } (ud;J #z /DEMM)zz*dDM:==E=JJ   e.. . <O 4 JJ D$w-     e.. . <O 4 JJ D$w-  PQ[QbQbPcck l  r%cx| |j}tt||}t||j}|j t jk(r0t j||t|j|S|j t jk(r3| tdtt j||t||S|j t jk(r3| tdtt j||t||Std|j d)rr^rz=masked amin expects explicit mask for sparse_csr tensor inputzBmasked amin expects strided, sparse_coo or sparse_csr tensor (got r)rOrr<rrrrrZr&rrr"rCr[rSrs r#r<r<rr%c | |j}tt||}|jtj k(r0t j||t |j|Std|jd)g{reduction_signature} {reduction_descr} {reduction_identity_dtype} {reduction_args} {reduction_example}r^z*masked argmax expects strided tensor (got r) rOrr?rrrZr&rr"rr3rNrOrPrs r#r?r? w } (=JEMM)||JT'];>>U>KK89J9J8K8 T  r%c | |j}tt||}|jtj k(r0t j||t |j|Std|jd)rr^z*masked argmin expects strided tensor (got r) rOrr>rrrZr&rr"rs r#r>r>:rr%cd}||j}d}|js|jstd|d||jt j k(r|Ztt j|jt j|j||}t||||}||z St||}|j|t| }t||||| }||z S|jt jk(rZtt ||} t#|| j$} | td t't j | | t||Std |jd )a{reduction_signature} {reduction_descr} By definition, the identity value of a mean operation is the mean value of the tensor. If all elements of the input tensor along given dimension(s) :attr:`dim` are masked-out, the identity value of the mean is undefined. Due to this ambiguity, the elements of output tensor with strided layout, that correspond to fully masked-out elements, have ``nan`` values. {reduction_args} {reduction_example}rInputz&mean(): Could not infer output dtype. z> dtype must be either a floating point or complex dtype. Got: rrNrFrmrrz=masked mean expects explicit mask for sparse_csr tensor inputz6masked mean expects strided or sparse_csr tensor (got r)rOrrr"rrrZr1onesrrrrbr&r[rr@rrrS) rr3rNrOrP dtype_sourcecounttotalinmaskrrs r#r@r@Tsy0L }   # #u'7'74\NC77EsG5vNEu} )) ),T5$? c:??3 <O 4 JJ D$w-  DU\\NRZ [  r%c| |j}t||jd}tj|}|s |j tj }tt||}|jtjk(rgtj|||j}|r|S|s5tj|js|j |Stdtd|jd)a{reduction_signature} {reduction_descr} By definition, the identity value of a median operation is the median value of the tensor. If all elements of the input tensor along given dimension(s) :attr:`dim` are masked-out, the identity value of the median is undefined. Due to this ambiguity, the elements of output tensor with strided layout, that correspond to fully masked-out elements, have ``nan`` values. {reduction_args} {reduction_example}rr^zMmasked median expects no fully masked out rows if dtype is not floating pointz*masked median expects strided tensor (got r)rOrrrrrrrrArrZ nanmedianrisnanrr") rr3rNrOrPris_floatroutputs r#rArAs( }  #uzz *1 -D&&u-H u{{+(=JEMM)T7;BB M%++f"5"9"9";9959) )_ 89J9J8K8 T  r%c(| |j}t||j}tt||}|j t jk(r(t j|||j|Std|j d)Nrr^z-masked logsumexp expects strided tensor (got r) rOrrrrGrrrZrr"rs r#rGrGs }  #uzz *D(E4@JEMM)z4ADD5DQQ;J Tensor Returns logaddexp of all the elements in the :attr:`input` and the :attr:`other` tensor. The :attr:`input` elements are masked out according to the boolean tensor :attr:`input_mask` and the attr:`other` elements are masked out according to the boolean tensor :attr:`other_mask`. The shapes of a mask tensor and the tensor to be masked don't need to match, but they must be :ref:`broadcastable ` and the dimensionality of the mask tensor must not be greater than of the tensor to be masked. Args: input (Tensor): the input tensor other (Tensor): the second input tensor Keyword args: dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the output tensor is casted to :attr:`dtype` after the operation is performed. Default: None. input_mask (:class:`torch.Tensor`, optional): the boolean tensor containing the binary mask of validity of :attr:`input` tensor elements. Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``. other_mask (:class:`torch.Tensor`, optional): the boolean tensor containing the binary mask of validity of :attr:`other` tensor elements. Default: None that is equivalent to ``torch.ones(other.shape, dtype=torch.bool)``. Example:: >>> input = torch.tensor([-100.0, -200, -300]) >>> input tensor([-100., -200., -300.]) >>> other = torch.tensor([-1.0, -2, -3]) >>> other tensor([-1., -2., -3.]) >>> mask = torch.tensor([True, False, True]) >>> mask tensor([ True, False, True]) >>> torch.masked._ops.logaddexp(input, other, input_mask=mask, other_mask=mask) tensor([-1., -inf, -3.])r^z.masked logaddexp expects strided tensors (got z tensor for input, z for other))rOrrrZrrrr")rrrOrrr mask_others r#rrsb }  ||u}}$)F,YzJ ,YzJ z:6999FFO>O=PPX Y  r%c6| |j}t||jd}tt||}|j t jk(r,t jjj|||Std|j d)Nrr^z+masked softmin expects strided tensor (got r) rOrrrr<rrrZrrrJr"rs r#rJrJrr%g-q=)rMrOrPrMcF| |j}tt||}|jtj k(rMt |||d||}t j||jg|}t j||Std|jd)NTrz-masked normalize expects strided tensor (got r) rOrr1rrrZrBrRnew_fullrr") rrCr3rMrOrPrnrm_denoms r#rKrKs } (eT:JEMM)E3TTJ dDMM"c$:;||J..;Jr@rArGrrrBrrDrFrHrIrJrKrr%r#rs II2 #8I?,+#H-Iy- EsCx)Ic T]t_Xb"f%5(2r6:J*X.v :I:IV:Iz sCx0  U T mFm6mvm&mj" uu S/u u E? u  uz" SS S/S S E? 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