&Vfl)UdZddlmZddlZddlZddlmZddlmZm Z ddl m Z ddl m ZddlmZmZddlmZdd lmZdd lmZmZdd lmZmZdd lmZmZgd ZedeZ de!d<edeZ"de!d<GddZ# ddl$m#Z#n #e%$rYnwxYw d8dddd9d Z&e&Z'd:d'Z(d;d+Z)dd7Z,dS)?zIntegration with JAX.) annotationsN) FunctionType)AnyCallable) TypeAlias)Arraylax)dtypes) ArrayLike) tree_flattentree_unflatten) PyTreeSpec PyTreeTypeVar)safe_ziptotal_order_sorted) ArrayLikeTree ArrayTree tree_ravelrrrcReZdZUdZded<ded<ded<dd ZddZddZddZdS)HashablePartialz1A hashable version of :class:`functools.partial`.rfuncztuple[Any, ...]argszdict[str, Any]kwargsFunctionType | HashablePartialrreturnNonecHt|std|dt|tr-|j|_|j|z|_i|j||_dSt|tr||_||_||_dStd|d)z.Construct a :class:`HashablePartial` instance.zExpected a callable, got .zExpected a function, got N)callable TypeError isinstancerrrrr)selfrrrs S/var/www/html/software/conda/lib/python3.11/site-packages/optree/integration/jax.py__init__zHashablePartial.__init__Cs~~ CAAAABB B dO , , C DI D(DI3T[3F3DKKK l + + CDIDI DKKKAAAABB Botherobjectboolct|tuo9|jj|jjko|j|jko|j|jkSN)typerr__code__rr)r"r&s r#__eq__zHashablePartial.__eq__SsP KK? * , "ej&99 , UZ' , u|+  r%intc t|jj|jt t |jdfS)Nc|dS)Nr)kvs r#z*HashablePartial.__hash__..`s RPQUr%)key)hashrr,rtuplerritems)r"s r#__hash__zHashablePartial.__hash__[sQ " ():):)<)|jg|j|Ri|j|Sr*)rrr)r"rrs r#__call__zHashablePartial.__call__ds0tyD$)DdDDDdkDVDDDr%N)rrrrrrrr)r&r'rr()rr.)rrrrrr) __name__ __module__ __qualname____doc____annotations__r$r-r8r:r1r%r#rr<s;;CCCC         EEEEEEr%r)rF) none_is_leaf namespacetreeis_leafCallable[[Any], bool] | NonerAr(rBstrr*tuple[Array, Callable[[Array], ArrayTree]]ct||||\}}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 {'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) {'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. )rDrArB)r _ravel_leavesr _tree_unravel)rCrDrArBleavestreespecflat unravel_flats r#rrosUp$ ! 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