Αi  SSKJr SSKrSSKrSSKJr SSKJr SSKJ r SSK J r J r J r Jr \(aSSKJr \R"R$R&\R"R$R(\R"R$R*\R"R$R,\R"R$R.\R"R$R0/r\R"R$R4\R"R$R6/r/S Qr/S Qr0S S _S S_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS _S!S"_S#S$_S%S&_S'S(_S)S*_S+S,S-S.S/S0S1S2.ErS3q S4r!S5r"g)6) annotationsN) TYPE_CHECKING)core)in_to_static_mode) OpProtoHolderVariabledefault_main_program static_only)Tensor)__eq____ne____lt____le____gt____ge__)__add____radd____sub____rsub____mul____rmul____mod____rmod____div____rdiv__ __truediv__ __rtruediv__ __floordiv__ __rfloordiv____pow____rpow__r rrrrrrzA + BrzA += BrzA - BrzA -= BrzA * BrzA *= BrzA / BrrzA /= Brr!zA ** Br"zA **= BrzA //Br zA //=BrzA % BrzA %= B __matmul__zA @ BzA @= BzA == BzA != BzA < BzA <= BzA > BzA >= B) __rmatmul__r rrrrrFcb^^^^^SSKJn SSKJm U4SjnSmU"TT:HUUUUU4Sj5nU$)Nr)cond) create_arrayc>T"S5nU$)Nfloat32) null_arrayr's `/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/base/layers/math_op_patch.pytrue_fn$_slice_tensor_array..true_fnns!), cXUnU$Nr*)arraystartend new_arrays r,false_fn%_slice_tensor_array..false_fnrs$ r/c>T"TTT5$r1r*)r2r4r6r3sr,%_slice_tensor_array..vsHUE34Or/)paddle.static.nnr& paddle.tensorr')r2r3r4r&r-r5r'r6s``` @@r,_slice_tensor_arrayr=js.%*Uc\7,OPI r/c^$^%^&^'^(^)^*^+^,^-Sm-Sm,Sm*U-4Sjm&U-4SjnU&4Sjm(U(4Sjm'U&U*4Sjm)[U*U-4S j5n[SjU*U-4S jj5n[S 5n[S 5n[S5nU&U*4Sjm%Sn[S5n[S5n[S5n U&U*4Sjm$U$4Sjn U+4Sjn [S5n Sn Sn[SkSj5nURSlSj5nSmSnSjjnU$4SjnU$4SjnU$4S jnU$4S!jnU$4S"jnSoU%U&U'U(U)U*U,4S$jjnS%nS&nS'nU&U*4S(jnU&U*4S)jnU&U*4S*jn/S+U 4PS,U 4PS-T%4PS.U4PS/U4PS0U4PS1U4PS2U4PS3U4PS4U4PS5U4PS6U 4PS7U4PS8U 4PS9U 4PSU4PS:U4PS;U"S;SU"S>S?S#U54PS@U"S@S?S U54PSAU"SASBS#U54PSCU"SCSBS#U54PSDU"SDSES#U54PSFU"SFSES#U54PSGU"SGSES S54PSHU"SHSES S54PSIU"SISJS#S54PSKU"SKSJS S54PSLU"SLSMS#S54PSNU"SNSMS S54PSOU"SOSPS#S54PSQU"SQSPS S54PSRU"SRSSS#S54PSTU"SUSSS S54PSVU"SVSWS#S54PSXU"SXSYS#S54PSZU"SZS[S#S54PS\U"S\S]S#S54PS^U"S^S_S#S54PS`U"S`SaS#S54PSbU4PScU4PSdU4PSeU4PSfU4PSgU4Pn[(d'UHnUShnUSin [ [ UU 5 M S qgShSKm+T+RRHKn[[ U5(aM[T+RUS5n U (dM:[ [ UU 5 MM T+RRH7un!n"[T+RU"S5n#U#(dM&[ [ U!U#5 M9 S qg)pNcH[5RRS5$)Ntmp)r _name_generatorgenerater*r/r,unique_tmp_name.monkey_patch_variable..unique_tmp_name{s#%55>>uEEr/cZURnU$! [SUR35e=f)NzCannot get data type from )dtype ValueErrorname)varrFs r,safe_get_dtype-monkey_patch_variable..safe_get_dtype~s7 FIIE  F9#((DE Es *cJURRR5$r1)blockprogram current_block)rIs r,rO,monkey_patch_variable..current_blocksyy  ..00r/c0>T"5nURX!S9$)N)rHrF create_var)rMrFtmp_namerCs r,create_new_tmp_var1monkey_patch_variable..create_new_tmp_vars"$X;;r/c2>T"5nURX1US9$)N)rHrFtyperR)rMrFrXrTrCs r,create_new_tmp_sparse_var8monkey_patch_variable..create_new_tmp_sparse_vars!"$XFFr/c >[U5nT"X5nURSSU/0URUUSS.SS9 SUlU$)N fill_constantOutF)rFshapevalue force_cpuT)rXoutputsattrs stop_gradient)float append_oprFrc)rMr_rFr^rIrUs r, create_tensor,monkey_patch_variable..create_tensors\e  .  