קg[0zddlZddlmZmZmZddlmZddlZddlm Z ddl m Z ddl m Z dgZGddZdS) N)AnyDictOptional) deprecated) constraints) lazy_property)_size DistributionceZdZdZdZdZdZededdfdZ e j e j dfde j d e j d e effd Z d'defd Zede j fd Zede j fdZedeeejffdZede efdZede jfdZede jfdZede jfdZede jfdZe j fdede jfdZe j fdede jfdZ e!de"de#de jfdZ$de jde jfdZ%de jde jfdZ&de jde jfdZ'd(dede jfd Z(de jfd!Z)de jfd"Z*e j fdede j fd#Z+de jddfd$Z,d'd%Z-defd&Z.xZ/S))r zP Distribution is the abstract base class for probability distributions. FTvaluereturnNc4|dvrt|t_dS)a Sets whether validation is enabled or disabled. The default behavior mimics Python's ``assert`` statement: validation is on by default, but is disabled if Python is run in optimized mode (via ``python -O``). Validation may be expensive, so you may want to disable it once a model is working. Args: value (bool): Whether to enable validation. )TFN) ValueErrorr _validate_args)r s \/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributions/distribution.pyset_default_validate_argsz&Distribution.set_default_validate_argss#  % % &+ ### batch_shape event_shape validate_argsc||_||_|||_|jr> |j}n4#t$r'i}t j|jddzdzYnwxYw|D]\}}tj |r||j vr1ttt||trTt||}||}|s_t%d|dt|jdt)|jdt-|dt-|d | t/dS) Nz$ does not define `arg_constraints`. zAPlease set `arg_constraints = {}` or initialize the distribution z2with `validate_args=False` to turn off validation.zExpected parameter z ( of shape z) of distribution z to satisfy the constraint , but found invalid values: ) _batch_shape _event_shaperarg_constraintsNotImplementedErrorwarningswarn __class__itemsr is_dependent__dict__ isinstancegetattrtypercheckallr__name__tupleshapereprsuper__init__) selfrrrrparam constraintr validr s rr.zDistribution.__init__)s ('  $"/D     "&"6&   "$ ~KKKYZJK &5%:%:%<%<  !z+J77 --*DJJ.. 33-e,,"((//yy{{$>e>> KK0>>>+/::>>6:*5E5E>>7< >> s*.AAct)a/ Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to `batch_shape`. This method calls :class:`~torch.Tensor.expand` on the distribution's parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in `__init__.py`, when an instance is first created. Args: batch_shape (torch.Size): the desired expanded size. _instance: new instance provided by subclasses that need to override `.expand`. Returns: New distribution instance with batch dimensions expanded to `batch_size`. r)r/r _instances rexpandzDistribution.expandPs &"!rc|jS)zF Returns the shape over which parameters are batched. )rr/s rrzDistribution.batch_shapee   rc|jS)zJ Returns the shape of a single sample (without batching). )rr8s rrzDistribution.event_shapelr9rct)a  Returns a dictionary from argument names to :class:`~torch.distributions.constraints.Constraint` objects that should be satisfied by each argument of this distribution. Args that are not tensors need not appear in this dict. r4r8s rrzDistribution.arg_constraintss "!rct)z Returns a :class:`~torch.distributions.constraints.Constraint` object representing this distribution's support. r4r8s rsupportzDistribution.support} "!rct)z7 Returns the mean of the distribution. r4r8s rmeanzDistribution.mean "!rc0t|jd)z7 Returns the mode of the distribution. z does not implement mode)rr r8s rmodezDistribution.modes "T^"M"M"MNNNrct)z; Returns the variance of the distribution. r4r8s rvariancezDistribution.variancerBrc4|jS)zE Returns the standard deviation of the distribution. )rFsqrtr8s rstddevzDistribution.stddevs }!!###r sample_shapectj5||cdddS#1swxYwYdS)z Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched. N)torchno_gradrsampler/rJs rsamplezDistribution.samples ]__ . .