TiuSSKJr SSKrSSKrSSKrSSKJrJrJrJ r J r SSK J r SSK r SSK Jr SSKJrJrJrJr \ "S5r\ "S5rSSS jjrSSS jjrSS jrSS jrSS jrg)) annotationsN)AnyCallableOptionalSequenceTypeVar) ParamSpec)ir) ast_utils converter irbuildervalues_R_PcR[R"UUUUS9nURU5$)zACheck that a function falls into the ONNXScript subset of Python.)opset global_namessource default_opset)r Convertertranslate_function_def)frrrrconverts X/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/onnxscript/_internal/main.py script_checkrs3!!!# G  ) )! ,,c ^^^T=(d [R"SS5m[T[R5(d [S5eSUUU4SjjnU$)aMain decorator. Declares a function as an onnx function. Args: opset: Opset the function belongs to (see :ref:`l-api-opsets`). default_opset: Opset to use for operators not in the function's opset. kwargs: Additional keyword arguments. Returns: an instance of :class:`onnxscript.values.OnnxFunction` Example: :: @script() def log2(x): one = op.Constant(value=make_tensor('one', TensorProto.FLOAT, [1], [1])) return op.Div(op.Log(x), op.CastLike(op.Log(cst), x)) Or: :: from onnxscript.onnx_opset import opset16 @script(opset16) def log2(x): one = op.Constant(value=make_tensor('one', TensorProto.FLOAT, [1], [1])) return op.Div(op.Log(x), op.CastLike(op.Log(cst), x)) thiszLScript parameter must be an opset. Did you use @script instead of @script()?c>[R"U5(d [S5e[R"U5up[R "U5n[R "U5nURR5nURUR5 [UT XQTS9n[R"T XUT5$)Nz=The ONNXScript decorator should be applied to functions only.)r)inspect isfunction TypeErrorr get_src_and_ast getmodulegetclosurevars__dict__copyupdate nonlocalsr onnxscript OnnxFunction) rsrcf_astmoduleclosureenvresultrkwargsrs r transformscript..transformQs!!!$$[\ \..q1  ""1%((+oo""$ 7$$%eUCMR&&uafEEr)rzCallable[_P, _R]returnzonnxscript.OnnxFunction[_P, _R])rOpset isinstancer#)rrr3r4s``` rscriptr9)sPD  ,V\\&!,E eV\\ * * Z  FF" rc^^[R"S5m[R"S5nURSnURRmSUU4SjjnU$)aA parametric decorator used to annotate nested-functions that are used as graph-attributes. Returns: A decorator that returns its input function, but attaches a graph_proto attribute representing the input function. The translation is not done at this time, but previously when the outer-level function was translated to an OnnxFunction. The decorator just looks up and retrieves the GraphProto representation previously generated. Example: :: @script() def cumulative_sum(X: INT64['N']): # Translation of cumulative_sum by @script will also translate Sum # into a GraphProto, which will be stored in the OnnxFunction generated # for cumulative_sum. At run-time (in eager-mode), the @graph decorator # retrieves the pre-computed GraphProto and attaches it to the Sum function. @graph() def Sum(sum_in, next): sum_out = sum_in + next scan_out = op.Identity(sum_out) return sum_out, scan_out zero = op.Constant(value_int=0) # The call to higher-order operator Scan below uses the above function # Sum as a graph-attribute. all_sum, result = op.Scan (zero, X, body=Sum, num_scan_inputs=1) return result rselfcN>[R"TURTU5$N)r OnnxClosure__name__)rfunction_framenested_functionss rr4graph..transforms#!!"21::">PQRRr)rrr6zvalues.OnnxClosure)sys _getframef_locals function_irrB) wrapper_frame onnx_functionr4rArBs @@rgraphrJesXT]]1%NMM!$M!**62M$00AASS rc6[U[R5$)zAReturn True if f is a function converted by onnxscript decorator.)r8r+r,)rs ris_converted_funrLs a00 11rc [R"[R"///SS0S9UVs/sH0n[RR UR 55PM2 snSSS9n[R "X15 gs snf)N)inputsoutputsnodes opset_imports p2o) functions ir_version producer_name)r ModelGraphserdedeserialize_functionto_function_protosave)rVfilenamermodels rexport_onnx_librasx HH r(  R[[QZA288001D1D1FGQZ[ EGGE \s7B r>) rzast.FunctionDefrz values.Opsetrzdict[str, Any]rstrrOptional[values.Opset]r6zirbuilder.IRFunction)NN)rrcrrcr3rr6z=Callable[[Callable[_P, _R]], onnxscript.OnnxFunction[_P, _R]])r6z(Callable[[Callable], values.OnnxClosure])rrr6bool)rVzSequence[values.OnnxFunction]r_rbr6None) __future__rastr!rDtypingrrrrrtyping_extensionsr r+r onnxscript._internalr r r rrrrr9rJrLrarrrls#  =='HH T]t_-1 -- -!-  - * -  -*%),09 !9)99C 9x2j2 r