
    h%                        S SK r S SKJrJrJrJrJr  S SKrS SK	r	S SK	J
r
Jr  S SKJr  S SKJrJrJr  S SKJrJrJrJrJr  S SKJr  S SKJrJr  \" S	 \" 5        5       S
 S9r\" S \" 5        5       S S9rS\\ \\!\\\!\ S4   S4   4   4   S\\ \4   4S jr" SS\S\\    S\\    S\\ \4   S\\\ \RF                  4      S\\ \4   4S jjr$ " S S\ RJ                  5      r&\'S:X  a  \ RP                  " 5         gg)    N)DictListOptionalTupleUnion)TensorProto	TypeProto)ValidationError)OpSchemaget_all_schemas_with_history
get_schema)
make_graph	make_nodemake_opsetidmake_tensor_type_protomake_tensor_value_info)
from_array)InferenceErrorinfer_node_outputsc              #   l   #    U  H*  oR                   S :X  d  M  UR                  S:X  d  M&  Uv   M,     g7f)Add Nnamedomain.0ss     [/var/www/fran/franai/venv/lib/python3.13/site-packages/onnx/test/inference_function_test.py	<genexpr>r       s)     U.1&&E/QahhRTnQQ.s   44	4c                     U R                   $ Nsince_versionr   s    r   <lambda>r&          !//    )keyc              #   n   #    U  H+  nUR                   S :X  d  M  UR                  S:X  d  M'  Uv   M-     g7f)Reshaper   Nr   r   s     r   r    r       s5      /A66Y 	
#$88r> 	
/s   55	5c                     U R                   $ r"   r#   r%   s    r   r&   r&   "   r'   r(   tensor_types.returnc                 f    U R                  5        VVs0 s H  u  pU[        U6 _M     snn$ s  snnf r"   )itemsr   )r-   r)   values      r   _to_tensor_typesr2   &   s6     COBTBTBVWBVJCC'//BVWWWs   -schemainput_namesoutput_namesinput_types
input_datac                     Uc  0 n[        U [        U R                  XU R                  S9UUR	                  5        VVs0 s H  u  pVU[        U5      _M     snn5      $ s  snnf )N)r   )r   r   r   r   r0   r   )r3   r4   r5   r6   r7   r)   arrs          r   	_run_caser:   ,   sd     
&++{O.8.>.>.@A.@(#jo	.@A	  	Bs   Ac                   P    \ rS rSrS
S jrS
S jrS
S jrS
S jrS
S jrS
S jr	S	r
g)TestInferenceFunctionCall=   Nc           	         [         R                  S4[         R                  S4S.S[         R                  S404[         R                  S4[         R                  S4S.S[         R                  S404[         R                  S4[         R                  S4S.S[         R                  S404[         R                  S4[         R                  S4S.S[         R                  S404[         R                  S	4[         R                  S
4S.S[         R                  S404/nU H2  u  p#[        [        SS/S/[        U5      5      [        U5      :X  a  M2   e   g )N ABC)N   )rD   )   rD   )nm)rE   rF   rG   )xrD   )yrD   rA   rB   )r   FLOATDOUBLEr:   
ADD_SCHEMAr2   )selfcasesinsoutss       r   test_add_inference,TestInferenceFunctionCall.test_add_inference>   sp    #(("-[5F5F4KL{(("-. &++Y7%++T2 {(()45 &++Y7%++V4 {(()45 &,,j9%,,m< {))=9: &++X6%++X6 {(()455!
D ICZ#scU<LS<QRVfgkVllll r(   c                     U R                  [        5         [        [        S/S/[	        S[
        R                  S405      5        S S S 5        U R                  [        5         [        [        SS/S/[	        [
        R                  S4SS.5      5        S S S 5        U R                  [        5         [        [        SS/S/[	        [
        R                  S4[
        R                  S4S.5      5        S S S 5        U R                  [        5         [        [        SS/S/[	        S[
        R                  S405      5        S S S 5        g ! , (       d  f       GN= f! , (       d  f       N= f! , (       d  f       N= f! , (       d  f       g = f)NrA   rC         rB   )rD   rT   r@   )rD   rV   )	assertRaisesr
   r:   rL   r2   r   rJ   r   KeyErrorrM   s    r    test_add_inference_raises_errors:TestInferenceFunctionCall.test_add_inference_raises_errorsd   sF   / #(9(96'B!CD	 0 /c
 (9(96'B!UV	 0 ~.c
 )//8)//8	
 / x(c
 #(9(96'B!CD	 )(5 0/ 0/ /. )(s0   0E#2E2AE.1E?
E
E+.
E<?
