0VhH*UdZddlmZddlZddlZddlZddlmZddlm Z ddl m Z m Z ddl mZddlmZddlmZmZdd lmZdd lmZdd lmZmZdd lmZmZdd lm Z m!Z!gdZ"edeZ#de$d<edeZ%de$d<GddZ&ejNe(5ddl)m&Z&ddd dddd ddZ*e*Z+ d dZ, d!dZ-d"dZ. d#dZ/ d$dZ0y#1swYSxYw)%zIntegration with JAX.) annotationsN) itemgetter) FunctionType)AnyCallable) TypeAlias)Arraylax)dtypes) ArrayLike) tree_flattentree_unflatten) PyTreeSpec PyTreeTypeVar)safe_ziptotal_order_sorted) ArrayLikeTree ArrayTree tree_ravelrrrcPeZdZUdZded<ded<ded<d dZdd Zdd Zdd Zy )HashablePartialz1A hashable version of :class:`functools.partial`.rfuncztuple[Any, ...]argszdict[str, Any]kwargsc6t|std|dt|tr;|j|_|j |z|_i|j ||_yt|tr||_||_||_ytd|d)z.Construct a :class:`HashablePartial` instance.zExpected a callable, got .zExpected a function, got N)callable TypeError isinstancerrrrr)selfrrrs G/home/dcms/DCMS/lib/python3.12/site-packages/optree/integrations/jax.py__init__zHashablePartial.__init__Gs~7xqAB B dO , DI D(DI3T[[3F3DK l +DIDI DK7xqAB Bct|tuxr`|jj|jjk(xr1|j|j f|j|j fk(SN)typerr__code__rr)r others r!__eq__zHashablePartial.__eq__Ws] K? * G ""ejj&9&99 GDKK(UZZ,FF r#c t|jj|jt t |j jtdfS)Nr)key) hashrr'rtuplerritemsr)r s r!__hash__zHashablePartial.__hash__^sI "" ():):)<*Q-PQ   r#cdi|j|}|jg|j|i|Sr%)rrr)r rrs r!__call__zHashablePartial.__call__gs7*DKK*6*tyy5$))5d5f55r#N)rzFunctionType | HashablePartialrrrrreturnNone)r(objectr2bool)r2int)rrrrr2r) __name__ __module__ __qualname____doc____annotations__r"r)r/r1r#r!rr@s,;   C   6r#r)rF) none_is_leaf namespacecht||||\}}t|\}}|tt||fS)a/ Ravel (flatten) a pytree of arrays down to a 1D array. >>> tree = { ... 'layer1': { ... 'weight': jnp.arange(0, 6, dtype=jnp.float32).reshape((2, 3)), ... 'bias': jnp.arange(6, 8, dtype=jnp.float32).reshape((2,)), ... }, ... 'layer2': { ... 'weight': jnp.arange(8, 10, dtype=jnp.float32).reshape((1, 2)), ... 'bias': jnp.arange(10, 11, dtype=jnp.float32).reshape((1,)), ... }, ... } >>> tree # doctest: +IGNORE_WHITESPACE { 'layer1': { 'weight': Array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), 'bias': Array([6., 7.], dtype=float32) }, 'layer2': { 'weight': Array([[8., 9.]], dtype=float32), 'bias': Array([10.], dtype=float32) } } >>> flat, unravel_func = tree_ravel(tree) >>> flat Array([ 6., 7., 0., 1., 2., 3., 4., 5., 10., 8., 9.], dtype=float32) >>> unravel_func(flat) # doctest: +IGNORE_WHITESPACE { 'layer1': { 'weight': Array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), 'bias': Array([6., 7.], dtype=float32) }, 'layer2': { 'weight': Array([[8., 9.]], dtype=float32), 'bias': Array([10.], dtype=float32) } } Args: tree (pytree): a pytree of arrays and scalars to ravel. is_leaf (callable, optional): An optionally specified function that will be called at each flattening step. It should return a boolean, with :data:`True` stopping the traversal and the whole subtree being treated as a leaf, and :data:`False` indicating the flattening should traverse the current object. none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the