a
    ht                     @   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ejjed fddZe	e
d fddZdeeed
f eee
f eee
d
f  eeee
f  d fddZeeed
f eee
f d 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__ M/var/www/auris/lib/python3.9/site-packages/torch/fx/experimental/normalize.pyr   %   s    zNormalizeArgs.__init__)nreturnc                    s   |  \}}fdd tj }t|ts4J tdd |D } fdd| D }jdkr|| j||||}nt	 
}jdkr| j|< j|j_j|j_|S )	Nc                    s,   t | tjr$d jv r  jd S d S t| S )Ntype)
isinstancefxr	   metar   )arg)r   r   r   get_type/   s    z(NormalizeArgs.run_node.<locals>.get_typec                 S   s   g | ]}t |qS r   )r   ).0ir   r   r   
<listcomp>6       z*NormalizeArgs.run_node.<locals>.<listcomp>c                    s   i | ]\}}| |qS r   r   )r$   kv)r#   r   r   
<dictcomp>7   r'   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   r2   ,   s    




zNormalizeArgs.run_nodeN.)r1   r-   r4   r5   r6   c           	         sR   t |sJ t|||||| j}|r>|\}}| jd|||S t |||S d S )Nr+   )callabler   r   ZtracerZcreate_proxyr   r+   )	r   r1   r-   r4   r5   r6   new_args_and_kwargsnew_args
new_kwargsr   r   r   r+   B   s    zNormalizeArgs.call_functionr1   r-   r4   c                    sR   t |tsJ t| j|||| j}|r>|\}}t |||S t |||S d S r   )r   strr   r   r   r   call_module)r   r1   r-   r4   r9   r:   r;   r   r   r   r>   [   s    zNormalizeArgs.call_module)T)NN)__name__
__module____qualname____doc__torchr    ZGraphModuleboolr   r	   r   r2   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< eeedf eeef d 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_remap.r<   c                    sf   t |sJ || jv rVt|dkr2t |||S |\}}t j| j| ||fi dS t |||S )N   r<   )r8   rH   lenr   r+   )r   r1   r-   r4   lhsrhsr   r   r   r+      s    
z NormalizeOperators.call_function) r?   r@   rA   rB   rC   addoperatormulsubdivtruedivZfloor_dividefloordiv	remaindermodeqneltlegtgerH   rE   r   r   __annotations__r
   r.   r   r=   r+   rF   r   r   r   r   rG   m   s&   
rG   )rN   typingr   r   r   rC   Ztorch.fxr    r   r   Ztorch.fx.noder   r   r	   r
   Ztorch.fx.operator_schemasr   r   r   Zschema_type_annotationr   r   rG   r   r   r   r   <module>   s   Z