<< -- . . . . . . . . . . . . . . . . . .s 6::ct)z Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. r4rOs rrNzDistribution.rsampler?rz=`sample_n(n)` will be deprecated. Use `sample((n,))` instead.)categoryncR|tj|fS)zq Generates n samples or n batches of samples if the distribution parameters are batched. )rPrLSize)r/rSs rsample_nzDistribution.sample_ns"{{5:qd++,,,rct)z Returns the log of the probability density/mass function evaluated at `value`. Args: value (Tensor): r4r/r s rlog_probzDistribution.log_probr<rct)z Returns the cumulative density/mass function evaluated at `value`. Args: value (Tensor): r4rXs rcdfzDistribution.cdfr<rct)z Returns the inverse cumulative density/mass function evaluated at `value`. 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Returns: Tensor of shape batch_shape. r4r8s rentropyzDistribution.entropys "!rcNtj|S)z Returns perplexity of distribution, batched over batch_shape. Returns: Tensor of shape batch_shape. )rLexprar8s r perplexityzDistribution.perplexitysy(((rct|tjstj|}tj||jz|jzS)ax Returns the size of the sample returned by the distribution, given a `sample_shape`. Note, that the batch and event shapes of a distribution instance are fixed at the time of construction. If this is empty, the returned shape is upcast to (1,). Args: sample_shape (torch.Size): the size of the sample to be drawn. )r$rLrUrrrOs r_extended_shapezDistribution._extended_shapesG, 33 4 :l33Lz,)::T=NNOOOrc t|tjstdt |t |jz }||d|jkr-td|d|jd|}|j|jz}tt|t|D]-\}}|dkr"|dkr||krtd|d|d. |j }n3#t$r&tj |jdd zd zYdSwxYw|J||}|s\td t#|jd t'|jd t+|dt+|d| dS)a Argument validation for distribution methods such as `log_prob`, `cdf` and `icdf`. The rightmost dimensions of a value to be scored via these methods must agree with the distribution's batch and event shapes. Args: value (Tensor): the tensor whose log probability is to be computed by the `log_prob` method. Raises ValueError: when the rightmost dimensions of `value` do not match the distribution's batch and event shapes. z/The value argument to log_prob must be a TensorNz5The right-most size of value must match event_shape: z vs .z9Value is not broadcastable with batch_shape+event_shape: z% does not define `support` to enable z;sample validation. Please initialize the distribution with z-`validate_args=False` to turn off validation.zExpected value argument (rz) to be within the support (z) of the distribution r)r$rLTensorrlensizerrzipreversedr>rrrr r'r(r&r)r*r+r,) r/r event_dim_start actual_shapeexpected_shapeijr>r2s r_validate_samplezDistribution._validate_samplesL%.. PNOO Oejjll++c$2C.D.DD ::<<(( )T-> > >n nnZ^Zknnn zz|| *T->>..0H0HII  DAqAvv!q&&Q!VV sP\ssbpsss lGG"    M>HHHOPAB    FF  """ e$$yy{{ 6KK(6649%+4F4F66-1']]66(,Dzz66/4 66   s.D66,E&%E&c|Bt|j|jkr%td|jjd|jd|"|t|n|S)Nz Subclass z of zR that defines a custom __init__ method must also define a custom .expand() method.)r&r.rr r)__new__)r/clsr5s r_get_checked_instancez"Distribution._get_checked_instanceDs|  d!4 !D!D%>DN3>>>>> ,5+z)Distribution.__repr__..Ms)XXXTQQ$-EWEWqEWEWEWrz, cg|]V}|dj|dkr j|nj|WS)z: ri)r#numelrl)r|pr/s rrz)Distribution.__repr__..Ost   hhDM!,<,B,B,D,D,I,I a((t}]^O_OdOdOfOfhh   r())rr!joinr r))r/ param_names args_strings` r__repr__zDistribution.__repr__LsXXXXT%9%?%?%A%AXXX ii    $     ~&,{:S@@r)N)T)0r) __module__ __qualname____doc__ has_rsamplehas_enumerate_supportr staticmethodboolrrLrUrr.r r6propertyrrrstrr Constraintrrr>rjrArDrFrIrPrNr FutureWarningintrVrYr[r]r_rardrfrtrxr __classcell__)r s@rr r smK!N,,$,,,\,$#-%*,,",%*,,(, %%Z%Z% ~ %%%%%%N""%""""*!UZ!!!X! !UZ!!!X! 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