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Examples: In Static Graph Mode: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.static.data(name="x", shape=[2,2], dtype='float32') >>> y = x.cpu() FTrHrFr^rX persistablercdst_place_typermemcpyXr]rXrrrarb)rSrFr^rXre)selfrMrToutputrbrOrCs r,cpu"monkey_patch_variable..cpus(d#"$!!**** " "1% $=VH%   r/Tc *>Ub[R"S5 USLa[R"S5 T"U5nT"5nURUURURUR SSS9nSS0nUR SS U/0S U/0US 9 U$) a/ In dy2static, Variable also needs cpu() and cuda() interface. But, the underneath operator has only forward op but not backward one. Args: self(Variable): The variable itself. device_id(int, optional): The destination GPU device id. Default: None, means current device. We add this argument for dy2static translation, please do not use it. blocking(bool, optional): Whether blocking or not, Default: True. We add this argument for dy2static translation, please do not use it. Returns: The tensor which has copied to cuda place. Examples: In Static Graph Mode: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> x = paddle.static.data(name="x", shape=[2,2], dtype='float32') >>> y = x.cpu() >>> z = y.cuda() z3device_id is not supported, and it will be ignored.Tz2blocking is not supported, and it will be ignored.Fr~rrmrrr]r)warningswarnrSrFr^rXre) r device_idblockingrMrTrrbrOrCs r,cuda#monkey_patch_variable..cudas8  MMO P 4  MMN Od#"$!!**** " "1% $=VH%   r/c0[R"S5 g)z Variable don't have 'place' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zgVariable do not have 'place' interface for static graph mode, try not to use it. None will be returned.Nrrrs r,place$monkey_patch_variable..place"s  u r/c2[R"S5 U$)z Variable don't have 'contiguous' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zlVariable do not have 'contiguous' interface for static graph mode, try not to use it. self will be returned.rrs r, contiguous)monkey_patch_variable..contiguous-s  z  r/c0[R"S5 g)z Variable don't have 'is_contiguous' interface in static graph mode But this interface can greatly facilitate dy2static. So we give a warning here and return None. zoVariable do not have 'is_contiguous' interface for static graph mode, try not to use it. True will be returned.Trrs r, is_contiguous,monkey_patch_variable..is_contiguous9s  } r/c>URU:XaU$T"U5nT"X!5nURSSU/0SU/0URURS.S9 URUlU$)a **Notes**: **The variable must be a** :ref:`api_paddle_Tensor` Cast a variable to a specified data type if it differs from the current dtype; otherwise, return the original variable. Args: self(Variable): The source variable dtype: The target data type Returns: Variable: Variable with new dtype Examples: In Static Graph Mode: .. code-block:: python >>> import paddle >>> import paddle.base as base >>> paddle.enable_static() >>> startup_prog = paddle.static.Program() >>> main_prog = paddle.static.Program() >>> with base.program_guard(startup_prog, main_prog): ... original_variable = paddle.static.data(name = "new_variable", shape=[2,2], dtype='float32') ... new_variable = original_variable.astype('int64') ... print("new var's dtype is: {}".format(new_variable.dtype)) ... new var's dtype is: paddle.int64 In Dygraph Mode: .. code-block:: python >>> import paddle.base as base >>> import paddle >>> import numpy as np >>> x = np.ones([2, 2], np.float32) >>> with base.dygraph.guard(): ... original_variable = paddle.to_tensor(x) ... print("original var's dtype is: {}, numpy dtype is {}".format(original_variable.dtype, original_variable.numpy().dtype)) ... new_variable = original_variable.astype('int64') ... print("new var's dtype is: {}, numpy dtype is {}".format(new_variable.dtype, new_variable.numpy().dtype)) ... original var's dtype is: paddle.float32, numpy dtype is float32 new var's dtype is: paddle.int64, numpy dtype is int64 castrr])in_dtype out_dtyper)rFrerc)rrFrMoutrUrOs r,astype%monkey_patch_variable..astypeEsvh :: Kd# . $=SEN#zz B  !.. r/c8URUR5$r1)rrF)rothers r,type_as&monkey_patch_variable..type_ass{{5;;''r/cX[U[5(d6[5(aSSKJn U"U5nO[ S[ U535eUR [RRR:wa[ SUR 35eSSK J nJ n U"X"U5US9 g)z8 Note: The type variable must be LoD Tensor Array. r) to_tensorz4Required input var should be Variable, but received zZOnly Variable with VarType.DENSE_TENSOR_ARRAY support `append` method, but received type: ) array_length array_write)xryr2N)rsr rpaddle.tensor.creationr TypeErrorrXrVarDescVarTypeDENSE_TENSOR_ARRAYpaddle.tensor.arrayrr)rrIrrrs r,ru%monkey_patch_variable..appends#x(( ""\<nJ4PS9+V 99 ,,?? ?lmqmvmvlwx  Bc\$/t._items6 tzz?Q J4::,W  r/cN^^ ^ SSKm SSKJn SSKJn TR [ RRR:wa[STR 35e[U5S:XaSnOUSn[U[5(deU U 4SjnU U4SjnT RRT5m US:aUT -nO U"S /S US 9nT RR!TU5nT R#T5n[%USU5n US -n U"XVX/5upT R#U TS 9 U$) a_ The type variable must be LoD Tensor Array. When self is DenseTensorArray, calling pop is similar to Python's pop on list. This interface is used to simplify dygraph to static graph operations. Args: self(Variable): The source variable, which must be DENSE_TENSOR_ARRAY *args: optional, a int means index. Returns: Variable: self[index] rN) while_loop)r\zWOnly Variable with VarType.DENSE_TENSOR_ARRAY support `pop` method, but received type: rlc(>TRUT5$r1) less_than)ryr5arr_lenpaddles r,r&0monkey_patch_variable..pop..conds##Aw/ /r/c>TRRTUS9nTRRUTRRU5U5 TR U5nX4$)N)r2ry)tensor array_readrr increment)ryr5itemrrs r,body0monkey_patch_variable..pop..bodys^==++$!+.pops // 99 ,,?? ?ijnjsjsitu  t9>Cq'C#s#### 0 --,,T2 7-CqcDC==++D#6mmD!'Q4 !G!$qn=  i -r/cp>T"U5nT"X0R5nURSSU/0SU/0XS.S9 U$)Nscalerr])rbiasrrFre)rIrrrMrrUrOs r, _scalar_op_*monkey_patch_variable.._scalar_op_sNc"  2 #<SEN!0   r/c>T"USS5$)Nr*)rIrs r,_neg_$monkey_patch_variable.._neg_s3c**r/c&>TRU5$r1)abs)rIrs r,_abs_$monkey_patch_variable.._abs_szz#r/c,[UR5$)ah Returns the dimension of current Variable Returns: the dimension Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Variable >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # print