
    [Th$                        S SK r S SKrS SKJr  S SKJr  S SKrS SKrS SK	rS SK
rS SKJs  Jr  / SQr\R                   R"                  r\R                   R&                  r\R                   R*                  rSrS\\R0                  R2                     4S jr\S 5       rS	 rS
 r " S S5      rS\S\4S jr S r!\S 5       r"g)    N)Iterator)contextmanager)enable_python_dispatcherno_python_dispatcherenable_pre_dispatchFreturnc               #      #    [         R                   HG  n [        [         R                  U 5      nU H$  n[        X5      nU H  n[        X45      v   M     M&     MI     g7f)a  
Warning: the set of overloads this will report is very subtle.  It is precisely
the set of torch.ops functions that have actually been accessed from Python
(e.g., we actually called torch.ops.aten.blah at some point.  This is DIFFERENT
from the set of registered operators, which will in general be a larger set,
as this would include all operators which we ran C++ static initializers or
Python operator registration on.  This does not eagerly populate the list on
torch.ops.aten; this list is lazy!

In other words, this is good for traversing over everything that has an
OpOverload object allocated in Python.  We use it for cache invalidation, but
don't rely on this list being complete.

Note that even if we did report all C++ registered overloads, this isn't guaranteed
to be complete either, as a subsequent lazy load of a library which triggers more
registrations could add more things to the set.
N)torchopsgetattr)nspacketsop_namepacketoverloads        N/var/www/auris/envauris/lib/python3.13/site-packages/torch/_dispatch/python.pyall_py_loaded_overloadsr      sN     $ ii%))R(GW.F"f// #  s   AAc               #     #    [         R                  R                  [         R                  R                  R                  5      n [         R                  R                  5       nU (       a  [         R                  " 5          S v   U (       a  [         R                  " US9  g g ! U (       a  [         R                  " US9  f f = f7f)N)reapply_views)r
   _C&_dispatch_tls_is_dispatch_key_includedDispatchKeyFunctionalize$_functionalization_reapply_views_tls_disable_functionalization_enable_functionalization)f_tlsf_rvs     r   suspend_functionalizationr   1   s     HH;;**E 8888:D((*@++$? 5++$? s   A<C?B  C B>>Cc           	         [        U5      (       d   eU R                  5       UR                  5       :X  d0   U" 5        SU R                  5        SUR                  5        35       eU R                  UR                  :X  d(   U" 5        SU R                   SUR                   35       e[        R                  R                  XSS9u  p4U(       d4   U" 5        SU R                  5        SUR                  5        SU S35       eg )	Nz: sizes  != z: dtype F)	only_cudaz
: strides z (mismatch at index ))callablesizedtyper
   _prims_commoncheck_significant_stridesstride)nvrvdescsame_stridesidxs        r   check_tensor_metadata_matchesr/   @   s    D>>>779	!PdfXXbggi[RWWYK#PP!88rxxLDF88BHH:T"((!LL++EE
% F L  6(*RYY[Mbiik]:NseSTU<    c                   ^^	 [        T5      (       d   e[        R                  " U 5      u  p4[        R                  " U5      u  pV[        U5      [        U5      :X  d   [        U5       S[        U5       35       e[	        [        [        U5      5      X55       H8  u  m	px[        U[        R                  5      (       d  M'  [        XxUU	4S j5        M:     g )Nr!   c                     > T " 5        ST 3$ )Nz output  )r,   is   r   <lambda>(check_metadata_matches.<locals>.<lambda>V   s    6Lr0   )
r$   pytreetree_flattenlenziprange
isinstancer
   Tensorr/   )
nrr,   n_vals_n_specr_vals_r_specr*   r+   r4   s
     `      @r   check_metadata_matchesrD   L   s    D>>>))!,OF))!,OF v;#f+%H#f+d3v;-'HH%s6{+V<	2"ell++%b.LM =r0   c                        \ rS rSrS rS rSrg)LitY   c                     Xl         g Ns)selfrK   s     r   __init__Lit.__init__Z   s    r0   c                     U R                   $ rI   rJ   )rL   s    r   __repr__Lit.__repr__]   s    vvr0   rJ   N)__name__
__module____qualname____firstlineno__rM   rP   __static_attributes__r3   r0   r   rF   rF   Y   s    r0   rF   ac           	          [        U [        R                  5      (       aD  [        S[	        U R                  5       5       SU R                  5        SU R                   S35      $ U $ )Nztorch.empty_strided(, z, dtype=r#   )r<   r
   r=   rF   tupler%   r)   r&   )rW   s    r   _fmtr[   a   sU    !U\\"""5?"32ahhj\!''RST
 	
