ёi-.SSKJr SSKJrJrJr SSKrSSKJ r SSK r SSK J r SSK J r Jr SSKJrJr SSKJrJrJrJr SS KJr S S KJr S S KJrJrJrJrJ r S SK!J"r" \(aSSK J#r# SSKJ$r$ /r%\"S/S/S.5S7S8Sjj5r&\"5S9SS.S:Sjjj5r'\ S;SS.SSjjj5r(\"SS/5S?SS.S@Sjjj5r(SASjr)SBSjr*\$"SS1SS S!S"9S7S8S#jj5r+SS.SCS$jjr,SDSES%jjr-\"S/S&/S'.5SFSS.SGS(jjj5r.\SFSHS)jj5r/SIS*jr0\"SS/5SJSS.SKS+jjj5r1"S,S-\5r2\"SS/SS/5SLSS.SMS.jjj5r3\"SS/S/S0/5SNSS.SOS1jjj5r4\"S2S/5SNSSSS3.SPS4jjj5r5SQSRS5jjr6SSSTS6jjr7g)U) annotations) TYPE_CHECKINGLiteral NamedTupleN)overload)_C_ops)argmaxargmin)VarDescVariable)ParamAliasDecoratorindex_select_decoratorparam_one_aliasparam_two_alias)inplace_apis_in_dygraph_only)check_variable_and_dtype) LayerHelpercorein_dynamic_modein_dynamic_or_pir_mode in_pir_mode)assign)Tensor)ForbidKeywordsDecoratorinputdim)xaxisrr c V[5(a[R"XX#5upVU$[US/SQS5 [ S 0[ 5D6nUR URSS9nUR [RRSS9nURSSU0XS.XUS .S 9 U$) a  Sorts the input along the given axis, and returns the corresponding index tensor for the sorted output values. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and the parameter name ``dim`` can be used as an alias for ``axis``. For example, ``argsort(input=tensor_x, dim=1)`` is equivalent to ``(x=tensor_x, axis=1)``. Args: x (Tensor): An input N-D Tensor with type bfloat16, float16, float32, float64, int16, int32, int64, uint8. alias: ``input``. axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is Rank(x). when axis<0, it works the same way as axis+R. Default is -1. alias: ``dim``. descending (bool, optional) : Descending is a flag, if set to true, algorithm will sort by descending order, else sort by ascending order. Default is false. stable (bool, optional): Whether to use stable sorting algorithm or not. When using stable sorting algorithm, the order of equivalent elements will be preserved. Default is False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, sorted indices(with the same shape as ``x`` and with data type int64). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]], ... [[5,2,4,2], ... [4,7,7,9], ... [1,7,0,6]]], ... dtype='float32') >>> out1 = paddle.argsort(x, axis=-1) >>> out2 = paddle.argsort(x, axis=0) >>> out3 = paddle.argsort(x, axis=1) >>> print(out1) Tensor(shape=[2, 3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[[0, 3, 1, 2], [0, 1, 2, 3], [2, 3, 0, 1]], [[1, 3, 2, 0], [0, 1, 2, 3], [2, 0, 3, 1]]]) >>> print(out2) Tensor(shape=[2, 3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[[0, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 0]], [[1, 0, 0, 0], [1, 1, 1, 1], [0, 0, 0, 1]]]) >>> print(out3) Tensor(shape=[2, 3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[[1, 1, 1, 2], [0, 0, 2, 0], [2, 2, 0, 1]], [[2, 0, 2, 0], [1, 1, 0, 2], [0, 2, 1, 1]]]) >>> x = paddle.to_tensor([1, 0]*40, dtype='float32') >>> out1 = paddle.argsort(x, stable=False) >>> out2 = paddle.argsort(x, stable=True) >>> print(out1) Tensor(shape=[80], dtype=int64, place=Place(cpu), stop_gradient=True, [55, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 1 , 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 17, 11, 13, 25, 7 , 3 , 27, 23, 19, 15, 5 , 21, 9 , 10, 64, 62, 68, 60, 58, 8 , 66, 14, 6 , 70, 72, 4 , 74, 76, 2 , 78, 0 , 20, 28, 26, 30, 32, 24, 34, 36, 22, 38, 40, 12, 42, 44, 18, 46, 48, 16, 50, 52, 54, 56]) >>> print(out2) Tensor(shape=[80], dtype=int64, place=Place(cpu), stop_gradient=True, [1 , 3 , 5 , 7 , 9 , 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 0 , 2 , 4 , 6 , 8 , 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78]) r)uint16float16float32float64int16int32int64uint8argsortTdtype stop_gradientr-XOutIndicesr descendingstabletypeinputsoutputsattrs)r*) rrr*rrlocals"create_variable_for_type_inferencer,r VarTypeINT64 append_op) rr r4r5name_idshelperouts T/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/tensor/search.pyr*r*7sF<     3&(377''8 77 OO ! !8  80VL   rDc[5(a[R"XX$S9$[S 0[ 5D6n[ US/SQS5 [ USSS/S5 UR UR5nURSXS .S U0S U0S 9 U$)a Returns a new tensor which indexes the ``input`` tensor along dimension ``axis`` using the entries in ``index`` which is a Tensor. The returned tensor has the same number of dimensions as the original ``x`` tensor. The dim-th dimension has the same size as the length of ``index``; other dimensions have the same size as in the ``x`` tensor. .. note:: Alias and Order Support: 1. The parameter name ``input`` can be used as an alias for ``x``. 