Αi9VZSSKJr SSKrSSKJr SSKrSSKqSSKJr SSK J r SSK J r SSK Jr \(aSS KJr SS KJr SS KJr SS KJrJrJr \ R.R0R2\ R.R0R4\ R.R0R6\ R.R0R8\ R.R0R:\ R.R0R</r/S Qr \ R.R0RB\ R.R0RD/r#Sq$0SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_S SS S!.Er%S&S"jr&"S#S$\'5r(S%r)g)') annotationsN) TYPE_CHECKING)_C_ops)size_args_decorator_patch)core)convert_np_dtype_to_dtype_)Any)NDArray)Tensor) DTypeLike PlaceLike ShapeLike) __add____radd____sub____rsub____mul____rmul____div__ __truediv____rdiv__ __rtruediv__ __matmul__Ffloat16halfbfloat16float32floatfloat64doubleint8charint16shortint32intint64longbool complex64 complex128)r,cfloatcdoublec4[R"UUUU5$N)paddletensor)datadtypedevice requires_grads a/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/base/dygraph/math_op_patch.py_rebuild_tensorr8ks! ==    c>^\rSrSr%S\S'U4SjrSSjrSrU=r$) TensorSizeyz list[int]as_shapecn>[TU]U[[R"U555nXlU$r0)super__new__r'npprodr=)clsshapeinstance __class__s r7r@TensorSize.__new__|s+7?3BGGEN(;<!r9cT[R"UR5nUcU$X!$r0)r1Sizer=)selfdimrDs r7__call__TensorSize.__call__s& DMM* ;Lzr9r0) __name__ __module__ __qualname____firstlineno____annotations__r@rL__static_attributes__ __classcell__)rFs@r7r;r;ys r9r;c^$^%S?Sjm%S@U%4SjjnU%4SjnSASjnSBSjm$SCU$4SjjnSCSjnSDSjnSES jnSFS jnSFS jnSFS jn SFS jn [SFSj5n SFSjn SFSjn [SFSj5n[SCSj5n[SCSj5nSSSSS.SGSjjn[SSSSS.SHSjj5n[SSSSS.SHSjj5n[SSSSS.SHSjj5n[SISj5nURSJSj5nSKSLS jjn[SMS!j5nSNS"jnS#U4S$U4S%U4S&U4S'U4S(U4S)U 4S*U 4S+T%4S,U4S-U4S.U4S/U 4S0U 4S1U 4SU4S2U4S3U4S4U4S5U4S6U4S7U4SU4S8U4S9S:U4S;U4/nU"5nURU5 /Sn[UUU5 M gS=SK q [RRHGn[UU5(aM[[RUS5nU(dM:[UUU5 MI [RRH7un!n"[[RU"S5n#U#(dM*[UU!U#5 M9 g)Oz Similar to monkey_patch_variable. The difference is, in dygraph mode, use auto-generated op functions for better performance. c[U[RR[R45(d [ U5nUR U:XaU$[R"X5$)a Cast a Tensor to a specified data type if it differs from the current dtype; otherwise, return the original Tensor. Args: dtype: The target data type. Returns: Tensor: a new Tensor with target dtype Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> original_tensor = paddle.ones([2, 2]) >>> print("original tensor's dtype is: {}".format(original_tensor.dtype)) original tensor's dtype is: paddle.float32 >>> new_tensor = original_tensor.astype('float32') >>> print("new tensor's dtype is: {}".format(new_tensor.dtype)) new tensor's dtype is: paddle.float32 ) isinstancerVarDescVarTypeDataTyper r4rcast)rJr4s r7astype(monkey_patch_math_tensor..astypesN2%$,,"6"6 !FGG.u5E :: K{{4''r9c>UR5(aT"US5nT"US5$UR5(a6T"UR5S5n[R"S5 T"US5$T"US5$)Nr"uint8z:Casting complex values to real discards the imaginary part)is_floating_point is_complexrealloggingwarning)rJr2rcr]s r7byte&monkey_patch_math_tensor..bytesu  ! ! # #D&)F&'* * __  $))+v.D OOL $( ($( (r9c>/n[R5H9upU4SjnU"U5nXlSUSUS3UlUR X45 M; U$)z/ Batch create all data type conversion methods c>^SUU4SjjnU$)Nc>T"UT5$r0rN)rJr]r4s r7conversion_method}monkey_patch_math_tensor.._create_dtype_conversion_methods..make_conversion_method..conversion_methods!$..r9rJr returnr rN)r4rkr]s` r7make_conversion_methodbmonkey_patch_math_tensor.._create_dtype_conversion_methods..make_conversion_methods//)(r9z Cast a Tensor to z data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with z dtype )_supported_dtype_conversionsitemsrO__doc__append)methods method_name target_dtypero method_implr]s r7 _create_dtype_conversion_methodsBmonkey_patch_math_tensor.._create_dtype_conversion_methodssu)E)K)K)M %K ) 1>K#. '*^,+,8.9 #K  NNK5 6#*N&r9c8URUR5$r0)r]r4)rJothers r7type_as)monkey_patch_math_tensor..type_ass{{5;;''r9cF[R"U[U5US5$)NT)rscaler)varrbiass r7_scalar_elementwise_op_9monkey_patch_math_tensor.._scalar_elementwise_op_s||CutT::r9c>T"USS5$)NggrN)rrs r7_neg_'monkey_patch_math_tensor.._neg_s&sD#66r9c"UR5$r0)absrs r7_abs_'monkey_patch_math_tensor.._abs_swwyr9c[R"UR5nUS:XdS5eUR5(dS5eUR 5(dUR S5n[ [R"U55$)Nz6only one element variable can be converted to complex.