o p i@sddlmZddlZddlmZddlmZmZmZer4ddlm Z ddl m Z ddl m Z ddlmZGd d d ej ZdddZdS)) annotationsN) TYPE_CHECKING) distribution independent transform)Sequence)Tensor) Distribution) TransformcsZeZdZUdZded<ded<dfdd Zgfdd dZgfdddZdddZZ S)TransformedDistributiona Applies a sequence of Transforms to a base distribution. Args: base (Distribution): The base distribution. transforms (Sequence[Transform]): A sequence of ``Transform`` . Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> from paddle.distribution import transformed_distribution >>> d = transformed_distribution.TransformedDistribution( ... paddle.distribution.Normal(0., 1.), ... [paddle.distribution.AffineTransform(paddle.to_tensor(1.), paddle.to_tensor(2.))] ... ) >>> # doctest: +SKIP('random sample') >>> print(d.sample([10])) Tensor(shape=[10], dtype=float32, place=Place(cpu), stop_gradient=True, [ 3.22699189, 1.12264419, 0.50283587, 1.83812487, -2.00740123, -2.70338631, 1.26663208, 4.47909021, -0.11529565, 4.32719326]) >>> print(d.log_prob(paddle.to_tensor(0.5))) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, -1.64333570) >>> # doctest: -SKIP r baseSequence[Transform] transformsreturnNonecs\t|tjstdt|dt|tjs tdt|dtdd|Ds-tdt |}|j |j }||_ ||_ |sKt|j |j dSt|j |j |jjkrftd|jjdt|d|jjt|j kr|t||jjt|j }||j |j }|jjtt|j |jjd }t|dt|||t||ddS) Nz1Expected type of 'base' is Distribution, but got .zGExpected type of 'transforms' is Sequence[Transform] or Chain, but got css|] }t|tjVqdS)N) isinstancerr ).0trs/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/paddle/distribution/transformed_distribution.py Ksz3TransformedDistribution.__init__..z1All element of transforms must be Transform type.z.'base' needs to have shape with size at least z , bug got r)rrr TypeErrortypetypingrallrZChainTransformZ batch_shape event_shape_base _transformssuper__init__len_domain event_rank ValueErrorrZ IndependentZ forward_shape _codomainmax)selfr rchainZ base_shapeZtransformed_shapeZtransformed_event_rank __class__rrr @sN     z TransformedDistribution.__init__shape Sequence[int]rcC&|j|}|jD]}||}q |S)zSample from ``TransformedDistribution``. Args: shape (Sequence[int], optional): The sample shape. Defaults to []. Returns: [Tensor]: The sample result. )rsamplerforwardr'r+xrrrrr.m  zTransformedDistribution.samplecCr-)zReparameterized sample from ``TransformedDistribution``. Args: shape (Sequence[int], optional): The sample shape. Defaults to []. Returns: [Tensor]: The sample result. )rrsamplerr/r0rrrr3{r2zTransformedDistribution.rsamplevaluecCsd}|}t|j}t|jD]!}||}||jj|jj7}|t| |||jj}|}q|t|j ||t|j j7}|S)zThe log probability evaluated at value. Args: value (Tensor): The value to be evaluated. Returns: Tensor: The log probability. g) r!rreversedrZinverser"r#r%_sum_rightmostZforward_log_det_jacobianrlog_prob)r'r4r7yr#rr1rrrr7s   z TransformedDistribution.log_prob)r r rr rr)r+r,rr)r4rrr) __name__ __module__ __qualname____doc____annotations__r r.r3r7 __classcell__rrr)rr s -r r4rnintrcCs"|dkr|tt| dS|S)Nr)sumlistrange)r4r?rrrr6s"r6)r4rr?r@rr) __future__rrrZpaddle.distributionrrrcollections.abcrZpaddlerZ paddle.distribution.distributionr Zpaddle.distribution.transformr r r6rrrrs