o װi@s.ddlZddlZddlZddZdddZdS)NcCs<t|jtjrt|d}n|d}t|S)N)cupy issubdtypedtypecomplexfloatingabspowersumsqrt)xsqnormr S/home/ubuntu/.local/lib/python3.10/site-packages/cupyx/scipy/sparse/linalg/_norm.py_sparse_frobenius_norms rc Cstjj|s td|dur|dvrt|S|}|dur"d}n$t|tsFd}zt |}Wn ty:t|w||krCt||f}d}t |dkr|\}}| |kr]|krknn | |krj|kstnt d||j f||||krt d|dkrt |d krt |d krt|j|d S|tjkrt|j|d S|d krt|j|d S|tj krt|j|d S|d vrt|St dt |d krx|\}| |kr|ksnt d||j f|tjkrt|j|d S|tj krt|j|d S|dkr.|dktjj|d tjS|d kr=t|j|d S|dvrRtt|dj|d Sz|d Wn tydt dwtt||j|d d |St d)aNorm of a cupy.scipy.spmatrix This function is able to return one of seven different sparse matrix norms, depending on the value of the ``ord`` parameter. Args: x (sparse matrix) : Input sparse matrix. ord (non-zero int, inf, -inf, 'fro', optional) : Order of the norm (see table under ``Notes``). inf means numpy's `inf` object. axis : (int, 2-tuple of ints, None, optional): If `axis` is an integer, it specifies the axis of `x` along which to compute the vector norms. If `axis` is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed. If `axis` is None then either a vector norm (when `x` is 1-D) or a matrix norm (when `x` is 2-D) is returned. Returns: ndarray : 0-D or 1-D array or norm(s). .. seealso:: :func:`scipy.sparse.linalg.norm` z)input is not sparse. use cupy.linalg.normN)Nfrof)rz6'axis' must be None, an integer or a tuple of integersrz*Invalid axis %r for an array with shape %rzDuplicate axes given.r)axis)Nrrz Invalid norm order for matrices.r)rNzInvalid norm order for vectors.z&Improper number of dimensions to norm.)cupyxscipysparseissparse TypeErrorrtocsr isinstancetupleintlen ValueErrorshapeNotImplementedErrorrr maxnumpyinfmintoarrayravelastypefloat32int_rr r) r ordrmsgint_axisndrow_axiscol_axisar r rnorms    2        &r3)NN)r$rcupyx.scipy.sparserrr3r r r rs