zj!.ddlmZddlZddlmZddlZddlZddlmZddl m Z m Z ddl m Z ddlmZmZerddlmZdd lmZdd lmZdd lmZmZGd d ZdS)) annotationsN) TYPE_CHECKING)_C_ops)check_variable_and_dtype convert_dtype)Variable)in_dynamic_or_pir_mode in_pir_mode)Sequence) TypeGuard)Tensor)NestedNumericSequence TensorLikeceZdZdZ d,d-fd Zed.d Zed.d Zed/d Zed/d Z gfd0dZ gfd0dZ d/dZ d1dZ d2dZd2dZd2dZd3dZd4dZd5d Zd6d"Z d7d8d(Z d7d9d*Zd5d+ZxZS): Distributiona> The abstract base class for probability distributions. Functions are implemented in specific distributions. Args: batch_shape(Sequence[int], optional): independent, not identically distributed draws, aka a "collection" or "bunch" of distributions. event_shape(Sequence[int], optional): the shape of a single draw from the distribution; it may be dependent across dimensions. For scalar distributions, the event shape is []. For n-dimension multivariate distribution, the event shape is [n]. batch_shape Sequence[int] event_shapereturnNonect|tr|nt||_t|tr|nt||_t dSN) isinstancetuple _batch_shape _event_shapesuper__init__)selfrr __class__s p/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/distribution/distribution.pyrzDistribution.__init__6sw +u-- $KK{## +u-- $KK{##  c|jS)zeReturns batch shape of distribution Returns: Sequence[int]: batch shape )rr s r"rzDistribution.batch_shapeF   r#c|jS)zeReturns event shape of distribution Returns: Sequence[int]: event shape )rr%s r"rzDistribution.event_shapeOr&r#r ct)zMean of distributionNotImplementedErrorr%s r"meanzDistribution.meanX "!r#ct)zVariance of distributionr)r%s r"variancezDistribution.variance]r,r#shapect)zSampling from the distribution.r)r r/s r"samplezDistribution.sampleb!!r#ct)zreparameterized sampler)r1s r"rsamplezDistribution.rsamplefr3r#ct)z The entropy of the distribution.r)r%s r"entropyzDistribution.entropyjr3r#otherct)z7The KL-divergence between self distributions and other.r))r r8s r" kl_divergencezDistribution.kl_divergencenr3r#valuecP||S)zProbability density/mass function evaluated at value. Args: value (Tensor): value which will be evaluated )log_probexpr r;s r"probzDistribution.probrs" }}U##'')))r#ct)z&Log probability density/mass function.r)r?s r"r=zDistribution.log_probzr3r#ct)zProbability density/mass function. Note: This method will be deprecated in the future, please use `prob` instead. r)r?s r"probszDistribution.probs~s "!r# sample_shapeSequence[int] | Tensorctt|t|jzt|jzS)zcompute shape of the sample Args: sample_shape (Sequence[int]|Tensor): sample shape Returns: Tensor: generated sample data shape )rrr)r rDs r" _extend_shapezDistribution._extend_shapes: ,  D%&& 'D%&& ' r#args"TensorLike | NestedNumericSequenceTypeGuard[Tensor]cd}d}|D]-}t|ttjjfrd}+d}.|r|rt d|S)z Argument validation for distribution args Args: value (float, list, numpy.ndarray, Tensor) Raises ValueError: if one argument is Tensor, all arguments should be Tensor FTz9if one argument is Tensor, all arguments should be Tensor)rrpaddlepirValue ValueError)r rH is_variable is_numberargs r"_validate_argszDistribution._validate_argsss   ! !C#&**:;<< !"   9 K r#tuple[Tensor, ...]c g}g}d}|D]}t|ttttjt tjj fstdt|t|tjj r| |t j |}|j}t|dkr>Y..M{ y11,C   f % % % %  / /C#vz/00 $$S)))!#!4S#!>!> OQ}} H%|O,ABB %}:::GG M/< 8 8 8   . . . .]###r#paramctj|r||jSt rxt rt |dddgd|j|jkrDt|jdvr.tj dtj ||jS|St |dddgd|j|jkr/tj dtj ||jS|S)a Log_prob and probs methods have input ``value``, if value's dtype is different from param, convert value's dtype to be consistent with param's dtype. Args: param (Tensor): low and high in Uniform class, loc and scale in Normal class. value (Tensor): The input tensor. Returns: value (Tensor): Change value's dtype if value's dtype is different from param. r;rWrXr=)rWrXztdtype of input 'value' needs to be the same as parameters of distribution class. dtype of 'value' will be converted.rY) rL is_complexrfrZr r rrrdrercast)r rur;s r"_check_values_dtype_in_probsz)Distribution._check_values_dtype_in_probss5  U # # -<< ,, , ! # # }} (7Y $:J{ek))mEK.H.HM// K{5%+666L    "     ;%+ % % MG   ;uEK888 8 r#FrCfloat | Tensor is_binaryboolc|r*tj|tj| z ntj|S)a Converts probabilities into logits. For the binary, probs denotes the probability of occurrence of the event indexed by `1`. For the multi-dimensional, values of last axis denote the probabilities of occurrence of each of the events. )rLloglog1p)r rCr{s r"_probs_to_logitszDistribution._probs_to_logitss> #VZ  uf!5!5 5 5E"" r#logitsc|r$tjj|n%tjj|dS)z Converts logits into probabilities. For the binary, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities of the events. )axis)rLnn functionalsigmoidsoftmax)r rr{s r"_logits_to_probszDistribution._logits_to_probs(sD ?FI ( ( 0 0 0%--f2->> r#c |D]d}t|ttttjt tjj fstdt|etd|Dsitj |D]1}t|t tjj fr |jn2fd|D}tj|Stj|S)a Returns a list where each arg is broadcasted. Scalar args are upcast to tensors having the same data type as the first Tensor passed to `args`. If all the args are scalars, then they are upcasted to Tensors with paddle default data type. Args: value (float, list, numpy.ndarray, Tensor) Returns: Broadcasted Tensor of args. rVc3bK|]*}t|ttjjfV+dSr)rrrLrMrN).0rRs r" z.Distribution._broadcast_all..LsG  >AJsXvz'78 9 9      r#cg|]@}t|ttjjfr|ntj|AS)rY)rrrLrMrN to_tensor)rrRrZs r" z/Distribution._broadcast_all..Ts[ "#&*2B'CDD#?@@IEE  H+H55 5'---r#)rr)rrrrrr)rr)rr )r/rrr )r8rrr )r;r rr )rDrErr )rHrIrrJ)rHrIrrT)rur r;r rr )F)rCrzr{r|rr )rrzr{r|rr )__name__ __module__ __qualname____doc__rpropertyrrr+r.r2r5r7r:r@r=rCrGrSrtryrrr __classcell__)r!s@r"rr(s  MO !!!X!!!!X!"""X""""X"-/""""".0"""""""""""""****""""""""     29$9$9$9$v,,,,^8=      9>      '.'.'.'.'.'.'.'.r#r) __future__rrdtypingrnumpyr]rLrpaddle.base.data_feederrrpaddle.base.frameworkrpaddle.frameworkr r collections.abcr typing_extensionsr r paddle._typingrrrrr#r"rsB#"""""  KKKKKKKK****** A((((((++++++@@@@@@@@u.u.u.u.u.u.u.u.u.u.r#