Fc                    [        [        SS/S/[        [        R                  S4[        R
                  S4S.5      S[        R                  " / SQ[        R                  S905      [        S[        R                  S405      :X  d   eg )	NrH   trI   )   rV   )rU   )rH   r]   )rD   rD   r^   )dtype)	r:   RESHAPE_SCHEMAr2   r   rJ   INT64nparrayint64rY   s    r   test_reshape_inference0TestInferenceFunctionCall.test_reshape_inference   s    #JE%++V4%++T2 "((9BHH56
 s[%6%6	$BCDE 	E Er(   c                    SnSnSn[        S[        R                  S 5      [        S[        R                  S 5      [        S[        R                  S 5      /n[        S[        R                  S 5      [        S[        R                  X45      /n[	        [        S	S/S/5      [        S
SS/S/5      /SUU5      n[        [        SS5      [        S/ SQSS/SUS9[        [        R                  U44[        R                  X44[        R                  U44S.5      [        SS5      /SS9[        [        R                  U44[        R                  X44S.5      :X  d   eg )NsequencerD   rU   loop_state_ininputouterloop_state_outoutputIdentityr   subgraphScan	   )loop_state_orig
scan_input
scan_outerloop_state_finalscan_outputrE   )num_scan_inputsbodyr   rV   )opset_imports
ir_version)ru   rv   )
r   r   	UNDEFINEDrJ   r   r   r   r   r2   r   )rM   seq_len
input_sizeloop_state_sizeinput_value_infosoutput_value_infosro   s          r   !test_scan_inference_with_subgraph;TestInferenceFunctionCall.test_scan_inference_with_subgraph   s   
 #?K4I4I4P"7K,A,A4H"7K,A,A4H
 ##3[5J5JDQ"8[->->@UV

 *&7:J9KL%'7!3hZ@ 
 "vq!?#]3 ! (3(9(9O;M'N#.#4#4w6K"L#.#4#4zm"D (A./%
& %0%6%68J$K + 1 1G3HI
'
 	
 
r(   c                 D   Sn[         R                  R                  U5      n[         R                  R	                  USS9  U R                  [         R                  R                  5         [         R                  R	                  USS9  S S S 5        g ! , (       d  f       g = f)Na  
        <
            ir_version: 8,
            opset_import: ["" : 18, "onnxscript.atenlib" : 1],
            producer_name: "pytorch",
            producer_version: "2.1.0"
        >
        torch_jit (float input_0) => (float reault, int64 index)
        {
            reault, index = onnxscript.atenlib.aten_min_dim <dim = 0, keepdim = 1> (input_0)
        }
        <
            domain: "onnxscript.atenlib",
            opset_import: ["" : 18]
        >
        aten_min_dim <dim>(self) => (result_7, indices_6)
        {
            tmp = Shape (self)
            tmp_0 = Size (tmp)
            tmp_1 = Constant <value = int64 tmp_1 {0}> ()
            tmp_1_cast = CastLike (tmp_1, tmp_0)
            tmp_2 = Equal (tmp_0, tmp_1_cast)
            cond = Not (tmp_2)
            indices_6, result_7 = If (cond) <
                then_branch = thenGraph_4 () => ( indices,  result) {
                    dim = Constant <value_int: int = @dim> ()
                    tmp_3 = Constant <value_ints = [-1]> ()
                    dims = Reshape (dim, tmp_3)
                    result = ReduceMin <keepdims: int = @keepdim> (self, dims)
                    indices = ArgMin <axis: int = @dim, keepdims: int = @keepdim> (self)
                }, else_branch = elseGraph_4 () => ( indices_4,  result_5) {
                    indices_4 = Constant <value_int = 0> ()
                    result_5 = Identity (self)
                }
            >
        }
        Fstrict_modeT)onnxparserparse_modelshape_inferenceinfer_shapesrW   r   rM   model_scriptmodels      r   test_inference_with_conflow5TestInferenceFunctionCall.test_inference_with_conflow   sy    $J ''5))%U)Ct33BBC  --e-F DCCs   )B
Bc                     Sn[         R                  R                  U5      n[         R                  R	                  USS9  g )Na  
        <
            ir_version: 8,
            opset_import: ["" : 18, "custom" : 1],
            producer_name: "",
            producer_version: "1.0"
        >
        MeanVarianceNormalization (float[N] x) => (float[M] y)
        {
            y = custom.custom_mvn <axes = [0]> (x)
        }
        <
            domain: "custom",
            opset_import: ["" : 18]
        >
        custom_mvn <axes>(X) => (Y)
        {
          Exponent = Constant <value = float {2.0}>()
          Epsilon = Constant <value = float {1e-9}>()
          axes = Constant <value_ints: ints = @axes>()
          X_RM = ReduceMean (X, axes)
          EX_squared = Pow (X_RM, Exponent)
          X_squared = Pow (X, Exponent)
          E_Xsquared = ReduceMean (X_squared, axes)
          Variance = Sub (E_Xsquared, EX_squared)
          STD = Sqrt (Variance)
          X_variance = Sub (X, X_RM)
          Processed_STD = Add (STD, Epsilon)
          Y = Div (X_variance, Processed_STD)
        }
        Tr   )r   r   r   r   r   r   s      r   test_inference_with_attribute7TestInferenceFunctionCall.test_inference_with_attribute   s8    > ''5))%T)Br(   r?   )r.   N)__name__
__module____qualname____firstlineno__rQ   rZ   re   r   r   r   __static_attributes__r?   r(   r   r<   r<   =   s(    $mL!FE1
f)GV"Cr(   r<   __main__r"   ))unittesttypingr   r   r   r   r   numpyrb   r   r   r	   onnx.checkerr
   	onnx.defsr   r   r   onnx.helperr   r   r   r   r   onnx.numpy_helperr   onnx.shape_inferencer   r   maxrL   r`   strintr2   ndarrayr:   TestCaser<   r   mainr?   r(   r   <module>r      sU  
  5 5   ' ( H H  ) CU,.U!
 -/
 	"XsE#uU3T>-BC-G'H"HIIJX	#y.X 37c s) c9n%	
 c2::o./ 
#y."XC 1 1 XCv zMMO r(   