YjBddlmZddlZddlmZddlZdZdZd dZdS) ) annotationsN)abcc fd g d}d}t|D]Z\}}|jtjjkrIt |t |D]*}|jd|}|dz }||} | || z }+i|jtjjks4|jtjj ks|jtjj kr_|j}d} ||z }d} |t |kr|| ||} n||vr|| ||} d}  || } | r|| z }|jtjj kr*| D]\}} | vr  || \ S)Nc\|dSd}t|tjr+t|D]\}}||d||z }|St|tjr0|D]\}}||d||z }|S|dS)z;Returns number of expanded inputs that _add_input processedNr_) isinstancerSequence enumerateMappingitemsappend)nameinputnum_expanded_non_none_inputsivalkey _add_input input_namess e/var/www/FlaskApp/flask-venv/lib/python3.11/site-packages/onnxruntime/tools/pytorch_export_helpers.pyrz1_parse_inputs_for_onnx_export.._add_inputs =1'($ eS\ * * 0$E** O O3- d==Q==#0N0NN,,0 / s{ + + 0"KKMM Q QS, d??S??C0P0PP,,0 / 4   qrrrTF) r kindinspect ParameterVAR_POSITIONALrangelenrPOSITIONAL_ONLYPOSITIONAL_OR_KEYWORD KEYWORD_ONLY VAR_KEYWORDr )all_input_parametersinputskwargsvar_positional_idx'num_expanded_non_none_positional_inputs input_idxinput_parameterargs_irinp is_positional"num_expanded_non_none_inputs_localrrs @@r_parse_inputs_for_onnx_exportr. s""""""HK./+&/0D&E&E**" ?  7#4#C C C 3v;;77 Q Q).EE1CEE"a'"Vn7::dC;P;PP77  Q  G$5$E E E#w'8'NNN#w'8'EEE#'DC + +I M3v;;&&6)+<+HY'F4L$<Tl % 1;D#1F1F . ^7;]]7  !W%6%B B B#\\^^ * * c{**JtS))) rcddfd|D}|fd|Dz }s|it|S)z5Flatten args and kwargs in a single tuple of tensors.cHt|ttthvSN)typeintboolfloatvalues ris_primitive_typez0_flatten_module_input..is_primitive_type^sE{{sD%000rc*tj|Sr1)torchtensorr6s r to_tensorz(_flatten_module_input..to_tensoras|E"""rc@g|]}|r |n|Sr>).0argr8r<s r z)_flatten_module_input..ds6 M M M..s33 <99S>>> M M Mrclg|]0}|v|r|n|1Sr>r>)r?rr8r%r<s rrAz)_flatten_module_input..esX   Y]jnrxjxjx#4#4VD\#B#BT &,t jxjxjxr)rtuple)namesargsr%retr8r<s ` @@r_flatten_module_inputrGZs111### N M M M M M M MC      af   C  2 ::rmoduletorch.nn.Modulectj|jj}t |||}t |||}||fS)a Infer the input names and order from the arguments used to execute a PyTorch module for usage exporting the model via torch.onnx.export. Assumes model is on CPU. Use `module.to(torch.device('cpu'))` if it isn't. Example usage: input_names, inputs_as_tuple = infer_input_info(module, ...) torch.onnx.export(module, inputs_as_type, 'model.onnx', input_names=input_names, output_names=[...], ...) :param module: Module :param inputs: Positional inputs :param kwargs: Keyword argument inputs :return: Tuple of ordered input names and input values. These can be used directly with torch.onnx.export as the `input_names` and `inputs` arguments. )r signatureforward parametersvaluesr.rG)rHr$r%module_parametersrinputs_as_tuples rinfer_input_inforQqsT  )&.99DKKMM/0A66RRK+KHHO  ''r)rHrI) __future__rr collectionsrr:r.rGrQr>rrrTs}#""""" KKK\.((((((r