ёiS2^SSKJr SSKJrJr SSKrSSKrSSKJrJ r SSK J r SSK J r JrJrJrJr SSKJr SSKJrJr \(aSSKJr SS KJrJrJrJr SS KJrJrJ r S S /r!S r"Sr#Sr$Sr%SSSjjr&Sr'\SSSjj5r(g)) annotations) TYPE_CHECKINGAnyN)_C_opsin_dynamic_mode) convert_dtype)_current_expected_place_get_paddle_placecore dygraph_only in_pir_mode) LayerHelper)max to_tensor)CPUPlaceCUDAPinnedPlace CUDAPlaceTensor) DTypeLikeNumericSequence ShapeLikesparse_coo_tensorsparse_csr_tensorcU(a<[U5[UR5:waUR[U55$U$)N)rdtypeastype)datars V/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/sparse/creation.py _handle_dtyper.s4  =#< <;;}U34 4 Kc[UR5S:Xde[USS9nUS-nUR5n[UR5S:a#[R "X!RSS5n[ U5$)N)axis)lenshapernumpynpappendlist)indicesvalueslenss r_infer_dense_shaper.5sm w}}  "" " wQ D !8D ::>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> print(coo) Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1, 2], [1, 2, 0]], values=[1., 2., 3.]) NTrr3 stop_gradientr"z'indices' must be 2-D.r#rzBthe indices and values must have same number of non-zero, but get z and Fzthe minimum shape required is z , but get z7the number of dimensions(len(shape) must be sparse_dim(z) + dense_dim(z ), but get sparse_sparse_coo_tensor)r,r+r&out)typeinputsoutputsattrs)rr4r0r eagerrrr%r&r2r<rr3_equals_copy_torr?r.r*rrAr r)create_sparse_variable_for_type_inference append_op)r+r,r&rr3r?nnz sparse_dim dense_dim min_shapeop_typerDrFhelperrBs rrrSsl5!'4::#4#455t5G&$**"3"344veCF w}}  "56 6mmA]]1% W]]+ ,,q/ !TUXTYY^_e_k_klm_n^op  %) }}$$U++&&ue4G||##E**__UE2Fv-,&w7 =EKE  4YKz%Q5zZ33 Mj\Yghqgrr}BCHI~JK..vFF ..vFF-"7 8 E!H% W%>>uE%U   r cfUR5nUR5n[R"USSUSS:5S-n[[ U5U- 5U-[ U5:wa[ SUS35eUR 5S-n[[ U5U- 5S- nUS:XaXe/$XFU/$)Nr@r#z-The calculated original matrix batch size is za, but it cannot correctly split the row data. Please carefully check the data or the input shape.)r'r(sumintr%r2r)crowscols crows_numpy cols_numpybatchscolrows r_infer_dense_csr_shaper\s++-KJ VVK${126 7! ;F C v %&/C 4DD;F8De f   .. Q C c+' (1 ,C {zS!!r c([U5n[U[RR5(d [ USUSS9n[U[RR5(d [ USUSS9n[U[RR5(d [ X$XV5n[ UR5 [ UR5 Ub-[U5S:wa[U5S:wa[SU35eO [X5nU[U5S- nURRU5(dURUS5nURRU5(dURUS5nURRU5(dURUS5n[X$5nXbl[UR 5S:wd2[UR 5S:wd[UR 5S:wa [S 5e[U5[U5:wa [S 5e[U5S:XakUR S US-:wa[S UR S S US35eUS S :wa [S5eUSUR S :wa [S5eO8UR S US--S :wa[S UR S SUS35e[RR#XX#U5$)a Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the ``crows``, ``cols`` and ``values``. Currently, the crows and cols of each batch must be incremented. Args: crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the starting position of the first non-zero element of each row in values. Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. cols(list|tuple|ndarray|Tensor): 1-D array, the column of non-zero elements. Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. values(list|tuple|ndarray|Tensor): 1-D array, the non-zero elements. Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor. shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of original dense tensor. hold all elements. dtype(str|paddle.dtype|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8', 'complex64' , 'complex128'. Default: None, infers dtype from ``data`` except for python float number which gets dtype from ``get_default_type`` . place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str|None, optional): The place to allocate Tensor. Can be CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True. Returns: Tensor: A Tensor constructed from ``crows``, ``cols`` and ``values`` . Examples: .. code-block:: python >>> import paddle >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> print(csr) Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(cpu), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[1, 2, 3, 4, 5]) NTr>r"z>SparseCsrTensor only support 2-D or 3-D matrix. but get shape Fr#z-The 'crows', 'cols' and 'values' must be 1-D.z3the length of cols must be same as length of valuesrz The length(z%) of crows must be equal to the rows(z)+1 of matrix.z the 0th value of crows must be 0r@zTAX %ekk!n--RSWRXXfg  8q=?@ @ 9 Q 'N  ( ;;q>TAX &! +ekk!n--STXSYYgh  :: ' ' VM r )NNNT)r+zMlist[list[int]] | tuple[tuple[int, ...], ...] | npt.NDArray[np.int_] | Tensorr,+NumericSequence | npt.NDArray[Any] | Tensorr&ShapeLike | NonerDTypeLike | Noner33CPUPlace | CUDAPinnedPlace | CUDAPlace | str | Noner?boolreturnr)rU;list[int] | tuple[int, ...] | npt.NDArray[np.int_] | TensorrVrfr,r`r&rarrbr3rcr?rdrer)) __future__rtypingrrr'r(r6rrpaddle.base.data_feederrpaddle.base.frameworkr r r r r paddle.base.layer_helperr paddle.tensorrr numpy.typingnptrrrrpaddle._typingrrr__all__rr.r4r<rr\rr rrrs?#% *11(CCDD    #"AEt t 8t t t ?tt tn "" #"AEs Fs Es 8s  s  s ? ss ssr