2. The parameter name ``dim`` can be used as an alias for ``axis``. 3. This API also supports the PyTorch argument order ``(input, dim, index)`` for positional arguments, which will be converted to the Paddle order ``(x, index, axis)``. For example, ``paddle.index_select(input=x, dim=1, index=idx)`` is equivalent to ``paddle.index_select(x=x, axis=1, index=idx)``, and ``paddle.index_select(x, 1, idx)`` is equivalent to ``paddle.index_select(x, idx, axis=1)``. Args: x (Tensor): The input Tensor to be operated. The data of ``x`` can be one of float16, float32, float64, int32, int64, complex64 and complex128. alias: ``input``. index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64. axis (int, optional): The dimension in which we index. Default: if None, the ``axis`` is 0. alias: ``dim``. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Keyword Args: out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor, A Tensor with same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [9.0, 10.0, 11.0, 12.0]]) >>> index = paddle.to_tensor([0, 1, 1], dtype='int32') >>> out_z1 = paddle.index_select(x=x, index=index) >>> print(out_z1.numpy()) [[1. 2. 3. 4.] [5. 6. 7. 8.] [5. 6. 7. 8.]] >>> out_z2 = paddle.index_select(x=x, index=index, axis=1) >>> print(out_z2.numpy()) [[ 1. 2. 2.] [ 5. 6. 6.] [ 9. 10. 10.]] rG index_selectr) boolr"r#r$r%r'r( complex64 complex128z!paddle.tensor.search.index_selectindexr'r(r/Indexr1rr6)rI) rrrIrr;rr<r,r?)rrMr r@rDrCs rErIrIst""1T;;8vx8   0 !   g  /  77@+CL$-   rFcgNras_tuplerDs rEnonzerorUsrFcgrQrRrSs rErUrU$srFcgrQrRrSs rErUrU*s#&rFc[5(a[R"XS9nOj[US/SQS5 [ S 0[ 5D6nUR [RRRS9nURSSU0SU/0S9 U(dU$URn[U5Vs/sH ocS S 2U4PM nn[U5$s snf) a Return a tensor containing the indices of all non-zero elements of the `input` tensor. If as_tuple is True, return a tuple of 1-D tensors, one for each dimension in `input`, each containing the indices (in that dimension) of all non-zero elements of `input`. Given a n-Dimensional `input` tensor with shape [x_1, x_2, ..., x_n], If as_tuple is False, we can get a output tensor with shape [z, n], where `z` is the number of all non-zero elements in the `input` tensor. If as_tuple is True, we can get a 1-D tensor tuple of length `n`, and the shape of each 1-D tensor is [z, 1]. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``nonzero(input=tensor_x)`` is equivalent to ``nonzero(x=tensor_x)``. Args: x (Tensor): The input tensor variable. alias: ``input``. as_tuple (bool, optional): Return type, Tensor or tuple of Tensor. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor or tuple of Tensor, The data type is int64. Examples: .. code-block:: python >>> import paddle >>> x1 = paddle.to_tensor([[1.0, 0.0, 0.0], ... [0.0, 2.0, 0.0], ... [0.0, 0.0, 3.0]]) >>> x2 = paddle.to_tensor([0.0, 1.0, 0.0, 3.0]) >>> out_z1 = paddle.nonzero(x1) >>> print(out_z1) Tensor(shape=[3, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0], [1, 1], [2, 2]]) >>> out_z1_tuple = paddle.nonzero(x1, as_tuple=True) >>> for out in out_z1_tuple: ... print(out) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 2]) Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 1, 2]) >>> out_z2 = paddle.nonzero(x2) >>> print(out_z2) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[1], [3]]) >>> out_z2_tuple = paddle.nonzero(x2, as_tuple=True) >>> for out in out_z2_tuple: ... print(out) Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [1, 3]) rGr)r&r'r(r"r#r$r%rJ where_indexr, Conditionr1r7r8r9N)rY)rrrUrrr;r<rr r=r>r?ndimrangetuple)rrTrDoutsrCrankilist_outs rErUrU0s|~~a)    7fh788,,&&,,9   Q'7%$    vv(-d 4 1AJ 4X5s1Cc[USS9$)a Return a tensor containing the indices of all non-zero elements of the `input` tensor. The returned tensor has shape [z, n], where `z` is the number of all non-zero elements in the `input` tensor, and `n` is the number of dimensions in the `input` tensor. Args: input (Tensor): The input tensor variable. Returns: Tensor, The data type is int64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 0.0, 0.0], ... [0.0, 2.0, 0.0], ... [0.0, 0.0, 3.0]]) >>> out = paddle.tensor.search.argwhere(x) >>> print(out) Tensor(shape=[3, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 0], [1, 1], [2, 2]]) F)rT)rU)rs rEargwhereres: 55 ))rFc.[R"X5$)a Return a tensor containing the indices of all non-zero elements of the `input` tensor. Using a manually set total_true_num as shape information, thereby eliminating the need to transfer shape information from the device to the host. Args: x (Tensor): The input tensor variable. total_true_num (int): The manually set output shape. Returns: Tensor, The data type is int64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.0, 1.0, 0.0, 3.0]) >>> out = paddle.tensor.search._restrict_nonzero(x, 2) >>> print(out) Tensor(shape=[2, 1], dtype=int64, place=Place(gpu), stop_gradient=True, [[1], [3]]) )rrestrict_nonzero) conditiontotal_true_nums rE_restrict_nonzerorjs4  " "9 ==rFz paddle.sortzpaddle.compat.sortz torch.sort) illegal_keys func_name correct_name url_suffixc 6[5(a[R"XX#5upVU$[S 0[ 5D6nUR UR SS9nUR [RRSS9n URSSU0XS.XUS.S 9 U$) an Sorts the input along the given axis, and returns the sorted output tensor. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True. Args: x (Tensor): An input N-D Tensor with type float32, float64, int16, int32, int64, uint8. axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is Rank(x). when axis<0, it works the same way as axis+R. Default is -1. descending (bool, optional) : Descending is a flag, if set to true, algorithm will sort by descending order, else sort by ascending order. Default is false. stable (bool, optional): Whether to use stable sorting algorithm or not. When using stable sorting algorithm, the order of equivalent elements will be preserved. Default is False. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, sorted tensor(with the same shape and data type as ``x``). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]], ... [[5,2,4,2], ... [4,7,7,9], ... [1,7,0,6]]], ... dtype='float32') >>> out1 = paddle.sort(x=x, axis=-1) >>> out2 = paddle.sort(x=x, axis=0) >>> out3 = paddle.sort(x=x, axis=1) >>> print(out1.numpy()) [[[5. 5. 8. 9.] [0. 0. 1. 7.] [2. 4. 6. 9.]] [[2. 2. 4. 5.] [4. 7. 7. 9.] [0. 1. 6. 7.]]] >>> print(out2.numpy()) [[[5. 2. 4. 2.] [0. 0. 1. 7.] [1. 7. 0. 4.]] [[5. 8. 9. 5.] [4. 7. 7. 9.] [6. 9. 2. 6.]]] >>> print(out3.numpy()) [[[0. 0. 1. 4.] [5. 8. 2. 5.] [6. 9. 9. 7.]] [[1. 2. 0. 2.] [4. 7. 4. 6.] [5. 7. 7. 9.]]] Fr+Tr.r*r/r0r3r6)sort) rrr*rr;r<r,r r=r>r?) rr r4r5r@r`rArCrDrBs rErprpsP..*= 0vx077''8 77 OO ! !8  80VL   rFcDUc [USS9$[[USS9U5$)a Sorts the input along the given axis = 0, and returns the sorted output tensor. The sort algorithm is ascending. Args: input (Tensor): An input N-D Tensor with type float32, float64, int16, int32, int64, uint8. out(Tensor, optional): The output tensor. Returns: Tensor, sorted tensor(with the same shape and data type as ``input``). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[[5,8,9,5], ... [0,0,1,7], ... [6,9,2,4]], ... [[5,2,4,2], ... [4,7,7,9], ... [1,7,0,6]]], ... dtype='float32') >>> out1 = paddle.msort(input=x) >>> print(out1.numpy()) [[[5. 