o X۷i)@svddlmZddlZejdkrddlmZn ddlmmZddl Z ddl m Z ddZ dd Z d d Zd d ZdS)) annotationsN2) index_tricks)internalcOs|j}t||}t||d}t|jdd}dd|D}zt|}Wn t y2t dw|||g|Ri|} t | } t |jdd| j| j } | | |<|D]} ||| g|Ri|} t | | | <qZt| jD] } t| d|} qv| S)aApply a function to 1-D slices along the given axis. Args: func1d (function (M,) -> (Nj...)): This function should accept 1-D arrays. It is applied to 1-D slices of ``arr`` along the specified axis. It must return a 1-D ``cupy.ndarray``. axis (integer): Axis along which ``arr`` is sliced. arr (cupy.ndarray (Ni..., M, Nk...)): Input array. args: Additional arguments for ``func1d``. kwargs: Additional keyword arguments for ``func1d``. Returns: cupy.ndarray: The output array. The shape of ``out`` is identical to the shape of ``arr``, except along the ``axis`` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of ``func1d``. So if ``func1d`` returns a scalar ``out`` will have one fewer dimensions than ``arr``. .. seealso:: :func:`numpy.apply_along_axis` Ncss|]}|tfVqdS)N)Ellipsis).0indr J/home/ubuntu/vllm_env/lib/python3.10/site-packages/cupy/lib/_shape_base.py )sz#apply_along_axis..z;Cannot apply_along_axis when any iteration dimensions are 0)ndimr_normalize_axis_indexcupymoveaxisrndindexshapenext StopIteration ValueErrorasarrayemptydtyperange)func1daxisarrargskwargsr inarr_viewindsind0resbuffr outir r r apply_along_axis s,    r&cCst|}|j}t|jdkr|f}|D](}t||}|||}|j|jkr+|}qt||}|j|jkr:|}qtd|S)aa Apply a function repeatedly over multiple axes. `func` is called as `res = func(a, axis)`, where `axis` is the first element of `axes`. The result `res` of the function call must have either the same dimensions as `a` or one less dimension. If `res` has one less dimension than `a`, a dimension is inserted before `axis`. The call to `func` is then repeated for each axis in `axes`, with `res` as the first argument. Parameters ---------- func : function This function must take two arguments, `func(a, axis)`. a : array_like Input array. axes : array_like Axes over which `func` is applied; the elements must be integers. Returns ------- apply_over_axis : ndarray The output array. The number of dimensions is the same as `a`, but the shape can be different. This depends on whether `func` changes the shape of its output with respect to its input. See Also -------- apply_along_axis : Apply a function to 1-D slices of an array along the given axis. Notes ----- This function is equivalent to tuple axis arguments to reorderable ufuncs with keepdims=True. Tuple axis arguments to ufuncs have been available. Examples -------- >>> a = np.arange(24).reshape(2,3,4) >>> a array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) Sum over axes 0 and 2. The result has same number of dimensions as the original array: >>> np.apply_over_axes(np.sum, a, [0,2]) array([[[ 60], [ 92], [124]]]) Tuple axis arguments to ufuncs are equivalent: >>> np.sum(a, axis=(0,2), keepdims=True) array([[[ 60], [ 92], [124]]]) rz7function is not returning an array of the correct shape) rrr numpyarrayrr expand_dimsr)funcaaxesvalNrr"r r r apply_over_axesHs @     r/c Cst|jtjs tdt||jkrtdd|j}tt |dgtt |d|j}g}t ||D])\}}|durD| |q6|d|d||dd}| t | |q6t|S)Nz"`indices` must be an integer arrayz;`indices` and `arr` must have the same number of dimensions)r0)r)r issubdtyperinteger IndexErrorlenr rlistrzipappendarangereshapetuple) arr_shapeindicesr shape_ones dest_dims fancy_indexdimn ind_shaper r r _make_along_axis_idxs"   rCcCsT|dur|jdkr tdt|||dSt||j}|j}||t|||<dS)av Put values into the destination array by matching 1d index and data slices. This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to place values into the latter. These slices can be different lengths. Functions returning an index along an axis, like `argsort` and `argpartition`, produce suitable indices for this function. Args: arr : cupy.ndarray (Ni..., M, Nk...) Destination array. indices : cupy.ndarray (Ni..., J, Nk...) Indices to change along each 1d slice of `arr`. This must match the dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast against `arr`. values : array_like (Ni..., J, Nk...) values to insert at those indices. Its shape and dimension are broadcast to match that of `indices`. axis : int The axis to take 1d slices along. If axis is None, the destination array is treated as if a flattened 1d view had been created of it. .. seealso:: :func:`numpy.put_along_axis` Nr0z+Tuple setitem isn't supported for flatiter.)r NotImplementedErrorrputrrrrC)rr<valuesrr;r r r put_along_axiss rG) __future__rr' __version__ numpy.librnumpy.lib._index_tricks_impllib_index_tricks_implr cupy._corerr&r/rCrGr r r r s   ;T