3jDSSKJrJrJrJrJrJrJrJr SSK r SSK J r SSK J r J r SSKJr \"S5r"SS\5r"S S \ R&\S 9r"S S \ 5r"SS\\ 5rg))AnyCallableDictOptionalTupleTypeTypeVarUnionN)nn) DistributionUniform)MultiprocessWrapperTc8\rSrSrSrS\\S\S\S\4SjrSr g ) _PostInitInjectionMetaClasszMTo inject the ``__post_init__`` function after the creation of each instance.clsargskwargsreturncZ[R"U/UQ70UD6nUR5 U$N)type__call__ __post_init__)rrrobjs c/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/augmentation/random_generator/base.pyr$_PostInitInjectionMetaClass.__call__ s+mmC1$1&1  N) __name__ __module__ __qualname____firstlineno____doc__rrrr__static_attributes__r rrrrs*Wd1gcSQrrc ~^\rSrSr%SrSr\S\\R 4\ S'\R\ S'SU4Sjjr SSjr S\R4S\\R S\RSS4S jjrS \S \SS4S jrS\R S\RSS4S jrSS\\S4S\S\\\R.44SjjrSrU=r$)RandomGeneratorBase&z9Base class for generating random augmentation parameters.Ndevicedtyperc">[TU]5 gr)super__init__)self __class__s rr.RandomGeneratorBase.__init__,s rc$UR5 gr)set_rng_device_and_dtype)r/s rr!RandomGeneratorBase.__post_init__/s %%'rcUc[R"S5nURU:wdURU:waURX5 XlX lgg)zChange the random generation device and dtype. Note: The generated random numbers are not reproducible across different devices and dtypes. Ncpu)torchr*r+ make_samplersr/r*r+s rr3,RandomGeneratorBase.set_rng_device_and_dtype2sK >\\%(F ;;& DJJ%$7   v - KJ%8rrrc[RRR"U0UD6up4 nUR X4S9 U$)zUpdate sampler device and dtype using ``torch.nn.Module.to`` semantics. Args: *args: Positional arguments accepted by ``Module.to``. **kwargs: Keyword arguments accepted by ``Module.to``. Returns: This generator instance. r*r+)r7_C_nn _parse_tor3)r/rrr*r+_s rtoRandomGeneratorBase.toEs>$hhll44dEfEq! %%V%A rc[e)zCreate distribution samplers for the given device and dtype. Args: device: Target device. dtype: Target floating-point dtype. Raises: NotImplementedError: Subclass did not implement sampler creation. NotImplementedErrorr9s rr8!RandomGeneratorBase.make_samplersSs "!r batch_shape. same_on_batchc[e)aNSample random augmentation parameters. Args: batch_shape: Target batch shape. same_on_batch: If ``True``, use one sample and broadcast it across the batch dimension. Returns: Dictionary of tensors consumed by augmentation modules. Raises: NotImplementedError: Subclass did not implement parameter sampling. rD)r/rGrHs rforwardRandomGeneratorBase.forward_s "!rr<)rN)F)r!r"r#r$r%r*r strr7__annotations__r+r.rfloat32rr3rrAr8rintboolrTensorrJr& __classcell__)r0s@rr(r(&sC-1FE$U\\) *1 ;;( *."]]&{{  &  s /D  "ELL " " ""5c?"4"TXY\^c^j^jYjTk""rr() metaclassc\rSrSrSrSS\S\\\R/\R4SS4Sjjr S\ \ S 4S\R4S jr S\ \ S 4S\R4S jrS \ S\R4S jrS\S\4SjrSrg)DistributionWithMapperpaWraps a distribution with a value mapper function. This is used to restrict the output values of a given distribution by a value mapper function. The value mapper function can be functions like sigmoid, tanh, etc. Args: dist: the target distribution. map_fn: the callable function to adjust the output from distributions. Example: >>> from torch.distributions import Normal >>> import torch.nn as nn >>> # without mapper >>> dist = DistributionWithMapper(Normal(0., 1.,), map_fn=None) >>> _ = torch.manual_seed(0) >>> dist.rsample((8,)) tensor([ 1.5410, -0.2934, -2.1788, 0.5684, -1.0845, -1.3986, 0.4033, 0.8380]) >>> # with sigmoid mapper >>> dist = DistributionWithMapper(Normal(0., 1.,), map_fn=nn.Sigmoid()) >>> _ = torch.manual_seed(0) >>> dist.rsample((8,)) tensor([0.8236, 0.4272, 0.1017, 0.6384, 0.2527, 0.1980, 0.5995, 0.6980]) Ndistmap_fnrcXlX lgrrWrX)r/rWrXs rr.DistributionWithMapper.__init__s   r sample_shape.cURR[R"U55nURbUR U5nU$)zDraw a reparameterized sample and apply ``map_fn`` when provided. Args: sample_shape: Desired sample shape. Returns: Sample tensor after optional mapping. )rWrsampler7SizerXr/r\outs rr^DistributionWithMapper.rsamples>ii < 89 ;; "++c"C rcURR[R"U55nURbUR U5nU$)zDraw a sample and apply ``map_fn`` when provided. Args: sample_shape: Desired sample shape. Returns: Sample tensor after optional mapping. )rWsampler7r_rXr`s rrdDistributionWithMapper.samples>iiuzz,78 ;; "++c"C rncxURRU5nURbURU5nU$)zDraw ``n`` samples and apply ``map_fn`` when provided. Args: n: Number of samples. Returns: Sample tensor after optional mapping. )rWsample_nrX)r/rfras rrhDistributionWithMapper.sample_ns5ii  # ;; "++c"C rattrcf[X5$![a [URU5s$f=fr)getattrAttributeErrorrW)r/rjs r __getattr__"DistributionWithMapper.__getattr__s2 ,4& & ,499d+ + ,s 00rZr)r!r"r#r$r%r rrr7rQr.rrOr^rdrhrLrrnr&r rrrUrUps2\8Hell^UZUaUaEarwsh$NMM 5A CL$G""))/JG"TL,\L,^r-wrr