Vpf:6ddlmZddlmZmZmZddlZddlZddlm Z ddl Z ddl m Z ddl m Z ddlmZddlmZdd lmZmZd Zd eZd ed ZdeZdeZeedfZ e Z!d5dZ" d6d7d Z# d6d8d&Z$ d6d9d*Z%d:d2Z&e'dd3d4Z(dS);) annotations)Callable CollectionSequenceN)Any)api)config)lax) lax_numpy)safe_mapsafe_zipz\w+z(?:{0:}(?:,{0:})*)?z\(z\)z{0:}(?:,{0:})*z ^{0:}->{0:}$. signaturestrreturn%tuple[list[CoreDims], list[CoreDims]]ctjt|std|d|dD\}}||fS)aParse string signatures for a generalized universal function. Args: signature: generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)`` for ``jnp.matmul``. Returns: Input and output core dimensions parsed from the signature. znot a valid gufunc signature: c3^K|](}dtjt|DV)dS)c\g|])}ttjt|*S)tuplerefindall_DIMENSION_NAME.0args X/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/_src/numpy/vectorize.py z5_parse_gufunc_signature...8s<???"*_c::;;???N)rr _ARGUMENT)rarg_lists r z*_parse_gufunc_signature..8sY::?? jH==???::::::rz->)rmatch _SIGNATURE ValueErrorsplit)rargsretvalss r_parse_gufunc_signaturer))so *i ( (6 444 6 66::#,??4#8#8:::-$ wr dim_sizesdict[str, int]shapetuple[int, ...] core_dimsCoreDims error_contextis_inputboolc t|}|r,t||krtd|d|d|n+t||krtd|d|d||r || dnd}t||D]<\}}||vr|||<|||kr!td|d |d ||d|=dS) aeIncrementally check and update core dimension sizes for a single argument. Args: dim_sizes: sizes of existing core dimensions. Will be updated in-place. shape: shape of this argument. core_dims: core dimensions for this argument. error_context: string context for error messages. is_input: are we parsing input or output arguments? zinput with shape z9 does not have enough dimensions for all core dimensions  z output shape z does not match core dimensions Nrz%inconsistent size for core dimension z: z vs )lenr%zip) r+r-r/r1r2 num_core_dims core_shapedimsizes r_update_dim_sizesr<>s9 i..- / 5zzM!! J %yyy-- A B BB"  5zz]"" J UUIII}} . / //*3:um^__%%*y*--88ic4 )inn 3   J SS$$$ #  7 8 88 88rr'tuple[NDArray, ...]input_core_dimslist[CoreDims]&tuple[tuple[int, ...], dict[str, int]]ct|t|kr2tdt|dt|d|g}i}t||D]W\}}t||j||d|jt|z }||jd|Xtj|}||fS)aParse broadcast and core dimensions for vectorize with a signature. Args: args: tuple of input arguments to examine. input_core_dims: list of core dimensions corresponding to each input. error_context: string context for error messages. Returns: broadcast_shape: common shape to broadcast all non-core dimensions to. dim_sizes: common sizes for named core dimensions. z/wrong number of positional arguments: expected , got r5Tr2N) r6 TypeErrorr7r<r-ndimappendr broadcast_shapes) r'r>r1shapesr+rr/rEbroadcast_shapes r_parse_input_dimensionsrJds  YY#o&&&& )     TMM ; < << & )D/22$$nc9iI}#%%%% 8c)nn $D MM#)ETE"####(&1/ ) ##rfuncrexpected_output_core_dimscfd}|S)z6Check that output core dimensions match the signature.cR |}ttjt|tr|n|g} dgt |z}n }t |dkr8t|ts#t d|t |t |kr2t dt |dt |dt}t||D]\}}t|||d|S) NrzGoutput must be a tuple when multiple outputs are expected, got: {!r} {}z+wrong number of output arguments: expected rBr5FrC) mapjnpr- isinstancerr6rDformatdictr7r<) r'out out_shapesoutput_core_dimssizesr-r/r+r1rLrKs rwrappedz#_check_output_dims..wrappedsT $+CSYz#u'='= HC5IIJ (J/2    " ":c5+A+A " "F3 6688 8 ZC 011 1 1i#$$$$c*oooo}} FGG G OOE ,<==((yui!