`ikOddlZ ddlZdZn #e$rdZYnwxYwddlZddlmZddlmZddlm Z ddlm Z ddlm Z dd lm Z dd lmZGd d e jZd ZdS)NTF)_core)_base)_csc)_csr)_data)_util)_sputilsceZdZdZdZejddddZdd Zdd Z dd Z ddZ dZ dZ ddZddZdddZdZd dZd!dZd!dZd!dZd"dZdS)# coo_matrixaRCOOrdinate format sparse matrix. This can be instantiated in several ways. ``coo_matrix(D)`` ``D`` is a rank-2 :class:`cupy.ndarray`. ``coo_matrix(S)`` ``S`` is another sparse matrix. It is equivalent to ``S.tocoo()``. ``coo_matrix((M, N), [dtype])`` It constructs an empty matrix whose shape is ``(M, N)``. Default dtype is float64. ``coo_matrix((data, (row, col)))`` All ``data``, ``row`` and ``col`` are one-dimenaional :class:`cupy.ndarray`. Args: arg1: Arguments for the initializer. shape (tuple): Shape of a matrix. Its length must be two. dtype: Data type. It must be an argument of :class:`numpy.dtype`. copy (bool): If ``True``, copies of given data are always used. .. seealso:: :class:`scipy.sparse.coo_matrix` coozraw T row, raw T colzT diffz T diff_out = 1; if (i == 0 || row[i - 1] == row[i] && col[i - 1] == col[i]) { diff_out = 0; } diff = diff_out; *cupyx_scipy_sparse_coo_sum_duplicates_diffNFcn |"t|dkrtdtj|rX||j}|j}|j}|j}|j|jkrd}||j }|j |_ ntj |rw|\} } t| t| } } tjd|r|nd}tjdd}tjdd}| | f}d}d|_ n2t rt"j|r||}tj|j}tj|jd}tj|jd}d}||j }|j |_ nt+|t,rt|dkr |\}\}}n$#t.tf$rt/d wxYwtj|rI|jd kr>tj|r*|jd krtj|r |jd kstd t|t|cxkrt|ksntd d|_ ntj|r^|jdkrt/d tj|} | \}}| ||f}| j }d|_ nt/d ||j}nt;j|}|t:jt:jt:j t:j!t:j"fvrtd|#||}|#d|}|#d|}|}t|dkst|dkrtdt|$d zt|$d zf}t|dkr|$|dkrtd|$|d krtd|%dkrtd|%dkrtdtLj'(||||_||_tj |stdt|dt|d f|_)dS)Nz1Only two-dimensional sparse arrays are supported.FrdidtypeTzinvalid input formatz(row, column, and data arrays must be 1-Dz7row, column, and data array must all be the same lengthz'expected dimension <= 2 array or matrixzCOnly bool, float32, float64, complex64 and complex128 are supportedcopyz4cannot infer dimensions from zero sized index arraysz#row index exceeds matrix dimensionsz&column index exceeds matrix dimensionsznegative row index foundznegative column index foundz(invalid shape (must be a 2-tuple of int))*len ValueErrorrissparseasformatformatdatarowcolshapehas_canonical_formatrisshapeintcupyzeros_scipy_availablescipysparsetocooarray isinstancetuple TypeErrorisdensendim atleast_2dnonzerornumpybool_float32float64 complex64 complex128astypemaxmin sparse_data _data_matrix__init___shape) selfarg1rrrxrrrmndenses k/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupyx/scipy/sparse/_coo.pyr<zcoo_matrix.__init__>sY  UqCEE E >$  B 4 dk**A6D%C%C{dk))} ()(>D % % ]4 3 4DAqq663q66qA:a%!8S99D*Qc***C*Qc***CFED(,D % % ' 4%,"7"7"="=' 4 A:af%%D*QU#...C*QU#...CD} ()(>D % % e $ $ 4Ta 8#' jsCCz* 8 8 8 6777 8M$'' MDINNM#&&-;+.8q==M#&&,9+.8q== !KLLLIIS5555SXX5555 MOOO).D % % ]4 4y1}} IJJJOD))E}}HCc?DKE(,D % %233 3 =JEEK&&E emU]%*:< < <!"" "{{5t{,,jj4j((jj4j(( =3xx1}}CA  JLLL^^a'SWWYY!);??? ))$555}U## IGHH H%(mmSq]]2 s 'G00!HTc|rNt||j|jff|j|jSt||j|jff|j|jS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the index arrays (i.e. .row and .col) are copied. )rr)r rrrrr)r>rrs rD _with_datazcoo_matrix._with_datas  " 9:j 444 4$(+,DJj""" "rc Z|j\}}|| ks||kr tjd|jjStjt |t |dz|t|dz |j}|j|z|j k}|j r|j|}|j|}n]t|j||j||j |ff|j}| |j}|j}|||t |dz<|S)aReturns the k-th diagonal of the matrix. Args: k (int, optional): Which diagonal to get, corresponding to elements a[i, i+k]. Default: 0 (the main diagonal). Returns: cupy.ndarray : The k-th diagonal. rrr) rr#emptyrrr$r9r8rrr r sum_duplicates) r>krowscolsdiag diag_maskrrdiag_coos rDdiagonalzcoo_matrix.diagonals.Z d ::d:aty777 7z#dSAYY.s1ayy0@AA $ ,,,X\dh.  $ !(9%C9Y'DD!49Y#7$(HY$7)9L#M#O(, 444H  # # % % %,C=D $S3q!99_ rGcL|j\}}|dkr||ks |dkr| |krtd|jrt|sdS|jj}|j|jz |k}|dkrt||z|}|jrt|t|}tj ||j|k}tj | | |z|} tj ||} nt|||z }|jrt|t|}tj ||j|k}tj ||} tj |||z|} |jr |d|} ntj |||j} tj |j|| f|_tj |j|| f|_tj |j || f|_ d|_dS)arSet diagonal or off-diagonal elements of the array. Args: values (ndarray): New values of the diagonal elements. Values may have any length. If the diagonal is longer than values, then the remaining diagonal entries will not be set. If values are longer than the diagonal, then the remaining values are ignored. If a scalar value is given, all of the diagonal is set to it. k (int, optional): Which off-diagonal to set, corresponding to elements a[i,i+k]. Default: 0 (the main diagonal). rzk exceeds matrix dimensionsNrF)rrr.rrrrr9r# logical_orarangefull concatenaterr ) r>valuesrLMN idx_dtype full_keep max_indexkeepnew_rownew_colnew_datas rDsetdiagzcoo_matrix.setdiagsz1 EEa1ff!a%%QB!GG:;; ; ; s6{{  FHN Htx'1, q55AE1 I{ 8 3v;;77 ?9dh).CDDDk1"qb9nIFFFGk)9===GGAq1u I{ 8 3v;;77 ?9dh).CDDDk)9===Gk!Q])DDDG ; Fjyj)HHyF$*EEEH#TXd^W$=>>#TXd^W$=>>$diox%@AA $)!!!rGc|jdk}|j||_|j||_|j||_dS)zRemoves zero entories in place.rN)rrr)r>inds rDeliminate_zeroszcoo_matrix.eliminate_zeros s8i1nIcN 8C=8C=rGc|jS)zcReturns the shape of the matrix. Returns: tuple: Shape of the matrix. )r=)r>s rD get_shapezcoo_matrix.get_shapes {rGc,| |jjSt)z>Returns the number of stored values, including explicit zeros.)rsizer)r>axiss rDgetnnzzcoo_matrix.getnnzs <9> ! rGctstd|j|}|j|}|j|}t j|||ff|j S)a:Returns a copy of the array on host memory. Args: stream (cupy.cuda.Stream): CUDA stream object. If it is given, the copy runs asynchronously. Otherwise, the copy is synchronous. Returns: scipy.sparse.coo_matrix: Copy of the array on host memory. zscipy is not availablerI) r% RuntimeErrorrgetrrr&r'r r)r>streamrrrs rDrnzcoo_matrix.get"s  9788 8y}}V$$hll6""hll6""|&& C: dj'22 2rGC)orderctj||j}||jkr|S|j\}}|dkr{tj|t d|dz zt d|dz z}t j||j||jz}t||d\}}n|dkr{tj|t d|dz zt d|dz z}t j||j||jz}t||d\}}ntd|j } t| ||ff|d S) a5Gives a new shape to a sparse matrix without changing its data. Args: shape (tuple): The new shape should be compatible with the original shape. order: {'C', 'F'} (optional) Read the elements using this index order. 'C' means to read and write the elements using C-like index order. 'F' means to read and write the elements using Fortran-like index order. Default: C. Returns: cupyx.scipy.sparse.coo_matrix: sparse matrix rprr)maxvalrFz'order' must be 'C' or 'F'Frr) r check_shaperget_index_dtyper8r#multiplyrrdivmodrrr ) r>rqrnrowsncolsr flat_indicesr_r`ras rDreshapezcoo_matrix.reshape6s"$UDJ77 DJ  Kz u C<<,Auqy 1 11C5194E4EEHHHE=/46668<AL%lE!H== GWW c\\,Auqy 1 11C5194E4EEHHHE=/46668<AL%lE!H== GWW9:: :98gw%78$&&& &rGc |jrdStj|j|jg}tj|}|j|}|j|}|j|}||||jj}|dd r|}|}|} nCtj |d} t| ddz} tj | |jj }tj| d}tj| d} |jj jdkr'tjdd d d |||| ||| n|jj jd kr'tjdd d d |||| ||| nQ|jj jdkr<tjdddd|j|j||| |j|j|| ||_||_| |_d|_dS)afEliminate duplicate matrix entries by adding them together. .. warning:: When sorting the indices, CuPy follows the convention of cuSPARSE, which is different from that of SciPy. Therefore, the order of the output indices may differ: .. code-block:: python >>> # 1 0 0 >>> # A = 1 1 0 >>> # 1 1 1 >>> data = cupy.array([1, 1, 1, 1, 1, 1], 'f') >>> row = cupy.array([0, 1, 1, 2, 2, 2], 'i') >>> col = cupy.array([0, 0, 1, 0, 1, 2], 'i') >>> A = cupyx.scipy.sparse.coo_matrix((data, (row, col)), ... shape=(3, 3)) >>> a = A.get() >>> A.sum_duplicates() >>> a.sum_duplicates() # a is