[Th SSKJr SSKJrJr SSKJr SSKJrJ r /SQr S\RS\ \RS\ 4S jrS \RS \\ \R\ \R4S\R4S jrSS \RS\RS\S\\4SjjrS \\RS\S\\R4SjrS \RS\S\\\44Sjr"SS\R5rg))chain)AnyOptional)nn)is_parametrizedtype_before_parametrizations)module_contains_param swap_module module_to_fqn fqn_to_moduleget_arg_info_from_tensor_fqn FakeSparsitymoduleparametrizationreturnc^[U5(a-[U4SjURR555$g)Nc3R># UHup[U4SjU55v M g7f)c3<># UHn[UT5v M g7fN) isinstance).0paramrs Y/var/www/auris/envauris/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/utils.py 2module_contains_param...sK u 5/22 sN)any)rkey param_listrs rr(module_contains_param..s( #B K K K K#Bs$'F)rrparametrizationsitems)rrs `rr r s<v #)#:#:#@#@#B    modmappingch[U5U;GaU[U5nURU5nURR5HnUR U5 M UR R5HnUR U5 M [UR5UR55Vs1sHofRiM nn[U5S::d SU35e[U5S:a[[U55OSnU(aURU5 U$U$s snf)a Swaps the module using from_dense according to the mapping passed in. Args: mod: input module mapping: a dictionary that maps from nn module to sparse nn module Return: The corresponding sparse module of `mod` according to mapping, created using from_dense zOswap_module only works with cpu or single-device CUDA modules, but got devices rN)r from_dense_forward_pre_hooksvaluesregister_forward_pre_hook_forward_hooksregister_forward_hookr parametersbuffersdevicelennextiterto) r#r$ sparse_modnew_mod pre_hook_fnhook_fnpdevicesr/s rr r s$C(G39#>? '',1188:K  - -k :;))002G  ) )' 23&+3>>+;S[[]%KL%K88%KL LA  g \]d\e f g (+G q(8d7m$d  JJv  Ms>D/modelprefixcXLagUR5H/up4[XAS5n[U[5(dM(X#-U-s $ g)zK Returns the fqn for a module or None if module not a descendent of model. .N)named_childrenr rstr)r:rr;namechildfqns rr r DsL ++- E3/ c3  =3& &. r"pathcZUS:wa$URS5Hn[XS5nM U$)z Given an fqn, returns the corresponding module or tensor or None if the fqn given by `path` doesn't correspond to anything. Similar to model.get_submodule(path) but works for tensors. r=r>N)splitgetattr)r:rDrAs rr r Ss.  rzJJsODE.E$ Lr" tensor_fqncxURS5SnUS[U5*SU;- n[X5nUUUUS.$)zP Uses tensor_fqn to obtain a dict containing module_fqn, module and tensor_name r>N) module_fqnr tensor_namerH)rFr0r )r:rHrLrKrs rr r ^sZ""3'+KEs;//3*3DEFJ 5 -F!"  r"c8^\rSrSrSrU4SjrSrSrSrU=r $)rsa#Parametrization for the weights. Should be attached to the 'weight' or any other parameter that requires a mask applied to it. Note:: Once the mask is passed, the variable should not change the id. The contents of the mask can change, but the mask reference itself should not. cF>[TU]5 URSU5 g)Nmask)super__init__register_buffer)selfrP __class__s rrRFakeSparsity.__init__~s  VT*r"clURRUR:XdeURU-$r)rPshape)rTxs rforwardFakeSparsity.forwards*yy!'')))yy1}r"c0$r)rTargskwargss r state_dictFakeSparsity.state_dicts  r"r]) __name__ __module__ __qualname____firstlineno____doc__rRrZr`__static_attributes__ __classcell__)rUs@rrrss+r"rN)r=) itertoolsrtypingrrtorchrtorch.nn.utils.parametrizerr__all__Moduletypeboolr dictr r@r r r rr]r"rrrs T "))d299oRV$ $!$ryy/4 ?"BC$YY$P8:  99  ii 14  c] ")),CHRYY