treespec rather than in the leaves list and :data:`None` will be remain in the result pytree. (default: :data:`False`) namespace (str, optional): The registry namespace used for custom pytree node types. (default: :const:`''`, i.e., the global namespace) Returns: A pair ``(array, unravel_func)`` where the first element is a 1D array representing the flattened and concatenated leaf values, with ``dtype`` determined by promoting the ``dtype``\s of leaf values, and the second element is a callable for unflattening a 1D array of the same length back to a pytree of the same structure as the input ``tree``. If the input pytree is empty (i.e. has no leaves) then as a convention a 1D empty array of the default dtype is returned in the first component of the output. )is_leafr>r?)r _ravel_leavesr _tree_unravel)treerAr>r?leavestreespecflat unravel_flats r!rrqsEJ$ ! FH 'v.D, ,G GGr#c&t|||Sr%)r)rFrHrGs r!rCrCs (L$6 77r#c |stjdtfStd|D}t j |td|D}td|D}tt j|}tfd|DrItj|Dcgc]}tj|c}}|tt||fStj|Dcgc]+}tjtj|-c}}|tt|||fScc}wcc}w)Nrc3FK|]}tj|ywr%)r dtype.0leafs r! z _ravel_leaves..s>t T*>!c3FK|]}tj|ywr%)jnpsizerMs r!rPz _ravel_leaves..s4T#((4.4rQc3FK|]}tj|ywr%)rSshaperMs r!rPz _ravel_leaves..s6t399T?6rQc3(K|] }|k( ywr%r<)rNdtto_dtypes r!rPz _ravel_leaves..s 0b2> 0s)rSzeros_unravel_emptyr-r result_type itertools accumulateall concatenateravelr_unravel_leaves_single_dtyper convert_element_type_unravel_leaves)rE from_dtypessizesshapesindicesrOraveledrYs @r!rBrBs  ! n-->v>>K!!;/H 4V4 4E 6v6 6FI((/0G 0K 00//v"Ft399T?"FG  8'6 J  ooIOP3++D(; <PG &+xP #G Qs ,E50Ectj|dk7r%tdddtj|dgS)N)r0The unravel function expected an array of shape , got shape r)rSrV ValueError)rGs r!r[r[sG yy$>tfLQTQZQZ[_Q`Paab c   Ir#c&tj||dfk7r)td|dfdtj|dtj||dd}t ||Dcgc]\}}|j |c}}Scc}}w)Nrkrlr)rSrVrmsplitrreshape)rhrgrGchunkschunkrVs r!rbrbs  yy72;.(> ~>NO4) ,  YYtWSb\ *F5=ff5M N\UEEMM% NN Ns.B c .tj||dfk7r)td|dfdtj|dtj|}||k7rtd|d|dtj ||dd}t j5t jdt|||D cgc]+\}}} tj|j|| -c} }}cdddScc} }}w#1swYyxYw)Nrorkrlrz0The unravel function expected an array of dtype z , got dtype ignore) rSrVrmr rLrpwarningscatch_warnings simplefilterrr rcrq) rhrgrerYrG array_dtyperrrsrVrLs r!rdrd s yy72;.(> ~>NO4) ,  ,,t$Kh>xj U`Taab c  YYtWSb\ *F  " h'(0 'L  #ue  $ $U]]5%95 A      s'D 0D 6D D  Dr%) rAzCallable[[Any], bool] | NonerDrr>r5r?strr2z*tuple[Array, Callable[[Array], ArrayTree]])rFrrHz"Callable[[Array], list[ArrayLike]]rGr r2r)rElist[ArrayLike]r2z0tuple[Array, Callable[[Array], list[ArrayLike]]])rGr r2r{)rhtuple[int, ...]rgtuple[tuple[int, ...], ...]rGr r2 list[Array]) rhr|rgr}reztuple[jnp.dtype, ...]rYz jnp.dtyperGr r2r~)1r: __future__r contextlibr]rvoperatorrtypesrtypingrrtyping_extensionsr jax.numpynumpyrSjaxr r jax._srcr jax.typingr optree.opsr r optree.typingrr optree.utilsrr__all__rr;rrsuppress ImportError jax._src.utilr ravel_pytreerCrBr[rbrdr<r#r!rs@# ' 335 7))D yD$[%8 98)6)6XZ%.-.-1LH  LH*LH LH  LH  LH0LH^ 8848 8  8! !!H O  O ' O  O  O   ' '      {..s C77D