the dimension of the Variable >>> print(x.ndim) 3 rr^rs r,_ndim$monkey_patch_variable.._ndims*4::r/c,[UR5$)ap Returns the dimension of current Variable Returns: the dimension Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Variable >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # print the dimension of the Variable >>> print(x.ndimension()) 3 rrs r, ndimension)monkey_patch_variable..ndimension(4::r/c,[UR5$)ai Returns the dimension of current Variable Returns: the dimension Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> # create a static Variable >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) >>> # print the dimension of the Variable >>> print(x.dim()) 3 rrs r,dim"monkey_patch_variable..dim'rr/c$UR(+$)a Whether this Tensor requires gradient computation. This is a convenience property that returns the opposite of stop_gradient. Setting requires_grad=True is equivalent to setting stop_gradient=False. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([2, 3]) >>> print(x.requires_grad) # False by default >>> >>> x.requires_grad = False >>> print(x.stop_gradient) # True )rcrs r, requires_grad,monkey_patch_variable..requires_grad=s$%%%%r/ct[U[5(d[S[U535eU(+Ulg)z Set whether this Tensor requires gradient computation. Args: value (bool): True to enable gradient computation, False to disable. $requires_grad must be bool, but got NrsboolrrXrc)rr_s r,rrQs8%&&6tE{mD "'Yr/rcv[U[5(d[S[U535eU(+UlU$)z Set whether this Tensor requires gradient computation. Args: requires_grad (bool): True to enable gradient computation, False to disable. rr)rrs r,requires_grad_-monkey_patch_variable..requires_grad__s@-..6tM7J6KL "/. r/c>T"USU5$N?r*rIr_rs r, _scalar_add_+monkey_patch_variable.._scalar_add_ms3U++r/c>T"USU*5$rr*rs r, _scalar_sub_+monkey_patch_variable.._scalar_sub_ps3eV,,r/c>T"USU5$)Nrr*rs r, _scalar_rsub_,monkey_patch_variable.._scalar_rsub_ss3e,,r/c>T"XS5$)Nrr*rs r, _scalar_mul_+monkey_patch_variable.._scalar_mul_vs3s++r/c>T"USU- S5$)Nrrr*rs r, _scalar_div_+monkey_patch_variable.._scalar_div_ys3e S11r/Fc >^^^^UUUU U U UUUU U4 Sjn[R"5RT5RnSUS3UlTUlU$)Nc> [U[5(a)UR[;a T "US5nTbT"X5$O[U[5(au[U5nTS:XaUR[;a T "US5nUR[ R RR:Xa T "US5nTbT"X5$OA[U[5(a+UR[;aT "US5nT "T"U5SS9nOT"U5n[U[5(dRT(a>URHnUS:dM T"XU5n O) T"T"U5UUURS9nO T"T"U5XS9nT"U5nX$:wGaNT[;Ga.[UR5S:Xd[UR5S:Xa[ R"X$5(a2[UR5S:XaK[UR5S:Xa2[ R "TX$5nX%:waT "X5nXE:waT "X5nO[UR5S:Xa T "X5nOpT "X5nOg[ R""TX$5(a5[$R&"S TS US US 35 [$R("S SS9 O[+STSUS US35eT(aUnUnUnTS:XdTS:Xa@UR[;a,URUR:XaT "US5nT "US5nT[,;aT "T"U5SS9nOT "T"U5T"U5S9nSnUR.S:anURSS:Xa[[0R2"5Sn U Sn U Sn [$R&"U SU S[4TSTSTS[4TS3 [6S9 T"U5R9TU/U/S.S U0S!U0S"9 U$)#Nr)elementwise_divr complex64)rFr)rFr^)r_rFzThe input dtypes of OP z are z and z", the output will be auto-promotedignorezThe input dtypes of OP)messagezgot different data type in z between .dividerrlrmr:z The behavior of expression z has been unified with zw(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use z(X, Y, axis=0) instead of z:. 