$variable's tensor is not initializedr+)rArBrD_is_initializedrbr]complexarrayrnumels r7 _complex_+monkey_patch_math_tensor.._complex_su "z D z""$$L&LL$~~**[)Crxx}%%r9c[R"UR5nUS:XdS5eUR5(dS5eUR[ R RR:Xd(UR[ RR:XaURS5n[[R"U55$)Nrz4only one element variable can be converted to float.rr)rArBrDrr4rrYrZBF16r[BFLOAT16r]rrrs r7_float_)monkey_patch_math_tensor.._float_s "z B z""$$L&LL$ II--22 2yyDMM222**Y'CRXXc]##r9c[R"UR5nUS:XdS5eUR5(dS5eUR[ R RR:Xd(UR[ RR:XaURS5n[[R"U55$)Nrz3only one element variable can be converted to long.rrrArBrDrr4rrYrZrr[rr]r'rrs r7_long_(monkey_patch_math_tensor.._long_s "zPPPz""$$L&LL$ II--22 2yyDMM222**Y'C288C=!!r9c[R"UR5nUS:XdS5eUR5(dS5eUR[ R RR:Xd(UR[ RR:XaURS5n[[R"U55$)Nrz2only one element variable can be converted to int.rrrrs r7_int_'monkey_patch_math_tensor.._int_ s "zOOOz""$$L&LL$ II--22 2yyDMM222**Y'C288C=!!r9cURS:dS5eUR[RRR :Xa'[ UR5R55$UR[RRR:Xa'[ UR5R55$URS$)Nrzlen() of a 0-D tensor is wrong) ndimtyperrYrZVOCABlenvalueget_map_tensorSTRINGSget_string_tensorrDrs r7_len_'monkey_patch_math_tensor.._len_sxx!|===| 88t||++11 1syy{1134 4 XX--55 5syy{4467 799Q< r9c[R"UR5nUS:XdS5eUR5(dS5eUR[ R RR:Xd(UR[ RR:XaURS5n[[R"U55$)Nrz;only one element variable can be converted to python index.rrrrs r7_index_)monkey_patch_math_tensor.._index_s "z I z""$$L&LL$ II--22 2yyDMM222**Y'C288C=!!r9c,[UR5$r0rrDrs r7_ndim'monkey_patch_math_tensor.._ndim*s399~r9c,[UR5$r0rrs r7 ndimension,monkey_patch_math_tensor..ndimension.399~r9c,[UR5$r0rrs r7rK%monkey_patch_math_tensor..dim1rr9c,[UR5$r0)r;rDrs r7_size_(monkey_patch_math_tensor.._size_4s#))$$r9c [UR5S:XaU$[[[ [UR5555n[ R "X5nU$)Nr)rrDlistreversedranger transposerpermouts r7_T_%monkey_patch_math_tensor.._T_8sH syy>Q JHU3syy>234s) r9c[UR5S:a[SURS35e[ [ [UR555nUSUSsUS'US'[ R"X5nU$)a Return the last two dimensions of a Tensor transposed. Args: var (Tensor): The input Tensor, which must have at least 2 dimensions. Returns: Tensor: A new Tensor with its last two dimensions swapped. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn([2, 3, 4]) >>> x_transposed = x.mT >>> x_transposed.shape paddle.Size([2, 4, 3]) rz Tensor.ndim(z/) is required to be greater than or equal to 2.)rrD ValueErrorrrrrrrs r7_mT_&monkey_patch_math_tensor.._mT_@s|( syy>A sxxj(WX E#cii.)*!"XtBxR$r(s) r9NFr4r5r6 pin_memorysizer6c pUc URnUc URn[R"UUUUUUS9$)a Create a new Tensor of specified shape and fill it with a given value. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. fill_value (bool | float | Tensor): Value to fill the Tensor with. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with `fill_value`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_full([3, 3], 5.0) >>> y.numpy() array([[5., 5., 5.], [5., 5., 5.], [5., 5., 5.]], dtype=float32) rr4placer1full)rr fill_valuer4r5r6rs r7 _new_full_,monkey_patch_math_tensor.._new_full_]sDJ =IIE >YYF{{  '!   r9cnUc URnUc URn[R"UUUUUS9$)a  Create a new uninitialized Tensor of the specified shape. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new uninitialized Tensor with the specified shape. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_empty(3, 3) # type: ignore >>> y.shape paddle.Size([3, 3]) r5r6r)r4rr1emptyrrr4r5r6rs r7 _new_empty_-monkey_patch_math_tensor.._new_empty_sAD =IIE >YYF||  '!   r9c pUc URnUc URn[R"USUUUUS9$)a[ Create a new Tensor of the specified shape filled with ones. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with ones. Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros([2, 2]) >>> y = x.new_ones(3, 3) # type: ignore >>> y.numpy() array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], dtype=float32) rrrrs r7 _new_ones_,monkey_patch_math_tensor.._new_ones_DH =IIE >YYF{{  '!   r9c pUc URnUc URn[R"USUUUUS9$)aA Create a new Tensor of the specified shape filled with zeros. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with zeros. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_zeros(3, 3) # type: ignore >>> y.numpy() array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], dtype=float32) rrrrs r7 _new_zeros_-monkey_patch_math_tensor.._new_zeros_rr9c$UR(+$)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 ) stop_gradientrJs r7r6/monkey_patch_math_tensor..requires_grad#s$%%%%r9ct[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. $requires_grad must be bool, but got NrXr* TypeErrorrr)rJrs r7r6r7s8%&&6tE{mD "'Yr9Tcv[U[5(d[S[U535eU(+UlU$)z Set whether this Tensor requires gradient computation. Args: requires_grad (bool): True to enable gradient computation, False to disable. rr)rJr6s r7requires_grad_0monkey_patch_math_tensor..requires_grad_Es@-..6tM7J6KL "/. r9c"UR5$)z Returns the number of bytes allocated on the machine for a single element of the Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn((2,3),dtype=paddle.float64) >>> x.itemsize 8 ) element_sizers r7itemsize*monkey_patch_math_tensor..itemsizeSs  ""r9cUR5n[UR5SSn[UR5SSnURn[ UUUU44$)Nr)numpystrrr4r6r8)rJproto data_numpyrr4r6s r7 _reduce_ex_-monkey_patch_math_tensor.._reduce_ex_bs^ZZ\ DJJ"%DJJ#**      !   r9__neg____abs__ __complex__ __float____long____int____len__ __index__r]rfr`r}rKrrTmTnew_full new_emptynew_ones new_zerosr)__array_ufunc__Nr __reduce_ex__)rrrrrrrrrr__mod____rmod__r __rmatmul____gt____ge____lt____le__ __floordiv__ __rfloordiv____pow____rpow____eq____ne__rr)rJr r4r rnr rm)rJr r|r rnr )rr rrrrrnr )rr rnr )rr rnr)rr rnr)rr rnr')rr rrrzbool | float | paddle.Tensorr4DTypeLike | Noner5PlaceLike | Noner6r*rr*rnr )rr rrr4rr5rr6r*rr*rnr )rJr rnr*)rJr rr*rnNone)T)r6r*rnr )rJr rnr')rJr )propertyrsetterextend_already_patch_eager_tensorreagerr getattrsetattr paddle.tensorr1r2tensor_method_funchasattrmagic_method_func)&rfryr}rrrrrrrrrrrKrrrrrrrr6rrr eager_methodsdtype_conversion_methodseager_cpp_level_patchlocal_already_patch local_tensorrvrxmethod magic_method origin_methodimplrr]s& @@r7monkey_patch_math_tensorr1s(B )6(; ;!;).; ; 7& $ " "  "%%B#'#'# 1 1 1 11  1 ! 1 1 1  1 f #'#'# , , ,  , ! ,  , ,  , , \ #'#'# / / /  / ! /  / /  / / b #'#'# / / /  / ! /  / /  / / b&&& ' '  # #   E E  " g V E E g 6  $ G  z"   c t Z k" Z k" -( >*! X +&9M> @A12:6"&::$$L 0K!, TBK{ k;?1 $F )K )K L+{ ;$ "==;;K|[11!&--dCK{ k;? <,2==+J+J 'L-6==->Dt lD9,Kr9)r3z NDArray[Any]r4r r5rrnr )* __future__rrdtypingrrrAr1rpaddle.utils.decorator_utilsrr frameworkr r numpy.typingr r paddle._typingr rrrYrZUINT8INT8INT16INT32INT64BOOL_supported_int_dtype_!_supported_promote_complex_types_ COMPLEX64 COMPLEX128_complex_dtypesr rqr8r'r;r1rNr9r7rDs#  2$>> LLLLLLLLLLLL  %! LL""LL## $  y  I    y    Y  y  i  F  F  W   W! " W# $ 7% & W' ( G) , F- ./ 05 <           A :r9