sh%:SSKJrJr SSKrSSKJr SSKJr SSKJ r J r J r SSK J r JrJr /SQr"SS\R"R$R&5r"S S \\R(5r"S S \\R*5r"S S\\R,5rg))ClassVarUnionN) _FusedModule) _size_1_t _size_2_t _size_3_t)_pair_single_triple)Conv1dConv2dConv3dc\rSrSr%\\\RRR\ S'SS\ S\ S\ \ S4S\ \ S4S \ \\ \ S44S \ \ S4S \S \ \ S4S \ S\S\SS4SjjrSr\SSj5rSrSrg)_ConvNd _FLOAT_MODULEN in_channels out_channels kernel_size.stridepaddingdilation transposedoutput_paddinggroupsbias padding_modereturncXS.n[RRRR"UUUUUUUUUU U U 4 0UD6 U (dS5eXlU R US9Ulg)N)devicedtypez'qconfig must be provided for QAT module)factory_kwargs)nnmodulesconvr__init__qconfigweightweight_fake_quant)selfrrrrrrrrrrrr'r r!r"s p/Users/tiagomarins/Projetos/claudeai/copy_bank/venv/lib/python3.13/site-packages/torch/ao/nn/qat/modules/conv.pyr&_ConvNd.__init__s~"%+; ((              AAAw !(~!NclURXRUR5UR5$N _conv_forwardr)r(rr*inputs r+forward_ConvNd.forward6(!!%)?)? )LdiiXXr-c l[U5UR:Xd.SUR-S-URR-5e[US5(dS5eUR(dS5e[ [U5[ 5(aUSnURnU"URURURURURURURURSLURUS9 nUR UlURUlU$) zCreate a qat module from a float module Args: `mod`: a float module, either produced by torch.ao.quantization utilities or directly from user zqat.z.from_float only works for r'z,Input float module must have qconfig definedz,Input float module must have a valid qconfigrN)rrrrrrr')typer__name__hasattrr' issubclassrrrrrrrrrrr()clsmoduse_precomputed_fake_quantr'qat_convs r+ from_float_ConvNd.from_float9sCyC--- ll + ,(( ) - sI&&V(VV&{{JJJ{ d3i . .a&C++ OO    OO::KK\\::%))  ** r-c [U5nURURURURUR UR URURURSLUR5 n[RRURR55UlURb<[RRURR55Ul [!U["5(acU/n[%US5(deUR'5nUR)U5 UR*"U6nUR-UR.5 U$U$)zzThis works for both single qat conv, and the qat conv - relu modules to convert the qat module to a floating point module N_FLOAT_RELU_MODULE)r8_FLOAT_CONV_MODULErrrrrrrrrtorchr# Parameterr(detachr;rr:rCappendrtraintraining)r*r<r%r$relufuseds r+to_float_ConvNd.to_float\s'4j%%          KK LL MM KK IIT !     hh((););)=> 99 **499+;+;+=>DI c< ( (fG3 455 55))+D NN4 %%w/E KK &LKr-)r'r))NNNF)r9 __module__ __qualname____firstlineno__rr8r#r$r%r__annotations__inttuplerstrboolr&r4 staticmethodr@rM__static_attributes__r-r+rrsD!8!89::#O#O#O38_ #O c3h #O sE#s(O+, #OS/#O#Oc3h#O#O#O#O !#OJY  Dr-rc^\rSrSr%Sr\R r\\ \R \ S'\R r \\ \R \ S'SS\ S\ S\ S \ S \\\ 4S \ S \ S \S\SS4U4Sjjjr\SU4Sjj5rSrU=r$)r |a: A Conv1d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as :class:`~torch.nn.Conv1d` Similar to :class:`~torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight rrDNrrrrrrrrrrc >[U5n [U5n[U[5(aUO [U5n[U5n[TU]UUU UUUS[S5UUU U U U S9 gNFr) rrrrrrrrr'r r!)r isinstancerVsuperr&r*rrrrrrrrrr'r r! kernel_size_stride_padding_ dilation_ __class__s r+r&Conv1d.__init__z{+ &/(#667GG[TU]XUS9$N)r>r`r@r<r=r>rfs r+r@Conv1d.from_float w! 1K"  r-rZ rrprpTzerosNNNrO)r9rPrQrR__doc__r#r rrr8rSrDrTrrrVrWr& classmethodr@rY __classcell__rfs@r+r r |s 02yyM8DO,846IIbii1=)*#" " "  "  " sI~& " " " " "  " " H  r-r c^\rSrSr%Sr\R r\\ \R \ S'\R r \\ \R \ S'SS\ S\ S\ S \ S \\\ 4S \ S \ S \S\SS4U4SjjjrSr\SU4Sjj5rSrU=r$)r a A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv2d`, please see https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d for documentation. Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight rrDNrrrrrrrrrrc >[U5n [U5n[U[5(aUO [U5n[U5n[TU]UUU UUUS[S5UUU U U U S9 gr^)r r_rVr`r&ras r+r&Conv2d.__init__sx[) -(#667E'N(O      8%  r-clURXRUR5UR5$r/r0r2s r+r4Conv2d.forwardr6r-c >[TU]XUS9$rjrkrls r+r@Conv2d.from_floatrnr-rZrorO)r9rPrQrRrrr#r rrr8rSrDrTrrrVrWr&r4rsr@rYrtrus@r+r r  02yyM8DO,846IIbii1=)*#" " "  "  " sI~& " " " " "  " " HY  r-r c^\rSrSr%Sr\R r\\ \R \ S'\R r \\ \R \ S'SS\ S\ S\ S \ S \\\ 4S \ S \ S \S\SS4U4SjjjrSr\SU4Sjj5rSrU=r$)ra A Conv3d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv3d`, please see https://pytorch.org/docs/stable/nn.html?highlight=conv3d#torch.nn.Conv3d for documentation. Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight rrDNrrrrrrrrrrc >[U5n [U5n[U[5(aUO [U5n[U5n[TU]UUU UUUS[S5UUU U U U S9 gr^)r r_rVr`r&ras r+r&Conv3d.__init__ rhr-clURXRUR5UR5$r/r0r2s r+r4Conv3d.forward-r6r-c >[TU]XUS9$rjrkrls r+r@Conv3d.from_float0rnr-rZrorO)r9rPrQrRrrr#rrrr8rSrDrTrrrVrWr&r4rsr@rYrtrus@r+rrr~r-r)typingrrrEtorch.nnr#torch.ao.nn.intrinsicrtorch.nn.common_typesrrrtorch.nn.modules.utilsr r r __all__r$r%rr r rrZr-r+rsy" .AA:: )kbjjoo%%k\8 Wbii8 v= Wbii= @= Wbii= r-