Ë
    f^(ht  ã                   ó°   — d dl Z d dlmZmZmZ d dlZd dlZd dlmZ d dlmZm	Z	 d dl
mZmZmZmZ d dlmZmZmZ ddlmZ  G d„ d	e	«      Z G d
„ de«      Zy)é    N)ÚAnyÚCallableÚOptional)ÚProxyÚTransformer)ÚArgumentÚmap_aggregateÚNodeÚTarget)Úcreate_type_hintÚnormalize_functionÚnormalize_moduleé   )ÚAnnotateTypesWithSchemac                   óò   ‡ — e Zd ZdZ	 ddej
                  j                  defˆ fd„Zde	de
fˆ fd„Z	 	 dded	eed
f   deee
f   deee
d
f      deeee
f      f
ˆ fd„Zded	eed
f   deee
f   fˆ fd„Zˆ xZS )ÚNormalizeArgsa‡  
    Normalize arguments to Python targets. This means that
    `args/kwargs` will be matched up to the module/functional's
    signature and rewritten to exclusively kwargs in positional order
    if `normalize_to_only_use_kwargs` is true. Also populates default
    values. Does not support positional-only parameters or varargs
    parameters (*args, **kwargs).

    If the nodes have 'type' metadata, it will use it to disambiguate
    overloads. Otherwise, it will throw an error.

    Example usage:
        m = torchvision.models.resnet18()
        traced = torch.fx.symbolic_trace(m)
        traced = NormalizeArgs(traced).transform()
    ÚmoduleÚnormalize_to_only_use_kwargsc                 ó@   •— t         ‰| �  |«       i | _        || _        y ©N)ÚsuperÚ__init__Únode_mapr   )Úselfr   r   Ú	__class__s      €ú]/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/fx/experimental/normalize.pyr   zNormalizeArgs.__init__%   s!   ø€ ô 	‰Ñ˜Ô Ø+-ˆŒØ,HˆÕ)ó    ÚnÚreturnc                 ó\  •‡— | j                  ‰«      \  }}ˆfd„}t        ‰j                  |«      }t        |t        «      sJ ‚t	        |D �cg c]  }t        |«      ‘Œ c}«      }|j                  «       D ��ci c]  \  }}| ||«      “Œ }	}}‰j                  dk(  r | j                  ‰j                  ||||	«      }
nt        ‰| �-  ‰«      }
‰j                  dk7  rE‰| j                  |
<   ‰j                  |
j                  _        ‰j                  |
j                  _        |
S c c}w c c}}w )Nc                 óŒ   •— t        | t        j                  «      rd‰j                  v r‰j                  d   S d S t	        | «      S )NÚtype)Ú
isinstanceÚfxr
   Úmetar"   )Úargr   s    €r   Úget_typez(NormalizeArgs.run_node.<locals>.get_type/   s9   ø€ Ü˜#œrŸw™wÔ'Ø)/°1·6±6Ñ)9�q—v‘v˜f‘~ÐC¸tÐCÜ˜“9Ðr   Úcall_functionÚoutput)Úfetch_args_kwargs_from_envr	   Úargsr#   Útupler   ÚitemsÚopr(   Útargetr   Úrun_noder   r%   Únoder"   )r   r   r+   Úkwargsr'   Ú	arg_typesÚiÚkÚvÚkwarg_typesÚoutr   s    `         €r   r0   zNormalizeArgs.run_node,   sû   ù€ Ø×6Ñ6°qÓ9‰ˆˆfô	ô
 " !§&¡&¨(Ó3ˆ	Ü˜)¤UÔ+Ð+Ð+Ü¸	ÖB°1Ô+¨AÕ.ÒBÓCˆ	Ø28·,±,³.×A©$¨!¨Q�q™( 1›+‘~ÐAˆÑAØ�4‰4�?Ò"Ø×$Ñ$ Q§X¡X¨t°V¸YÈÓT‰Cä‘'Ñ" 1Ó%ˆCØ�4‰4�8ÒØ!"ˆD�M‰M˜#ÑØŸF™FˆC�H‰HŒMØŸF™FˆC�H‰HŒMØˆ
ùò CùÛAs   ÁD#Á9D(r/   r+   .r2   r3   r7   c                 ó¾   •— t        |«      sJ ‚t        |||||| j                  «      }|r#|\  }}| j                  j	                  d|||«      S t
        ‰	| �  |||«      S )Nr(   )Úcallabler   r   ÚtracerÚcreate_proxyr   r(   )
r   r/   r+   r2   r3   r7   Únew_args_and_kwargsÚnew_argsÚ
new_kwargsr   s
            €r   r(   zNormalizeArgs.call_functionB   s{   ø€ ô ˜ÔÐÐÜ0ØØØØØØ×-Ñ-ó
Ðñ Ø#6Ñ ˆH�jØ—;‘;×+Ñ+Ø ¨°:óð ô ‘7Ñ(¨°°vÓ>Ð>r   c                 óÀ   •— t        |t        «      sJ ‚t        | j                  |||| j                  «      }|r|\  }}t
        ‰| �  |||«      S t
        ‰| �  |||«      S r   )r#   Ústrr   r   r   r   Úcall_module)r   r/   r+   r2   r=   r>   r?   r   s          €r   rB   zNormalizeArgs.call_module[   sp   ø€ ô ˜&¤#Ô&Ð&Ð&Ü.Ø�K‰KØØØØ×-Ñ-ó
Ðñ Ø#6Ñ ˆH�jÜ‘7Ñ& v¨x¸ÓDÐDä‘7Ñ& v¨t°VÓ<Ð<r   )T)NN)Ú__name__Ú
__module__Ú__qualname__Ú__doc__Útorchr$   ÚGraphModuleÚboolr   r
   r   r0   r   r,   r   ÚdictrA   r   r(   rB   Ú__classcell__©r   s   @r   r   r      sß   ø„ ñð$ RVñIØ—h‘h×*Ñ*ðIØJNõIð˜$ð  3õ ð6 04Ø04ñ?àð?ð �H˜c�MÑ"ð?ð �S˜#�X‘ð	?ð
 ˜E # s (™OÑ,ð?ð ˜d 3¨ 8™nÑ-õ?ð2=Øð=Ø$)¨(°C¨-Ñ$8ð=ØBFÀsÈCÀxÁ.÷=ñ =r   r   c                   óŠ  ‡ — e Zd ZU dZej
                  ej
                  ej                  ej                  ej                  ej                  ej                  ej                  ej                  ej                  ej                  ej                  ej                  ej                  ej                   ej                   ej"                  ej"                  ej$                  ej$                  ej&                  ej&                  ej(                  ej(                  iZeeeegef   eeegef   f   ed<   dedeedf   deeef   fˆ fd„Zˆ xZS )ÚNormalizeOperatorsaÚ  
    Normalize callsites that are different ways of "spelling" the same
    invocation into a single, canonical call. Currently supports:

    1. Normalize operators (e.g. operator.add) to the `torch` ops they
       ultimately invoke (e.g. torch.add) when it is possible to statically
       reason that

    Example usage:

        m = torchvision.models.resnet18()

        traced = torch.fx.symbolic_trace(m)

        traced = NormalizeOperators(traced).transform()
    Úbinary_magic_method_remapr/   r+   .r2   c                 óæ   •— t        |«      sJ ‚|| j                  v rEt        |«      dk7  rt        ‰| �  |||«      S |\  }}t        ‰| �  | j                  |   ||fi ¬«      S t        ‰| �  |||«      S )Né   )r/   r+   r2   )r:   rO   Úlenr   r(   )r   r/   r+   r2   ÚlhsÚrhsr   s         €r   r(   z NormalizeOperators.call_function�   sŽ   ø€ ô ˜ÔÐÐà�T×3Ñ3Ñ3Ü�4‹y˜AŠ~Ü‘wÑ,¨V°T¸6ÓBÐBØ‰HˆC�ä‘7Ñ(Ø×5Ñ5°fÑ=Ø˜3�ZØð )ó ð ô ‰wÑ$ V¨T°6Ó:Ð:r   ) rC   rD   rE   rF   rG   ÚaddÚoperatorÚmulÚsubÚdivÚtruedivÚfloor_divideÚfloordivÚ	remainderÚmodÚeqÚneÚltÚleÚgtÚgerO   rJ   r   r   Ú__annotations__r   r,   r   rA   r(   rK   rL   s   @r   rN   rN   m   s  ø… ñð( 	�	‰	�8—<‘<Ø�	‰	�8—<‘<Ø�	‰	�8—<‘<Ø�	‰	�8×#Ñ#Ø×Ñ˜H×-Ñ-Ø�‰˜Ÿ™Ø�‰�(—+‘+Ø�‰�(—+‘+Ø�‰�(—+‘+Ø�‰�(—+‘+Ø�‰�(—+‘+Ø�‰�(—+‘+ð	ð ˜tØ�#�s�˜S�Ñ! 8¨S°#¨J¸¨OÑ#<Ð<ñ ó ð";Øð;Ø$)¨(°C¨-Ñ$8ð;ØBFÀsÈCÀxÁ.÷;ñ ;r   rN   )rV   Útypingr   r   r   rG   Útorch.fxr$   r   r   Útorch.fx.noder   r	   r
   r   Útorch.fx.operator_schemasr   r   r   Úschema_type_annotationr   r   rN   © r   r   ú<module>rl      sK   ðã ß *Ñ *ã Û Ý ß 'ß ?Ó ?÷ñ õ <ôW=�Kô W=ôt6;Ð0õ 6;r   