ijdZddlmZddlZddlZddlmZmZmZm Z ddl m Z ddl m Z ddlmZddlmZdd lmZmZerdd lmZd d gZej0e5dd lmZedgz ZdddGddZe j8e j:Gdd ej<eeZy#1swYMxYw)z)PyTree integration with :mod:`functools`.) annotationsN) TYPE_CHECKINGAnyCallableClassVar)Self)registry) GetAttrEntry) tree_reduce)CustomTreeNodeT) PyTreeEntrypartialreduce) PlaceholderrcfeZdZUdZdZded<ded<ded<d ed <dd Zdd Zdd ZddZ ddZ y)_HashablePartialShimzzA shim object that delegates :meth:`__call__`, :meth:`__eq__`, and :meth:`__hash__` to a :func:`functools.partial` object.)argsfunckeywords partial_funczClassVar[tuple[str, ...]] __slots__Callable[..., Any]rztuple[Any, ...]rzdict[str, Any]rc||_yNr)selfrs e/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/optree/functools.py__init__z_HashablePartialShim.__init__8s /;c&|j|i|Srr)rrkwargss r__call__z_HashablePartialShim.__call__;s t  $1&11r crt|tr|j|jk(S|j|k(Sr) isinstancerr)rothers r__eq__z_HashablePartialShim.__eq__>s5 e1 2$$(:(:: :  E))r c,t|jSr)hashrrs r__hash__z_HashablePartialShim.__hash__CD%%&&r c,t|jSr)reprrr*s r__repr__z_HashablePartialShim.__repr__Fr,r N)rzfunctools.partialreturnNone)rrr"rr0r)r&objectr0bool)r0intr0str) __name__ __module__ __qualname____doc__r__annotations__rr#r'r+r/r rrr/s>E+WI(W  <2* ''r r) namespaceceZdZUdZdZded<ded<ded<d ed <eZd ed <dfd ZddZ ddZ e ddZ xZ S)raA version of :func:`functools.partial` that works in pytrees. Use it for partial function evaluation in a way that is compatible with transformations, e.g., ``partial(func, *args, **kwargs)``. (You need to explicitly opt-in to this behavior because we did not want to give :func:`functools.partial` different semantics than normal function closures.) For example, here is a basic usage of :class:`partial` in a manner similar to :func:`functools.partial`: >>> import operator >>> import torch >>> add_one = partial(operator.add, torch.ones(())) >>> add_one(torch.tensor([[1, 2], [3, 4]])) tensor([[2., 3.], [4., 5.]]) Pytree compatibility means that the resulting partial function can be passed as an argument within tree-map functions, which is not possible with a standard :func:`functools.partial` function: >>> def call_func_on_cuda(f, *args, **kwargs): ... f, args, kwargs = tree_map(lambda t: t.cuda(), (f, args, kwargs)) ... return f(*args, **kwargs) ... >>> # doctest: +SKIP >>> tree_map(lambda t: t.cuda(), add_one) optree.functools.partial(, tensor(1., device='cuda:0')) >>> call_func_on_cuda(add_one, torch.tensor([[1, 2], [3, 4]])) tensor([[2., 3.], [4., 5.]], device='cuda:0') Passing zero arguments to :class:`partial` effectively wraps the original function, making it a valid argument in tree-map functions: >>> # doctest: +SKIP >>> call_func_on_cuda(partial(torch.add), torch.tensor(1), torch.tensor(2)) tensor(3, device='cuda:0') Had we passed :func:`operator.add` to ``call_func_on_cuda`` directly, it would have resulted in a :class:`TypeError` or :class:`AttributeError`. On Python 3.14+, :data:`functools.Placeholder` can be used to reserve positional argument slots: >>> from functools import Placeholder # doctest: +SKIP >>> square = partial(pow, Placeholder, 2) # doctest: +SKIP >>> square(5) 25 :data:`~functools.Placeholder` objects are treated as leaves in the pytree and their identity is preserved through flatten/unflatten round-trips. r<zClassVar[tuple[()]]rrrz tuple[T, ...]rz dict[str, T]rzClassVar[type[PyTreeEntry]]TREE_PATH_ENTRY_TYPEc2t|tjri|}t|}t |drJdt |||g|i|}|j|_|j|_|j|_ |St |||g|i|S)z'Create a new :class:`partial` instance.rz3shimmed function should not have a `func` attribute) r% functoolsrrhasattrsuper__new__rrr)clsrrr original_funcout __class__s rrDzpartial.__new__s dI-- . M' 6DtV, c.c c,'/#t?d?h?C%**DI%**DI)22DMJwsD<4<8<z#partial.__repr__..s/DG/sc30K|]\}}|d|yw)=Nr<)rKkvs rrMz#partial.__repr__..sEVaqc1%LEs.(z, )) r.rextendrritemsrHr8r9join)rrs rr/zpartial.__repr__sTYY  /TYY// Et}}/B/B/DEE..++,Adnn.I.I-J!DIIVZOK\\]^^r cL|j|jf|jdfS)zAFlatten the :class:`partial` instance into children and metadata.)rr)rrrr*s r__tree_flatten__zpartial.__tree_flatten__s# 4==)4996JJJr c$|\}}||g|i|S)zEUnflatten the children and metadata into a :class:`partial` instance.r<)rEmetadatachildrenrrs r__tree_unflatten__zpartial.__tree_unflatten__s""h8/d/h//r )rrrr rr r0rr5)r0zNtuple[tuple[tuple[T, ...], dict[str, T]], Callable[..., Any], tuple[str, str]])r[rr\z"tuple[tuple[T, ...], dict[str, T]]r0r)r7r8r9r:rr;r r?rDr/rY classmethodr] __classcell__)rHs@rrrKs 4l&(I"'  8D5D=$_ K  K0$050  00r )r: __future__r contextlibrAtypingrrrrtyping_extensionsroptreer optree.accessorsr optree.opsr r optree.typingr r r__all__suppress ImportErrorrrregister_pytree_node_class__GLOBAL_NAMESPACErr<r rrms0"99"),+,   Z%% G ''8%$$x/J/JKo0 1o0Lo0Is  B44B=