a h=1@spddlZddlmZddlmZddlZddlmZddlmZddl m Z ddl m Z dgZ Gd ddZdS) N)Optional) deprecated)Tensor) constraints) lazy_property)_size DistributioncseZdZdZdZdZdZeeddddZ e e dfe j e j e eddfd d Z d>ed d d Zee j dddZee j dddZeeeejfdddZee ejdddZeedddZeedddZeedddZeedddZe feedd d!Ze feedd"d#Ze d$e!d%e"ed&d'd(Z#eedd)d*Z$eedd+d,Z%eedd-d.Z&d?eed/d0d1Z'edd2d3Z(edd4d5Z)e fee j dd6d7Z*eddd8d9Z+d@d:d;Z,eddr r rsample_ns zDistribution.sample_ncCstdS)z Returns the log of the probability density/mass function evaluated at `value`. Args: value (Tensor): Nr/r*r r r rlog_probszDistribution.log_probcCstdS)z Returns the cumulative density/mass function evaluated at `value`. Args: value (Tensor): Nr/rAr r rcdfszDistribution.cdfcCstdS)z Returns the inverse cumulative density/mass function evaluated at `value`. Args: value (Tensor): Nr/rAr r ricdfszDistribution.icdf)r1r cCstdS)ar Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be `(cardinality,) + batch_shape + event_shape` (where `event_shape = ()` for univariate distributions). Note that this enumerates over all batched tensors in lock-step `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, `[[0], [1], ..`. To iterate over the full Cartesian product use `itertools.product(m.enumerate_support())`. Args: expand (bool): whether to expand the support over the batch dims to match the distribution's `batch_shape`. Returns: Tensor iterating over dimension 0. Nr/)r*r1r r renumerate_supportszDistribution.enumerate_supportcCstdS)z Returns entropy of distribution, batched over batch_shape. Returns: Tensor of shape batch_shape. Nr/r2r r rentropyszDistribution.entropycCst|S)z Returns perplexity of distribution, batched over batch_shape. Returns: Tensor of shape batch_shape. )r"exprFr2r r r perplexityszDistribution.perplexitycCs,t|tjst|}t||j|jS)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. )rr"r?rrr;r r r_extended_shape s  zDistribution._extended_shapec CsVt|tjstdt|t|j}||d|jkr\td|d|jd|}|j|j}tt |t |D]6\}}|dkr|dkr||krtd|d|dqz |j }Wn.t yt |jdd d YdS0|dusJ||}t|sRtd 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)rr"rr lensizerrzipreversedr3rrrrr!r#r r$r%r&r') r*r Zevent_dim_startZ actual_shapeZexpected_shapeijr3r-r r r_validate_samplesR       zDistribution._validate_samplecCsN|dur4t|j|jkr4td|jjd|jd|durJ|t|S|S)Nz Subclass z of zR that defines a custom __init__ method must also define a custom .expand() method.)r r)rrr$__new__)r*clsr0r r r_get_checked_instanceJs z"Distribution._get_checked_instancecsDfddjD}dfdd|D}jjd|dS)Ncsg|]\}}|jvr|qSr )r).0k_r2r r Sz)Distribution.__repr__..z, cs@g|]8}|dj|dkr*j|n j|qS)z: rK)rZnumelrM)rVpr2r rrYUs())rrjoinrr$)r*Z param_names args_stringr r2r__repr__Rs zDistribution.__repr__)N)T)N)/r$ __module__ __qualname____doc__Z has_rsampleZhas_enumerate_supportr staticmethodboolrr"r?rr)rr1propertyrrdictstrr Constraintrr3rr4r5r6r8r<r:r FutureWarningintr@rBrCrDrErFrHrIrRrUr` __classcell__r r r.rrsb '      2 )rtypingrZtyping_extensionsrr"rZtorch.distributionsrZtorch.distributions.utilsrZ torch.typesr__all__rr r r rs