o i @sddlmZmZmZddlmZddlmZddlm Z m Z m Z ddl m Z mZmZ     dd ed d eed eededee deddeeeffddZdefddZdS))AnyCallableOptional)torch)Model)PyTorchGradScaler PyTorchShimTorchScriptShim)convert_pytorch_default_inputsconvert_pytorch_default_outputsforwardNFtorchscript_modelztorch.jit.ScriptModuleconvert_inputsconvert_outputsmixed_precision grad_scalerdevicez torch.devicereturnc CsD|durt}|dur t}tdt||dt||||dgddddS)aWrap a TorchScript model, so that it has the same API as Thinc models. torchscript_model: The TorchScript module. A value of `None` is also possible to construct a shim to deserialize into. convert_inputs: Function that converts inputs and gradients that should be passed to the model to Torch tensors. convert_outputs: Function that converts model outputs and gradients from Torch tensors Thinc arrays. mixed_precision: Enable mixed-precision. This changes whitelisted ops to run in half precision for better performance and lower memory use. grad_scaler: The gradient scaler to use for mixed-precision training. If this argument is set to "None" and mixed precision is enabled, a gradient scaler with the default configuration is used. device: The PyTorch device to run the model on. When this argument is set to "None", the default device for the currently active Thinc ops is used. Npytorch_script)rr)modelrrr)nInO)attrsshimsdims)r r rrr )rrrrrrrS/home/ubuntu/.local/lib/python3.10/site-packages/thinc/layers/torchscriptwrapper.pyTorchScriptWrapper_v1 s" rrc Cs~|jd}t|tstd|jd}|jd}|j}t|tjjs&tdtj |}|j }|j }|j }t||||||dS)zzConvert a PyTorch wrapper to a TorchScript wrapper. The embedded PyTorch `Module` is converted to `ScriptModule`. rz6Expected PyTorchShim when converting a PyTorch wrapperrrz*PyTorchShim does not wrap a PyTorch module)rrrrr)r isinstancer ValueErrorr_modelrnnModulejitscript _grad_scaler_mixed_precisionrr) rshimrr pytorch_modelrrrrrrrpytorch_to_torchscript_wrapperBs(     r+)NNNFNN)typingrrrcompatrrrrrr r pytorchwrapperr r rboolrr+rrrrs6    5