 r0   c                    ^ ^^ SSK Jm  T [        R                  R                  R
                  R                  :X  a  T$ UUU 4S jnU$ )Nr   )FakeTensorModec                     >^	^
^ T" 5       m	U	4S jnS n[         R                  R                  R                  5          [	        5          [
        R                  " X U45      u  pE[
        R                  " X4U45      u  m
mT	   T" U0 UD6nS S S 5        S S S 5        S S S 5        TR                  " T/U Q70 UD6nUU
U4S jn[        WXx5        U$ ! , (       d  f       NJ= f! , (       d  f       NS= f! , (       d  f       N\= f)Nc                 `  > [        U [        R                  5      (       a  [        R                  " U 5      (       a_  [        R                  " U 5      nU R                  5       UR                  5       :X  d   eU R                  5       UR                  5       :X  d   eOU nTR                  U5      $ U $ rI   )r<   r
   r=   _is_functional_tensor_from_functional_tensorr%   r)   from_tensor)tr?   	fake_modes     r   fakeify_defunCmake_crossref_functionalize.<locals>.handler.<locals>.fakeify_defunt   s    !U\\**..q1155a8A
 668qvvx///88:333A ,,Q//Hr0   c                 d    [        U [        R                  5      (       a  U R                  5       $ U $ rI   )r<   r
   r=   detach)rc   s    r   maybe_detachBmake_crossref_functionalize.<locals>.handler.<locals>.maybe_detach   s$    !U\\**xxz!r0   c                     > SR                  [        R                  " S T 5       S TR                  5        5       5      5      n T SU  S3$ )NrY   c              3   j   #    U  H)  n[        [        R                  " [        U5      5      v   M+     g 7frI   )reprr7   tree_mapr[   ).0rW   s     r   	<genexpr>Mmake_crossref_functionalize.<locals>.handler.<locals>.desc.<locals>.<genexpr>   s#     I[T&//$233[s   13c              3   f   #    U  H'  u  pU S [         R                  " [        U5       3v   M)     g7f)=N)r7   rn   r[   )ro   kvs      r   rp   rq      s0      $9DA #QvtQ789$9s   /1(r#   )join	itertoolschainitems)fmt_argsoporig_f_argsorig_f_kwargss    r   r,   :make_crossref_functionalize.<locals>.handler.<locals>.desc   sP    yyI[I$1$7$7$9H T8*A&&r0   )	r
   utils_python_dispatch_disable_current_modesr   r7   rn   _op_dkrD   )argskwargsre   ri   f_argsf_kwargsf_rr?   r,   rd   r}   r~   r]   	final_keyr|   s            @@@r   handler,make_crossref_functionalize.<locals>.handlerq   s    "$		 	 KK((??A%'%}VnMF)/x0*&K &-H-  ( B IIi1$1&1
	' 	sA,#  (' BAs;   C/8C 	C	CC/
CC
C,	(C//
C=)torch._subclasses.fake_tensorr]   r
   r   aten
lift_freshdefault)r|   r   r   r]   s   `` @r   make_crossref_functionalizer   j   s7    < 
UYY^^&&...4l Nr0   c               #   t  #    [        5        H6  n U R                  [        R                  R                  R
                  5        M8      [        5          [        R                  R                  SS5         S v   S S S 5        S S S 5        [        5        H6  n U R                  [        R                  R                  R
                  5        M8     g ! , (       d  f       N[= f! , (       d  f       Nd= f! [        5        H6  n U R                  [        R                  R                  R
                  5        M8     f = f7f)Nz-torch._dispatch.python.CROSSREF_FUNCTIONALIZET)
r   _uncache_dispatchr
   r   r   r   r   unittestmockpatch)r|   s    r   enable_crossref_functionalizer      s     %'
UXX11??@ (E$&MM OQUV W '
 *+B  !5!5!C!CD ,	 WV '&
 *+B  !5!5!C!CD ,sP   AD8
C/ !C3C8C C/ AD8
C	C
C,(C/ /AD55D8)#rx   unittest.mockr   collections.abcr   
contextlibr   r
   torch._C
torch._opstorch.utils._python_dispatchtorch.utils._pytreer   _pytreer7   __all__r   _DisablePythonDispatcherr   _EnablePythonDispatcherr   _EnablePreDispatchr   CROSSREF_FUNCTIONALIZE_ops
OpOverloadr   r   r/   rD   rF   objectr[   r   r   r3   r0   r   <module>r      s      $ %    # $ $ Vxx88  88;; hh11  0%***?*?!@ 04 @ @	
N F v =D E Er0   