o
    Zht                     @   s   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	 d	e	ZG d
d deZdS )    N)AnyCallableOptional)ProxyTransformer)Argumentmap_aggregateNodeTarget)create_type_hintnormalize_functionnormalize_module   )AnnotateTypesWithSchemac                       s   e Zd ZdZ	ddejjdef fddZde	de
f fd	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
 fddZdedeedf deee
f f fddZ  ZS )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()
    Tmodulenormalize_to_only_use_kwargsc                    s   t  | i | _|| _d S N)super__init__node_mapr   )selfr   r   	__class__ N/var/www/auris/lib/python3.10/site-packages/torch/fx/experimental/normalize.pyr   %   s   
zNormalizeArgs.__init__nreturnc                    s   |  \}}fdd tj }t|tsJ tdd |D } fdd| D }jdkr>| j||||}nt	 
}jdkrX| j|< j|j_j|j_|S )	Nc                    s,   t | tjrd jv r jd S d S t| S )Ntype)
isinstancefxr	   metar   )arg)r   r   r   get_type/   s   z(NormalizeArgs.run_node.<locals>.get_typec                 S   s   g | ]}t |qS r   )r   ).0ir   r   r   
<listcomp>6   s    z*NormalizeArgs.run_node.<locals>.<listcomp>c                    s   i | ]	\}}| |qS r   r   )r$   kv)r#   r   r   
<dictcomp>7   s    z*NormalizeArgs.run_node.<locals>.<dictcomp>call_functionoutput)Zfetch_args_kwargs_from_envr   argsr   tupleitemsopr*   targetr   run_noder   r!   noder   )r   r   r,   kwargs	arg_typeskwarg_typesoutr   )r#   r   r   r1   ,   s   




zNormalizeArgs.run_nodeNr0   r,   .r3   r4   r5   c           	         sN   t |sJ t|||||| j}|r|\}}| jd|||S t |||S )Nr*   )callabler   r   ZtracerZcreate_proxyr   r*   )	r   r0   r,   r3   r4   r5   new_args_and_kwargsnew_args
new_kwargsr   r   r   r*   B   s   zNormalizeArgs.call_functionc                    sN   t |tsJ t| j|||| j}|r|\}}t |||S t |||S r   )r   strr   r   r   r   call_module)r   r0   r,   r3   r8   r9   r:   r   r   r   r<   [   s   zNormalizeArgs.call_module)T)NN)__name__
__module____qualname____doc__torchr    ZGraphModuleboolr   r	   r   r1   r
   r-   r   dictr;   r   r*   r<   __classcell__r   r   r   r   r      s:    



r   c                       s   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 fddZ  ZS )	NormalizeOperatorsa  
    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()
    binary_magic_method_remapr0   r,   .r3   c                    sf   t |sJ || jv r+t|dkrt |||S |\}}t j| j| ||fi dS t |||S )N   )r0   r,   r3   )r7   rF   lenr   r*   )r   r0   r,   r3   lhsrhsr   r   r   r*      s   
z NormalizeOperators.call_function) r=   r>   r?   r@   rA   addoperatormulsubdivtruedivZfloor_dividefloordiv	remaindermodeqneltlegtgerF   rC   r   r   __annotations__r
   r-   r   r;   r*   rD   r   r   r   r   rE   m   s2   
 

rE   )rL   typingr   r   r   rA   Ztorch.fxr    r   r   Ztorch.fx.noder   r   r	   r
   Ztorch.fx.operator_schemasr   r   r   Zschema_type_annotationr   r   rE   r   r   r   r   <module>   s   Z