o ְi@sfddlZddlZddlmZddZdddZedd d d Zd d ZdddZdddZ ddZ dS)N)_corecCslt|}|j|jkrtdt|}|d}|jdkr#dS|jdkr,td|j||dddS)a}Change elements of an array based on conditional and input values. This function uses the first N elements of `vals`, where N is the number of true values in `mask`. Args: arr (cupy.ndarray): Array to put data into. mask (array-like): Boolean mask array. Must have the same size as `a`. vals (array-like): Values to put into `a`. Only the first N elements are used, where N is the number of True values in `mask`. If `vals` is smaller than N, it will be repeated, and if elements of `a` are to be masked, this sequence must be non-empty. Examples -------- >>> arr = np.arange(6).reshape(2, 3) >>> np.place(arr, arr>2, [44, 55]) >>> arr array([[ 0, 1, 2], [44, 55, 44]]) .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.place` z$Mask and data must be the same size.rNz"Cannot insert from an empty array.wrapmode)cupyasarraysize ValueErrorravelnonzeroput)arrmaskvals mask_indicesrI/home/ubuntu/.local/lib/python3.10/site-packages/cupy/_indexing/insert.pyplaces     rrcCs|j|||ddS)aReplaces specified elements of an array with given values. Args: a (cupy.ndarray): Target array. ind (array-like): Target indices, interpreted as integers. v (array-like): Values to place in `a` at target indices. If `v` is shorter than `ind` it will be repeated as necessary. mode (str): How out-of-bounds indices will behave. Its value must be either `'raise'`, `'wrap'` or `'clip'`. Otherwise, :class:`TypeError` is raised. .. note:: Default `mode` is set to `'wrap'` to avoid unintended performance drop. If you need NumPy's behavior, please pass `mode='raise'` manually. .. seealso:: :func:`numpy.put` rN)r )aindvrrrrr 1sr z%Q mask, raw S values, uint64 len_valszT outz3 if (mask) out = (T) values[i % len_vals]; cupy_putmask_kernelcCst|tjs tdt|tjstdt|s#t|tjs#td|j|jks-td|tj }t|r>|||<dSt |j |j sPtd |j |j |j|jkr^||||<dS| }t||t||dS)a Changes elements of an array inplace, based on a conditional mask and input values. Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``. If `values` is not the same size as `a` and `mask` then it will repeat. Args: a (cupy.ndarray): Target array. mask (cupy.ndarray): Boolean mask array. It has to be the same shape as `a`. values (cupy.ndarray or scalar): Values to put into `a` where `mask` is True. If `values` is smaller than `a`, then it will be repeated. Examples -------- >>> x = cupy.arange(6).reshape(2, 3) >>> cupy.putmask(x, x>2, x**2) >>> x array([[ 0, 1, 2], [ 9, 16, 25]]) If `values` is smaller than `a` it is repeated: >>> x = cupy.arange(6) >>> cupy.putmask(x, x>2, cupy.array([-33, -44])) >>> x array([ 0, 1, 2, -44, -33, -44]) .. seealso:: :func:`numpy.putmask` z"`a` should be of type cupy.ndarrayz%`mask` should be of type cupy.ndarrayz'`values` should be of type cupy.ndarrayz#mask and data must be the same sizezACannot cast array data from {} to {} according to the rule 'safe'N) isinstancerndarray TypeErrorisscalarshaper astypenumpybool_can_castdtypeformatr _putmask_kernellen)rrvaluesrrrputmaskOs& #       r&FcCs|jdkr tdd}|jdkr$|jdd}|s#|jd|jd}ntt|jdks3tddt|jdd}||jd||<dS)a1Fills the main diagonal of the given array of any dimensionality. For an array `a` with ``a.ndim > 2``, the diagonal is the list of locations with indices ``a[i, i, ..., i]`` all identical. This function modifies the input array in-place, it does not return a value. Args: a (cupy.ndarray): The array, at least 2-D. val (scalar): The value to be written on the diagonal. Its type must be compatible with that of the array a. wrap (bool): If specified, the diagonal is "wrapped" after N columns. This affects only tall matrices. Examples -------- >>> a = cupy.zeros((3, 3), int) >>> cupy.fill_diagonal(a, 5) >>> a array([[5, 0, 0], [0, 5, 0], [0, 0, 5]]) .. seealso:: :func:`numpy.fill_diagonal` zarray must be at least 2-dNr/All dimensions of input must be of equal length) ndimr rralldiffcumprodsumflat)rvalrendsteprrr fill_diagonals  r4r'cCst|}|f|S)aIReturn the indices to access the main diagonal of an array. Returns a tuple of indices that can be used to access the main diagonal of an array with ``ndim >= 2`` dimensions and shape (n, n, ..., n). Args: n (int): The size, along each dimension of the arrays for which the indices are to be returned. ndim (int): The number of dimensions. default `2`. Examples -------- Create a set of indices to access the diagonal of a (4, 4) array: >>> di = cupy.diag_indices(4) >>> di (array([0, 1, 2, 3]), array([0, 1, 2, 3])) >>> a = cupy.arange(16).reshape(4, 4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> a[di] = 100 >>> a array([[100, 1, 2, 3], [ 4, 100, 6, 7], [ 8, 9, 100, 11], [ 12, 13, 14, 100]]) Create indices to manipulate a 3-D array: >>> d3 = cupy.diag_indices(2, 3) >>> d3 (array([0, 1]), array([0, 1]), array([0, 1])) And use it to set the diagonal of an array of zeros to 1: >>> a = cupy.zeros((2, 2, 2), dtype=int) >>> a[d3] = 1 >>> a array([[[1, 0], [0, 0]], [[0, 0], [0, 1]]]) .. seealso:: :func:`numpy.diag_indices` )rarange)nr+idxrrr diag_indicess 4 r8cCsVt|tjs td|jdkstdtt|jdks"tdt |jd|jS)z Return the indices to access the main diagonal of an n-dimensional array. See `diag_indices` for full details. Args: arr (cupy.ndarray): At least 2-D. .. seealso:: :func:`numpy.diag_indices_from` zArgument must be cupy.ndarrayr'z input array must be at least 2-drr)) rrrrr+r r,r-rr8)r rrrdiag_indices_froms  r9)r)F)r') rrrrr ElementwiseKernelr#r&r4r8r9rrrrs  * ? ) 8