o i @sVddlZddlZddlZddlmZddlmZmZmZm Z m Z m Z GdddZ dS)N)Path)AnyCallableDictOptionalTupleUnionc@s(eZdZUdZdZeed<eZ ejed<e ed<e ed<e e ed<d*d e d e fd d Z d edee ede fffddZde de fddZde dee ede fffddZddZejddZddZdedefdd Zd!eeeffd"d#Zd!eeefddfd$d%Zd&d'Zd+d(d)Z dS),ShimaZDefine a basic interface for external models. Users can create subclasses of 'shim' to wrap external libraries. We provide shims for PyTorch. The Thinc Model class treats Shim objects as a sort of special type of sublayer: it knows they're not actual Thinc Model instances, but it also knows to talk to the shim instances when doing things like using transferring between devices, loading in parameters, optimization. It also knows Shim objects need to be serialized and deserialized with to/from bytes/disk, rather than expecting that they'll be msgpack-serializable. r global_idglobal_id_lockcfg_model _optimizerNmodel optimizercCsbtjtjd7_tj|_Wdn1swY|dur&t|ni|_||_||_dS)N)r r r iddictr r r)selfrconfigrrD/home/ubuntu/.local/lib/python3.10/site-packages/thinc/shims/shim.py__init__s  z Shim.__init__is_trainreturn.cCtNNotImplementedError)rinputsrrrr__call__#z Shim.__call__fwd_argscCs||dd\}}|S)NFrr)rr"Ybackproprrrpredict&sz Shim.predictcCs ||ddS)NTr#r)rr"rrr begin_update*s zShim.begin_updatecCrrr)rrrrr finish_update-r!zShim.finish_updateccs dVdSrr)rparamsrrr use_params0s zShim.use_paramscCs t|Sr)copydeepcopyrrrrr+4s z Shim.copy device_type device_idcCrrr)rr.r/rrr to_device7r!zShim.to_devicepathcCsX|}t|tr t|n|}|d}||WddS1s%wYdS)Nwb)to_bytes isinstancestrropenwrite)rr1 bytes_datafile_rrrto_disk:s   "z Shim.to_diskcCsRt|tr t|n|}|d }|}Wdn1swY||S)Nrb)r4r5rr6read from_bytes)rr1r9r8rrr from_disk@s    zShim.from_diskcCrrrr-rrrr3Fr!z Shim.to_bytescCrrr)rdatarrrr=Ir!zShim.from_bytes)NN)rr )!__name__ __module__ __qualname____doc__r int__annotations__ threadingLockr rrrrboolrrr r&r'r( contextlibcontextmanagerr*r+r5r0rrr:r>r3r=rrrrr s(   " " r ) rIr+rFpathlibrtypingrrrrrrr rrrrs