scipy.sparse.coo_matrix >>> A.row array([0, 1, 1, 2, 2, 2], dtype=int32) >>> a.row array([0, 1, 2, 1, 2, 2], dtype=int32) >>> A.col array([0, 0, 1, 0, 1, 2], dtype=int32) >>> a.col array([0, 0, 0, 1, 1, 2], dtype=int32) .. warning:: Calling this function might synchronize the device. .. seealso:: :meth:`scipy.sparse.coo_matrix.sum_duplicates` N)rirrrbz5T src_data, int32 src_row, int32 src_col, int32 indexz(raw T data, raw int32 row, raw int32 colz if (src_data) data[index] = true; row[index] = src_row; col[index] = src_col; ,cupyx_scipy_sparse_coo_sum_duplicates_assignfz atomicAdd(&data[index], src_data); row[index] = src_row; col[index] = src_col; czAT src_real, T src_imag, int32 src_row, int32 src_col, int32 indexz4raw T real, raw T imag, raw int32 row, raw int32 colz atomicAdd(&real[index], src_real); atomicAdd(&imag[index], src_imag); row[index] = src_row; col[index] = src_col; 4cupyx_scipy_sparse_coo_sum_duplicates_assign_complexT)r r#stackrrlexsortr_sum_duplicates_diffriallcumsumr"r$rrJkindElementwiseKernelrealimag) r>keysrqsrc_datasrc_rowsrc_coldiffrrrindexris rDrKzcoo_matrix.sum_duplicatesbs`J  $  F z48TX.// T""9U#(5/(5/((' (NN 8<<>>/ 2DCCCKC000EuRy>>A%D:d$)/:::D*T---C*T---Cy#s** &K> C  GWeT3 E E E E%,, &K> C  GWeT3 E E E E%,, &"J K  -%)TYS 2 2 2 $(!!!rGcT|||S)a'Returns a dense matrix representing the same value. Args: order (str): Not supported. out: Not supported. Returns: cupy.ndarray: Dense array representing the same value. .. seealso:: :meth:`scipy.sparse.coo_matrix.toarray` )rqout)tocsrtoarray)r>rqrs rDrzcoo_matrix.toarrays%zz||##%S#999rGc2|r|S|S)zConverts the matrix to COOrdinate format. Args: copy (bool): If ``False``, it shares data arrays as much as possible. Returns: cupyx.scipy.sparse.coo_matrix: Converted matrix. r)r>rs rDr(zcoo_matrix.tocoos  99;; KrGcddlm}|jdkr tj|j|jS|}|| |d| |}d|_ |S)a{Converts the matrix to Compressed Sparse Column format. Args: copy (bool): If ``False``, it shares data arrays as much as possible. Actually this option is ignored because all arrays in a matrix cannot be shared in coo to csc conversion. Returns: cupyx.scipy.sparse.csc_matrix: Converted matrix. rcusparserrT) cupyxrnnzr csc_matrixrrrrKcoosortcoo2cscr r>rrr@s rDtocsczcoo_matrix.tocsc #""""" 8q==?4:TZ@@@ @ IIKK C     Q  !%rGcddlm}|jdkr tj|j|jS|}|| |d| |}d|_ |S)axConverts the matrix to Compressed Sparse Row format. Args: copy (bool): If ``False``, it shares data arrays as much as possible. Actually this option is ignored because all arrays in a matrix cannot be shared in coo to csr conversion. Returns: cupyx.scipy.sparse.csr_matrix: Converted matrix. rrrrT) rrrr csr_matrixrrrrKrcoo2csrr rs rDrzcoo_matrix.tocsrrrGc|td|jd|jdf}t|j|j|jff||S)a?Returns a transpose matrix. Args: axes: This option is not supported. copy (bool): If ``True``, a returned matrix shares no data. Otherwise, it shared data arrays as much as possible. Returns: cupyx.scipy.sparse.spmatrix: Transpose matrix. NzoSparse matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.rrru)rrr rrr)r>axesrrs rD transposezcoo_matrix.transposeso  GHH H 1 tz!}, Y48, -UGGG GrG)NNF)T)r)N)NN)F)NF)__name__ __module__ __qualname____doc__rrrrr<rFrRrbrergrkrnr}rKrr(rrrrGrDr r s:F252 : ; ;m3m3m3m3^ " " " ">0*0*0*0*d!!!2222(%(*&*&*&*&*&Xg)g)g)R : : : : 22GGGGGGrGr c,t|tS)zChecks if a given matrix is of COO format. Returns: bool: Returns if ``x`` is :class:`cupyx.scipy.sparse.coo_matrix`. )r*r )r@s rDisspmatrix_coor1s a $ $$rG)r1 scipy.sparser&r% ImportErrorr#rcupyx.scipy.sparserrrrr:rr r;r rrrGrDrs/  $$$$$$############333333$$$$$$''''''\G\G\G\G\G)\G\G\G~%%%%%s