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If you are using @to_static, you can try this: 1. If you want to get the value of Variable, you can switch to non-fullgraph mode by setting @to_static(full_graph=True). 2. If you want to run it in full graph mode, you need use Variable.astype(paddle.int32), and do not use int(Variable).rrs r,_int_$monkey_patch_variable.._int_ s E  r/c[S5e)NaLfloat(Variable) is not supported in static graph mode. If you are using @to_static, you can try this: 1. If you want to get the value of Variable, you can switch to non-fullgraph mode by setting @to_static(full_graph=True). 2. If you want to run it in full graph mode, you need use Variable directly, and do not use float(Variable).r2rs r,_float_&monkey_patch_variable.._float_'s {  r/c[S5e)NaPcomplex(Variable) is not supported in static graph mode. If you are using @to_static, you can try this: 1. If you want to get the value of Variable, you can switch to non-fullgraph mode by setting @to_static(full_graph=True). 2. If you want to run it in full graph mode, you need use Variable directly, and do not use complex(Variable).r2rs r, _complex_(monkey_patch_variable.._complex_.s }  r/cl>T"U5nT"XR5nURSSU/0SU/00S9 U$)N sparse_valuesrrrrrIrMrrUrOs r,values%monkey_patch_variable..values5sKc"  2  #<SEN   r/cl>T"U5nT"XR5nURSSU/0SU/00S9 U$)Nsparse_indicesrrrrr=s r,indices&monkey_patch_variable..indices@sKc"  2 !#<SEN   r/cl>T"U5nT"XR5nURSSU/0SU/00S9 U$)Nsparse_to_denserrrrr=s r,to_dense'monkey_patch_variable..to_denseKsKc"  2 "#<SEN   r/__neg____abs__rrrrrrrrurrrrrrrelementwise_addrrelementwise_subrrelementwise_mulrrrrrrr!elementwise_powr"relementwise_floordivr relementwise_modrr# matmul_v2r$ __rmatmulr equalr not_equalrrr less_equalr greater_thanr greater_equal __float____int__ __complex__r>rBrFrrm)NT)returnr)r_rrZNone)T)rrrZr )FN) r propertysetter_already_patch_variablesetattrr r<rtensor_method_funchasattrgetattrmagic_method_func).rYrrrrrrrurrrrrrrrrrrrrrr/r3r6r9r>rBrFvariable_methodsmethodr$ method_impl magic_method origin_methodimplrrrUrirfr{rOrrJrCs. @@@@@@@@@@r,monkey_patch_variablerjzsF1<G"<B%%N22h      @D(==2  66p +,,,&&& ' ' ,--,2<@bbH      v Ev Ev 6 v G v  v v %v z"v -(v 6v v  v  v z"v !v" -(#v$ >*%v(  Y(95, O 'v2  -ul  1v>  Y(95, O =vF  -t]  EvR  Y(95, O Qv\  -ul  [vh  Y(95, O gvp  0%  ov|  Z):D$ G {vD  ^->d K CvL  Y(95$ G KvT  Z):D$ G Sv\   6t  [vh  !7t  gvt  Y(95$ G sv|  Z):D$ G {vD  \;t D CvL  [+tT B KvT #HgudCDUvV #Hk5$GHWvX #Hk5$GHYvZ #HlE4HI[v\ #HneTJK]v^ #HoudKL_v` gavb Ecvd  "evf 6gvh Givj Xkvr # "&F )K )K Hk; 7'&# !==;;Kx--!&--dCK{+{; <,2==+J+J 'L-6==->Dt,5,K #r/)# __future__rrrtypingrr dygraph.baser frameworkrr r r rr rrrUINT8INT8INT16INT32INT64r COMPLEX64 COMPLEX128rrrrr^r=rjr*r/r,rws# , LLLLLLLLLLLL  LL""LL## K 2 ww  w   w7Hx GXw !"'#$16   i #r/