o 2hH*@sUdZddlmZddlZddlZddlZddlmZddlm Z ddl m Z m Z ddl mZddlmZddlmZmZdd lmZdd lmZdd lmZmZdd lmZmZdd lm Z m!Z!gdZ"edeZ#de$d<edeZ%de$d<GdddZ&e'e(ddl)m&Z&Wdn1swY d?dddd@d!d"Z*e*Z+dAd)d*Z,dBd.d/Z-dCd0d1Z.dDd7d8Z/dEd=d>Z0dS)FzIntegration with JAX.) annotationsN) itemgetter) FunctionType)AnyCallable) TypeAlias)Arraylax)dtypes) ArrayLike) tree_flattentree_unflatten) PyTreeSpec PyTreeTypeVar)safe_ziptotal_order_sorted) ArrayLikeTree ArrayTree tree_ravelrrrc@sReZdZUdZded<ded<ded<dd d ZdddZdddZdddZdS)HashablePartialz1A hashable version of :class:`functools.partial`.rfuncztuple[Any, ...]argszdict[str, Any]kwargsFunctionType | HashablePartialrreturnNonecOszt|s td|dt|tr%|j|_|j||_i|j||_dSt|tr5||_||_||_dStd|d)z.Construct a :class:`HashablePartial` instance.zExpected a callable, got .zExpected a function, got N)callable TypeError isinstancerrrrr)selfrrrr!R/var/www/html/chatgem/venv/lib/python3.10/site-packages/optree/integrations/jax.py__init__Gs    zHashablePartial.__init__otherobjectboolcCs4t|tuo|jj|jjko|j|jf|j|jfkSN)typerr__code__rr)r r$r!r!r"__eq__Ws zHashablePartial.__eq__intcCs*t|jj|jtt|jtddfS)Nr)key) hashrr)rtuplerritemsr)r r!r!r"__hash__^s zHashablePartial.__hash__cOs*i|j|}|jg|j|Ri|Sr')rrr)r rrr!r!r"__call__gszHashablePartial.__call__N)rrrrrrrr)r$r%rr&)rr+)rrrrrr) __name__ __module__ __qualname____doc____annotations__r#r*r0r1r!r!r!r"r@s     r)rF) none_is_leaf namespaceis_leafCallable[[Any], bool] | Nonetreer8r&r9strr*tuple[Array, Callable[[Array], ArrayTree]]cCs0t||||d\}}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. )r:r8r9)r _ravel_leavesr _tree_unravel)r<r:r8r9leavestreespecflat unravel_flatr!r!r"rqsE rrBrrD"Callable[[Array], list[ArrayLike]]rCrcCst|||Sr')r )rBrDrCr!r!r"r@sr@rAlist[ArrayLike]0tuple[Array, Callable[[Array], list[ArrayLike]]]cs|s tdtfStdd|D}tj|tdd|D}tdd|D}tt|}tfdd|DrMt dd|D}|t t ||fSt fd d|D}|t t |||fS) Nrcs|]}t|VqdSr')r dtype.0leafr!r!r" z _ravel_leaves..csrHr')jnpsizerJr!r!r"rMrNcsrHr')rOshaperJr!r!r"rMrNc3s|]}|kVqdSr'r!)rKdtto_dtyper!r"rMscSsg|]}t|qSr!)rOravelrJr!r!r" sz!_ravel_leaves..csg|] }tt|qSr!)rOrUr convert_element_typerJrSr!r"rVs) rOzeros_unravel_emptyr.r result_type itertools accumulateall concatenater_unravel_leaves_single_dtype_unravel_leaves)rA from_dtypessizesshapesindicesraveledr!rSr"r?s$  r?cCs.t|dkrtdddt|dgS)N)r0The unravel function expected an array of shape , got shape r)rOrQ ValueError)rCr!r!r"rYs rYrdtuple[int, ...]rctuple[tuple[int, ...], ...] list[Array]cCs^t||dfkrtd|dfdt|dt||dd}ddt||DS)NrfrgrcSsg|] \}}||qSr!)reshape)rKchunkrQr!r!r"rVsz0_unravel_leaves_single_dtype..)rOrQrhsplitr)rdrcrCchunksr!r!r"r_sr_ratuple[jnp.dtype, ...]rT jnp.dtypecCst||dfkrtd|dfdt|dt|}||kr/td|d|dt||dd}ttddd t |||DWdS1sXwYdS) Nrlrfrgrz0The unravel function expected an array of dtype z , got dtype ignorecSs$g|]\}}}t|||qSr!)r rWrm)rKrnrQrIr!r!r"rVsz#_unravel_leaves..) rOrQrhr rIrowarningscatch_warnings simplefilterr)rdrcrarTrC array_dtyperpr!r!r"r` s$    $r`r') r:r;r<rr8r&r9r=rr>)rBrrDrErCrrr)rArFrrG)rCrrrF)rdrircrjrCrrrk) rdrircrjrarqrTrrrCrrrk)1r5 __future__r contextlibr[rtoperatorrtypesrtypingrrtyping_extensionsr jax.numpynumpyrOjaxrr jax._srcr jax.typingr optree.opsr r optree.typingrr optree.utilsrr__all__rr6rrsuppress ImportError jax._src.utilr ravel_pytreer@r?rYr_r`r!r!r!r"sD        ,O  $