&((((( Jrr)rKr+rLr1rYs```` r_check_output_dimsrZs5. .rCallable[..., Any]excludedCollection[int | str] Sequence[Any]kwargsdict[str, Any]8tuple[Callable[..., Any], Sequence[Any], dict[str, Any]]c2s|fSfdtD}fd|D}fdtdDDfd|Dfd}|||fS)zAPartially apply positional arguments in `excluded` to a function.c"g|] \}}|v | Srr)rirr\s rrz#_apply_excluded..s'III&!Sq7H7H#7H7H7Hrc$i|] \}}|v || Srrrkeyvalr\s r z#_apply_excluded..s)SSScs(?R?RC?R?R?RrcJg|]}|tk||f Sr)r6)rrdr's rrz#_apply_excluded..s7###!D MMT!W!MMrc3DK|]}t|t|VdSNrRintres rr"z"_apply_excluded..s3-X-XAZPQSVEWEW-Xa-X-X-X-X-X-Xrc$i|] \}}|v || Srrrfs rriz#_apply_excluded..s$NNNScXoo3ooorcrt|}D]\}}||||i|Srl)listinsert)r'r_rdrrK static_args static_kwargss rnew_funcz!_apply_excluded..new_funcsR ::D3 kk!S 4 1 1= 1 11r) enumerateitemssorted) rKr\r'r_ dynamic_argsdynamic_kwargsrwrurvs ``` @@r_apply_excludedr}s  v IIIIIdOOIII,SSSSV\\^^SSS.####v-X-X-X-X-X'X'X###+NNNNFLLNNNNN-2222222 < //r)r\rctdDr"tdtdDrtddt jfd}|S)a Define a vectorized function with broadcasting. :func:`vectorize` is a convenience wrapper for defining vectorized functions with broadcasting, in the style of NumPy's `generalized universal functions `_. It allows for defining functions that are automatically repeated across any leading dimensions, without the implementation of the function needing to be concerned about how to handle higher dimensional inputs. :func:`jax.numpy.vectorize` has the same interface as :class:`numpy.vectorize`, but it is syntactic sugar for an auto-batching transformation (:func:`vmap`) rather than a Python loop. This should be considerably more efficient, but the implementation must be written in terms of functions that act on JAX arrays. Args: pyfunc: function to vectorize. excluded: optional set of integers representing positional arguments for which the function will not be vectorized. These will be passed directly to ``pyfunc`` unmodified. signature: optional generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for vectorized matrix-vector multiplication. If provided, ``pyfunc`` will be called with (and expected to return) arrays with shapes given by the size of corresponding core dimensions. By default, pyfunc is assumed to take scalars arrays as input and output. Returns: Vectorized version of the given function. Here are a few examples of how one could write vectorized linear algebra routines using :func:`vectorize`: >>> from functools import partial >>> @partial(jnp.vectorize, signature='(k),(k)->(k)') ... def cross_product(a, b): ... assert a.shape == b.shape and a.ndim == b.ndim == 1 ... return jnp.array([a[1] * b[2] - a[2] * b[1], ... a[2] * b[0] - a[0] * b[2], ... a[0] * b[1] - a[1] * b[0]]) >>> @partial(jnp.vectorize, signature='(n,m),(m)->(n)') ... def matrix_vector_product(matrix, vector): ... assert matrix.ndim == 2 and matrix.shape[1:] == vector.shape ... return matrix @ vector These functions are only written to handle 1D or 2D arrays (the ``assert`` statements will never be violated), but with vectorize they support arbitrary dimensional inputs with NumPy style broadcasting, e.g., >>> cross_product(jnp.ones(3), jnp.ones(3)).shape (3,) >>> cross_product(jnp.ones((2, 3)), jnp.ones(3)).shape (2, 3) >>> cross_product(jnp.ones((1, 2, 3)), jnp.ones((2, 1, 3))).shape (2, 2, 3) >>> matrix_vector_product(jnp.ones(3), jnp.ones(3)) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ValueError: input with shape (3,) does not have enough dimensions for all core dimensions ('n', 'k') on vectorized function with excluded=frozenset() and signature='(n,k),(k)->(k)' >>> matrix_vector_product(jnp.ones((2, 3)), jnp.ones(3)).shape (2,) >>> matrix_vector_product(jnp.ones((2, 3)), jnp.ones((4, 3))).shape (4, 2) Note that this has different semantics than `jnp.matmul`: >>> jnp.matmul(jnp.ones((2, 3)), jnp.ones((4, 3))) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): TypeError: dot_general requires contracting dimensions to have the same shape, got [3] and [4]. c3PK|]!}t|ttf V"dSrl)rRrrn)rexcludes rr"zvectorize..s3EEZ#s , , ,EEEEEErzSjax.numpy.vectorize can only exclude integer or string arguments, but excluded={!r}c3LK|]}t|to|dkV dS)rNrmros rr"zvectorize..s588!As   %A888888rz excluded=z contains negative numbersc(d}t||\}}}t\}ndgt|zd}dt |Dt rat fdDrt ddt||i\}}}fdt Dtttj |}t||\}}t||||}tjjdkrd t!|D} tt#| d krad d |Dd d} tjjdkrt%j| n$tjjdkrt | g} g} t!|D]\} }| jd| jt|z }t|t|z }|dz|z}| |dddtdt |D}tj| |}| ||}gt t!| D]u\}}td|D}t1d|Dr)t|d z |z `t3j||}v|| }s|St7|trtfd|DStj|S)Nzz-vectorize..wrapped..s@@@vq#CKKKKrc30K|]}|dkVdS)rNr)rrdr>s rr"z-vectorize..wrapped..s- 9 9!_Q 2 % 9 9 9 9 9 9rzCannot pass None at locations z with signature=c"g|] \}}|v | Srr)rrdr: none_argss rrz.vectorize..wrapped..s(\\\CR[I[I[I[I[I[rallowcXg|]'\}}|jdk|jt|z (S)r)rEr6)rrr/s rrz.vectorize..wrapped..(s<!!!!3 (a--x#i..(--rrOzoperands with shapes cg|] }|j Sr)r-rs rrz.vectorize..wrapped..,s'B'B'Bc 'B'B'BrzB require rank promotion for jnp.vectorize function with signature z. Set the jax_numpy_rank_promotion config option to 'allow' to disable this message; for more information, see https://jax.readthedocs.io/en/latest/rank_promotion_warning.html.warnraise)rOc3,K|]\}}|dk |VdS)rONr)rrdr;s rr"z-vectorize..wrapped..Hs+UUGAt4ST99a9999UUraxisc3*K|]}|dkrdndVdS)rONrr)rr;s rr"z-vectorize..wrapped..Os.FF4daiiddQFFFFFFrc3K|]}|duV dSrlr)rrs rr"z-vectorize..wrapped..Ps& . .dTT\ . . . . . .rc3DK|]}tj|VdS)rN)rQ expand_dims)rrdims_to_expands rr"z-vectorize..wrapped..Ys2KKq3?1>:::KKKKKKr)rSr}r)r6rxanyr%rrPrQasarrayrJrZr numpy_rank_promotionvaluer7setwarningsrr-rErFsqueezeallrvmaprRr)r'r_r1 excluded_funcrW_rIr+ checked_funcranksmsg squeezed_argsrev_filled_shapesrr/ noncore_shapepad_ndim filled_shapesqueeze_indices squeezed_argvectorized_funcnegdim axis_sizesin_axesresultrr>rr\pyfuncrs @@@rrYzvectorize..wrapped s&&,fXy&A&A"1&(D&"Q"QM4*A)*L*L'o''s4yy(o@@4@@@I 9~~] 9 9 9 9y 9 9 9 9 9YW)WW9WWXXX.}irRRmT1\\\\9_+E+E\\\o S[$'' ( (D!8 o}".".OY&y"2MCCL"(G33!!%(%?%?!!!e SZZ1  T'B'BT'B'B'BTTGPTTT  & , 6 6 -      ( .' 9 93 MdO44 ) )Yi :C NN!: :;m_%%M(:(::h_}4l|DDbD1222UUy/G/GUUUUUo[?;;;l<(((("ON'->(?@@== FF:FFFFFg . .g . . . . .=c/22Q6?@@@@(?G<< _m ,F : m FE " ": KKKKFKKK K KK _V. 9 9 99r)rrDrSr% functoolswraps)rr\rrYs``` r vectorizersR EEHEEEEE: ((.x(8(8 : ::88x88888I GGGG H HH ?6O:O:O:O:O:O:O:b .r)rrrr)r*) r+r,r-r.r/r0r1rr2r3)r'r=r>r?r1rrr@) rKrr+r,rLr?r1rrr) rKr[r\r]r'r^r_r`rra)) __future__rcollections.abcrrrrrtypingrrjax._srcrr jaxr jax._src.numpyr rQ jax._src.utilr rPr r7rrS_CORE_DIMENSION_LISTr _ARGUMENT_LISTr$rrr0NDArrayr)r<rJrZr} frozensetrrrrrs#""""":::::::::: ++++++::::::::,33ODD *& * * * !((33  " "> 2 2  c? 2 #8#8#8#8#8R$$$$$H B0000.#,)++aaaaaaar