2. 4. 2.] [0. 0. 1. 7.] [1. 7. 0. 4.]] [[5. 8. 9. 5.] [4. 7. 7. 9.] [6. 9. 2. 6.]]] >>> out2 = paddle.empty_like(x) >>> paddle.msort(input=x, out=out2) >>> print(out2.numpy()) [[[5. 2. 4. 2.] [0. 0. 1. 7.] [1. 7. 0. 4.]] [[5. 8. 9. 5.] [4. 7. 7. 9.] [6. 9. 2. 6.]]] r)r )rpr)rrDs rEmsortrr,s+\ {E""d5q)3//rFc$[5(a[R"XU5$[S 0[ 5D6nSU/0n0nXS'X&S'UR UR S9nUR SS9nURSUU/U/S.US9 S UlXx4$) a? Used to find values and indices of the modes at the optional axis. Args: x (Tensor): Tensor, an input N-D Tensor with type float32, float64, int32, int64. axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. Default is -1. keepdim (bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimensions is one fewer than x since the axis is squeezed. Default is False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: tuple (Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> import paddle >>> tensor = paddle.to_tensor([[[1,2,2],[2,3,3]],[[0,5,5],[9,9,0]]], dtype=paddle.float32) >>> res = paddle.mode(tensor, 2) >>> print(res) (Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[2., 3.], [5., 9.]]), Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 2], [2, 1]])) moder/r keepdimrZr(r0r6T)rt) rrrtrr;r<r,r?r-) rr rur@rCr8r:valuesindicess rErtrt`sB{{1G,,0vx0sf "i:::I;;';J#H';  !%rFother)ryc[R"U5(a[R"U5n[R"U5(a[R"U5nUcUc [ USUS9$UbUc [ S5e[ UR5n[ UR5n[ UR5n[5(aUR[R:wa[ SUR35e[R"Xg5n[R"X5nUn Un Un XX:wa[R"X5n Xh:wa[R"X5n Xx:wa[R"X5n [R"XXS9$Xg:Xa XV:XaUn Un Un GO [R"U5n [R"U5n [R"U5n[R "XR5n[R "XR5n[R""X5n[R""UU5n[R""UU5n [R""UU5n [R""UU5n [R "U S5n [%5(a[R"XXS9$['USS/S5 ['US /S QS5 ['US /S QS5 [)S0[+5D6nUR-URS 9nUR/SU U U S .SU/0S9 U$)a Return a Tensor of elements selected from either :attr:`x` or :attr:`y` according to corresponding elements of :attr:`condition`. Concretely, .. math:: out_i = \begin{cases} x_i, & \text{if} \ condition_i \ \text{is} \ True \\ y_i, & \text{if} \ condition_i \ \text{is} \ False \\ \end{cases}. Notes: ``numpy.where(condition)`` is identical to ``paddle.nonzero(condition, as_tuple=True)``, please refer to :ref:`api_paddle_nonzero`. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``other`` can be used as an alias for ``y``. For example, ``paddle.where(condition, input=x, other=y)`` can be written as ``paddle.where(condition, x=x, y=y)``. Args: condition (Tensor): The condition to choose x or y. When True (nonzero), yield x, otherwise yield y, must have a dtype of bool if used as mask. x (Tensor|scalar|None, optional): A Tensor or scalar to choose when the condition is True with data type of bfloat16, float16, float32, float64, int32 or int64. Either both or neither of x and y should be given. alias: ``input``. y (Tensor|scalar|None, optional): A Tensor or scalar to choose when the condition is False with data type of bfloat16, float16, float32, float64, int32 or int64. Either both or neither of x and y should be given. alias: ``other``. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out (Tensor|None, optional): The output tensor. If set, the result will be stored to this tensor. Default is None. Returns: Tensor, A Tensor with the same shape as :attr:`condition` and same data type as :attr:`x` and :attr:`y`. If :attr:`x` and :attr:`y` have different data types, type promotion rules will be applied (see `Auto Type Promotion `_). Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([0.9383, 0.1983, 3.2, 1.2]) >>> y = paddle.to_tensor([1.0, 1.0, 1.0, 1.0]) >>> out = paddle.where(x>1, x, y) >>> print(out) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1. , 1. , 3.20000005, 1.20000005]) >>> out = paddle.where(x>1) >>> print(out) (Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, [2, 3]),) T)rTrD1either both or neither of x and y should be givenPThe `condition` is expected to be a boolean Tensor, but got a Tensor with dtype rGrJrhwhererr"r#r$r%r'r(ryrZ)r[r/Yr1r\)r})npisscalarpaddle to_tensorrU ValueErrorlistshaperr,rJbroadcast_shape broadcast_torr} zeros_likecastaddrrrr;r<r?)rhrryr@rDcondition_shapex_shapey_shaper broadcast_x broadcast_ybroadcast_condition zeros_like_x zeros_like_yzeros_like_condition cast_condbroadcast_zerosrCs rEr}r}st {{1~~   Q  {{1~~   Q yQYy4S99yAILMM9??+O177mG177mG ??fkk )//8.?A !00B 00    '  -"("5"5##   % --kKK  % --kKK|| k   /"<"+ KK!,,Q/L!,,Q/L#)#4#4Y#? #);;/CWW#M  Iww7I$jjDO$jj:NOO **Q8K **Q8K"(**Y"H "(++.A6"J  ==<<#+  %Y fXw O $M   %M   !5FH5F;;!'';JC   !4$$   JrFc[R"U5(d"U5(a [S5eUbUc [S5eUR[R :wa[SUR35e[ UR5n[ UR5n[ UR5n[R"XV5n[R"Xt5nUnUn Un XG:wa[R"X5n XW:wa[R"X5nXg:wa[R"X5n [5(a[R"XU 5$g)z Inplace version of ``where`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_where`. r{Nr|) rrrr,rrJrrrrrrwhere_) rhrryr@rrrrrrrs rErr7s3 {{1~~QLMMyAILMM&++% ++4??*; =  9??+O177mG177mG,,W>O,,_NOKK#)$11  !))+G !))+G }}0{KKrFc[5(a[R"X5$[S 0[ 5D6n[ US/SQS5 [ USSS/S5 UR URS9nURSXS .S U0S 9 U$) a **IndexSample Layer** IndexSample OP returns the element of the specified location of X, and the location is specified by Index. .. code-block:: text Given: X = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] Index = [[0, 1, 3], [0, 2, 4]] Then: Out = [[1, 2, 4], [6, 8, 10]] Args: x (Tensor): The source input tensor with 2-D shape. Supported data type is int32, int64, bfloat16, float16, float32, float64, complex64, complex128. index (Tensor): The index input tensor with 2-D shape, first dimension should be same with X. Data type is int32 or int64. Returns: Tensor, The output is a tensor with the same shape as index. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [9.0, 10.0, 11.0, 12.0]], dtype='float32') >>> index = paddle.to_tensor([[0, 1, 2], ... [1, 2, 3], ... [0, 0, 0]], dtype='int32') >>> target = paddle.to_tensor([[100, 200, 300, 400], ... [500, 600, 700, 800], ... [900, 1000, 1100, 1200]], dtype='int32') >>> out_z1 = paddle.index_sample(x, index) >>> print(out_z1.numpy()) [[1. 2. 3.] [6. 7. 8.] [9. 9. 9.]] >>> # Use the index of the maximum value by topk op >>> # get the value of the element of the corresponding index in other tensors >>> top_value, top_index = paddle.topk(x, k=2) >>> out_z2 = paddle.index_sample(target, top_index) >>> print(top_value.numpy()) [[ 4. 3.] [ 8. 7.] [12. 11.]] >>> print(top_index.numpy()) [[3 2] [3 2] [3 2]] >>> print(out_z2.numpy()) [[ 400 300] [ 800 700] [1200 1100]] index_sampler)r"r#r$r%r'r(rKrLz!paddle.tensor.search.index_samplerMr'r(rZrNr1r\)r) rrrrr;rr<r,r?)rrMrCrDs rErrhsR""1,,8vx8   0  !   g  /  77agg7F+CL   rFcj[5(aC[5(a[US/SQS5 [USS/S5 [R"XUS9$[US/SQS5 [USS/S5 [ S 0[ 5D6nURURS 9nURSXS .S U0S 9 U$)a Returns a new 1-D tensor which indexes the input tensor according to the ``mask`` which is a tensor with data type of bool. Note: ``paddle.masked_select`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: x (Tensor): The input Tensor, the data type can be int32, int64, uint16, float16, float32, float64. alias: ``input``. mask (Tensor): The Tensor containing the binary mask to index with, it's data type is bool. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor, A 1-D Tensor which is the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0], ... [5.0, 6.0, 7.0, 8.0], ... [9.0, 10.0, 11.0, 12.0]]) >>> mask = paddle.to_tensor([[True, False, False, False], ... [True, True, False, False], ... [True, False, False, False]]) >>> out = paddle.masked_select(x, mask) >>> print(out.numpy()) [1. 5. 6. 9.] r)r#r$r%r'r(r"z paddle.tensor.search.mask_selectmaskrJz"paddle.tensor.search.masked_selectrG masked_selectrZ)r/Maskrr\)r) rrrrrrr;r<r,r?)rrr@rDrCs rErrsV == $M2   %fvh(L ##A55  I .  ! &6($H 9977agg7F )#J   rFc*\rSrSr%S\S'S\S'Srg) TopKRetTypeirrvrwrRN)__name__ __module__ __qualname____firstlineno____annotations____static_attributes__rRrFrErrs N OrFrc [5(a9UcSn[R"XX#XFS9upxUb[USUSS9$[XxS9$[ S0[ 5D6n SU/0n 0n [ U[5(aU/U S'OS U0n X;S 'XKS 'UbX+S 'U RURS 9nU RSS 9nU RSU U/U/S.U S9 SUl Xx4$)a Return values and indices of the k largest or smallest at the optional axis. If the input is a 1-D Tensor, finds the k largest or smallest values and indices. If the input is a Tensor with higher rank, this operator computes the top k values and indices along the :attr:`axis`. Args: x (Tensor): Tensor, an input N-D Tensor with type float32, float64, int32, int64. k (int, Tensor): The number of top elements to look for along the axis. axis (int|None, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. Default is -1. largest (bool, optional) : largest is a flag, if set to true, algorithm will sort by descending order, otherwise sort by ascending order. Default is True. sorted (bool, optional): controls whether to return the elements in sorted order, default value is True. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> import paddle >>> data_1 = paddle.to_tensor([1, 4, 5, 7]) >>> value_1, indices_1 = paddle.topk(data_1, k=1) >>> print(value_1) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [7]) >>> print(indices_1) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [3]) >>> data_2 = paddle.to_tensor([[1, 4, 5, 7], [2, 6, 2, 5]]) >>> value_2, indices_2 = paddle.topk(data_2, k=1) >>> print(value_2) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[7], [6]]) >>> print(indices_2) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[3], [1]]) >>> value_3, indices_3 = paddle.topk(data_2, k=1, axis=-1) >>> print(value_3) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[7], [6]]) >>> print(indices_3) Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True, [[3], [1]]) >>> value_4, indices_4 = paddle.topk(data_2, k=1, axis=0) >>> print(value_4) Tensor(shape=[1, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 6, 5, 7]]) >>> print(indices_4) Tensor(shape=[1, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 1, 0, 0]]) rGrr)rvrwtop_k_v2r/Kklargestsortedr rZr(r0r6T)r) rrtopkrrr; isinstancer r<r,r?r-) rrr rrr@rDrvrwrCr8r:s rErr$s\ <D ++aD6K ?c!fc!f= =&::4684s a " "#F3K!HE"i h   &M:::I;;';J#H';  !%rFsorted_sequence boundariesc [US/SQS5 UR5S:wa[SUR535e[XX#XES9$)a[ This API is used to find the index of the corresponding 1D tensor `sorted_sequence` in the innermost dimension based on the given `x`. Args: x (Tensor): An input N-D tensor value with type int32, int64, float32, float64. alias: ``input``. sorted_sequence (Tensor): An input 1-D tensor with type int32, int64, float32, float64. The value of the tensor monotonically increases in the innermost dimension. alias: ``boundaries``. out_int32 (bool, optional): Data type of the output tensor which can be int32, int64. The default value is False, and it indicates that the output data type is int64. right (bool, optional): Find the upper or lower bounds of the sorted_sequence range in the innermost dimension based on the given `x`. If the value of the sorted_sequence is nan or inf, return the size of the innermost dimension. The default value is False and it shows the lower bounds. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor (the same sizes of the `x`), return the tensor of int32 if set :attr:`out_int32` is True, otherwise return the tensor of int64. Examples: .. code-block:: python >>> import paddle >>> sorted_sequence = paddle.to_tensor([2, 4, 8, 16], dtype='int32') >>> x = paddle.to_tensor([[0, 8, 4, 16], [-1, 2, 8, 4]], dtype='int32') >>> out1 = paddle.bucketize(x, sorted_sequence) >>> print(out1) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 2, 1, 3], [0, 0, 2, 1]]) >>> out2 = paddle.bucketize(x, sorted_sequence, right=True) >>> print(out2) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 3, 2, 4], [0, 1, 3, 2]]) >>> out3 = x.bucketize(sorted_sequence) >>> print(out3) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 2, 1, 3], [0, 0, 2, 1]]) >>> out4 = x.bucketize(sorted_sequence, right=True) >>> print(out4) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 3, 2, 4], [0, 1, 3, 2]]) SortedSequence)r$r%r'r(paddle.searchsortedrz8sorted_sequence tensor must be 1 dimension, but got dim rG)rrr searchsorted)rr out_int32rightr@rDs rE bucketizers^r0  !FGZGZG\F] ^   Id LLrFrv)siderDsortercVUbUS:XaSn[5(a)UbURSUS9n[R"XX#US9$[ US/SQS5 [ US /SQS5 [ S0[ 5D6nU(aS OS n URU S 9nURS XS.SU0X#S.S9 U$)a Find the index of the corresponding `sorted_sequence` in the innermost dimension based on the given `values`. Args: sorted_sequence (Tensor): An input N-D or 1-D tensor with type int32, int64, float16, float32, float64, bfloat16. The value of the tensor monotonically increases in the innermost dimension. values (Tensor): An input N-D tensor value with type int32, int64, float16, float32, float64, bfloat16. alias: ``input``. out_int32 (bool, optional): Data type of the output tensor which can be int32, int64. The default value is False, and it indicates that the output data type is int64. right (bool, optional): Find the upper or lower bounds of the sorted_sequence range in the innermost dimension based on the given `values`. If the value of the sorted_sequence is nan or inf, return the size of the innermost dimension. The default value is False and it shows the lower bounds. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. side (str|None, optional): The same as right but preferred. `left` corresponds to False for right and `right` corresponds to True for right. It will error if this is set to `left` while right is True. Default value is None. sorter (Tensor|None, optional): if provided, a tensor matching the shape of the unsorted `sorted_sequence` containing a sequence of indices that sort it in the ascending order on the innermost dimension out (Tensor|None, optional): The output tensor. Default: None. Returns: Tensor (the same sizes of the `values`), return the tensor of int32 if set :attr:`out_int32` is True, otherwise return the tensor of int64. Examples: .. code-block:: python >>> import paddle >>> sorted_sequence = paddle.to_tensor([[1, 3, 5, 7, 9, 11], ... [2, 4, 6, 8, 10, 12]], dtype='int32') >>> values = paddle.to_tensor([[3, 6, 9, 10], [3, 6, 9, 10]], dtype='int32') >>> out1 = paddle.searchsorted(sorted_sequence, values) >>> print(out1) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 3, 4, 5], [1, 2, 4, 4]]) >>> out2 = paddle.searchsorted(sorted_sequence, values, right=True) >>> print(out2) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[2, 3, 5, 5], [1, 3, 4, 5]]) >>> sorted_sequence_1d = paddle.to_tensor([1, 3, 5, 7, 9, 11, 13]) >>> out3 = paddle.searchsorted(sorted_sequence_1d, values) >>> print(out3) Tensor(shape=[2, 4], dtype=int64, place=Place(cpu), stop_gradient=True, [[1, 3, 4, 5], [1, 3, 4, 5]]) rTr)r rwrGrr~rValuesrr'r(rZ)rrr1)rrr6)r) rtake_along_axisrrrrr;r<r?) rrvrrr@rrDrrCout_types rErrst DGO  -==>O"" Y3   !   I !  !   I !  8vx8'7W77h7G&5HCL ):   rFc\[5(a2Ub[R"XX#5$[R"XSU5$[S 0[ 5D6nSU/0nSU0nUbX'S'UR UR S9nUR SS9n URSUU/U /S.US 9 S U lX4$) a Find values and indices of the k-th smallest at the axis. Args: x (Tensor): A N-D Tensor with type float16, float32, float64, int32, int64. k (int): The k for the k-th smallest number to look for along the axis. axis (int, optional): Axis to compute indices along. The effective range is [-R, R), where R is x.ndim. when axis < 0, it works the same way as axis + R. The default is None. And if the axis is None, it will computed as -1 by default. keepdim (bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimensions is one fewer than x since the axis is squeezed. Default is False. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn((2,3,2)) >>> print(x) >>> # doctest: +SKIP('Different environments yield different output.') Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[ 0.11855337, -0.30557564], [-0.09968963, 0.41220093], [ 1.24004936, 1.50014710]], [[ 0.08612321, -0.92485696], [-0.09276631, 1.15149164], [-1.46587241, 1.22873247]]]) >>> # doctest: -SKIP >>> y = paddle.kthvalue(x, 2, 1) >>> print(y) >>> # doctest: +SKIP('Different environments yield different output.') (Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.11855337, 0.41220093], [-0.09276631, 1.15149164]]), Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1], [1, 1]])) >>> # doctest: -SKIP rkthvaluer/rr rZr(r0r6T)r) rrrrr;r<r,r?r-) rrr rur@rCr8r:rvrws rErr9sb  ??17 7??1W5 5  0vx 0FA3ZF !HE f  6 6QWW 6 EF77g7FG  gY7  !G ?rFc [5(a,[R"XX#XEU5n U(aU $U SU S4$XX#S.n XEUS.n [S 0[ 5D6n U R UR S9n U R SS9nU R UR S9nU R SS9nU RSU U UUUS.U S 9 U(aXUU4$X4$) a Get the TopP scores and ids. Args: x(Tensor): An input 2-D Tensor with type float32, float16 and bfloat16. ps(Tensor): A 1-D Tensor with type float32, float16 and bfloat16, used to specify the top_p corresponding to each query. threshold(Tensor|None, optional): A 1-D Tensor with type float32, float16 and bfloat16, used to avoid sampling low score tokens. topp_seed(Tensor|None, optional): A 1-D Tensor with type int64, used to specify the random seed for each query. seed(int, optional): the random seed. Default is -1, k(int): the number of top_k scores/ids to be returned. Default is 0. mode(str): The mode to choose sampling strategy. If the mode is `truncated`, sampling will truncate the probability at top_p_value. If the mode is `non-truncated`, it will not be truncated. Default is `truncated`. return_top(bool): Whether to return the top_k scores and ids. Default is False. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.seed(2023) >>> x = paddle.randn([2,3]) >>> print(x) Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[-0.32012719, -0.07942779, 0.26011357], [ 0.79003978, -0.39958701, 1.42184138]]) >>> paddle.seed(2023) >>> ps = paddle.randn([2]) >>> print(ps) Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [-0.32012719, -0.07942779]) >>> value, index = paddle.tensor.top_p_sampling(x, ps) >>> print(value) Tensor(shape=[2, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[0.26011357], [1.42184138]]) >>> print(index) Tensor(shape=[2, 1], dtype=int64, place=Place(gpu:0), stop_gradient=True, [[2], [2]]) rr)rps threshold topp_seed)seedrrttop_p_samplingrZr()rDrB topk_scorestopk_idsr6)r)rrrrr;r<r,r?)rrrrrrrt return_topr@resr8r:rCrDrBrrs rErrs~##A9$O Jq63q6> !Y OF4 0E  6VX 6F  3 3!'' 3 BC  3 3' 3 BC;;!'';JK88w8GH  &   h..xrF)rFFN) rrr intr4rJr5rJr@ str | Nonereturnr)rN) rrrMrr rr@rrD Tensor | Nonerr).)rrrTzLiteral[False]rDrrr)rrrTz Literal[True]rDrrztuple[Tensor, ...])rrrTrJrDrrzTensor | tuple[Tensor, ...])F)rrrDr)rrrr)rhrrirrr)rrrDrrr)rFN) rrr rrurJr@rrtuple[Tensor, Tensor])NNN) rhrrTensor | float | Noneryrr@rrDrrr) rhrrrryrr@rrr)rrrMrrrrQ) rrrrr@rrDrrr)NTTN)rrrz int | Tensorr int | NonerrJrrJr@rrDztuple[Tensor, Tensor] | Nonerr)FFN)rrrrrrJrrJr@rrDrrr)rrrvrrrJrrJr@rrrrDrrrrr)NFN) rrrrr rrurJr@rrr)NNrr truncatedFN)rrrrrrrrrrrrrtz%Literal['truncated', 'non-truncated']rrJr@rrr)8 __future__rtypingrrrnumpyrtyping_extensionsrrr paddle._C_opsr r paddle.common_ops_importr r paddle.utils.decorator_utilsr rrrpaddle.utils.inplace_utilsrbase.data_feederr frameworkrrrrrcreationrrr__all__r*rIrUrerjrprrrtr}rrrrrrrrrrRrFrErs#55& (6 D7@ G9ug67 C C CC  C  C  C8CL \  \ \ \ \  \  \ \\~ *-GK '7D    ),FJ &6C   #&=A& &&-:& & & #w _t_!_D*@>:5!%  S S SS  S  S  S  Sl2610jJN4 44(,4