cQj@ddlmZddlZddlmZddlZdZdZddZy)) annotationsN)abccJ fd g d}d}t|D]\}}|jtjjk(rBt |t |D])}|jd|}|dz }||} | || z }+p|jtjjk(sN|jtjjk(s'|jtjjk(rR|j}d} ||z }d} |t |kr ||||} n||vr ||||} d}  || } | s0|| z }7|jtjjk(s`|jD]\}} | vs  ||  S)Nc<|yd}t|tjr&t|D]\}}||d||z }|St|tjr+|j D]\}}||d||z }|Sj |y)z;Returns number of expanded inputs that _add_input processedr_) isinstancerSequence enumerateMappingitemsappend)nameinputnum_expanded_non_none_inputsivalkey _add_input input_namess m/home/oem/CyberBrain26-unified/.venv/lib/python3.12/site-packages/onnxruntime/tools/pytorch_export_helpers.pyrz1_parse_inputs_for_onnx_export.._add_inputs ='($ eS\\ *$E* O3- dV1QC=#0NN, O0 / s{{ +"KKM QS, dV1SE?C0PP, Q 0 / 4 rrrTF) 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*" ?   7#4#4#C#C C 3v;7 Q)../q1C0DE"a'"Vn7:dC;PP7  Q  G$5$5$E$E E##w'8'8'N'NN##w'8'8'E'EE#''DC + +I M3v;&6)+<+HY'F4L$<Tl % 1;D#1F .7;]]7  ! !W%6%6%B%B B#\\^ * c{*tS) *9*@ rcd}d}|Dcgc]}||r||n|}}||Dcgc]"}||vs|||r |||n||$c}z }|s|jit|Scc}wcc}w)z5Flatten args and kwargs in a single tuple of tensors.c:t|ttthvSN)typeintboolfloatvalues ris_primitive_typez0_flatten_module_input..is_primitive_type^sE{sD%000rc,tj|Sr1)torchtensorr6s r to_tensorz(_flatten_module_input..to_tensoras||E""r)rtuple)namesargsr%r8r<argretrs r_flatten_module_inputrBZs1#IM M.s39S> < MC Maf Y]jnrxjx#4VD\#B &,t T C  2 : N sA5 A:A:ctj|jjj }t |||}t |||}||fS)a Infer the input names and order from the arguments used to execute a PyTorch module for usage exporting the model via torch.onnx.export. Assumes model is on CPU. Use `module.to(torch.device('cpu'))` if it isn't. Example usage: input_names, inputs_as_tuple = infer_input_info(module, ...) torch.onnx.export(module, inputs_as_type, 'model.onnx', input_names=input_names, output_names=[...], ...) :param module: Module :param inputs: Positional inputs :param kwargs: Keyword argument inputs :return: Tuple of ordered input names and input values. These can be used directly with torch.onnx.export as the `input_names` and `inputs` arguments. )r signatureforward parametersvaluesr.rB)moduler$r%module_parametersrinputs_as_tuples rinfer_input_inforKqsP  ))&..9DDKKM/0A66RK+KHO  ''r)rHztorch.nn.Module) __future__rr collectionsrr:r.rBrKrrrOs$# K\.(r