o * i5@sddlmZddlZddlZddlmZddlmZddlm Z m Z ddl Z ddl m Z mZddlmZddlmZdd lmZdd lmZe rTdd l mZdd lmZd gZddZdddZdddZ d ddZdd!ddZGdd d ej Z!dS)") annotationsN)Sequence)reduce) TYPE_CHECKINGLiteral) check_type convert_dtype)Variable) distribution)Beta)in_dynamic_mode)Tensor) _DTypeLiteral LKJCholeskycCsptjtj|jd}tjd|d|jd}t|dd|d}tj|dd}||ddt ||S)zW Computes the multivariate log gamma function for input `a` and dimension `p`. dtypeaxis) paddleZ to_tensormathpirarangelgamma unsqueezesumlog)aprjZ gammaln_termsZ gammaln_sumr#l/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/distribution/lkj_cholesky.pymvlgamma%s r%cCsBtj||fdd}tj||d}tj|dd\}}||fS)zk Returns the indices of the lower triangular part of an n x n matrix, including the k-th diagonal. Zint32rdiagonalT)as_tuple)rZonestrilZnonzeroflatten)nkZ full_matrixZ tril_matrixrowscolsr#r#r$ tril_indices0sr/cCs*tjt||d}t||d}|S)z| Extracts the lower triangular part of the input matrix or batch of matrices `x`, including the specified diagonal. r&bool)rr)Z ones_like masked_selectZastype)xr' tril_maskZ tril_elementsr#r#r$matrix_to_tril:sr4r#cCs||}g||ttj|d||R}tt|t|\}} || k} tj|| | | gdd} tj| |dd} t|d |dg} tj | | gdd}t |dddf}||}tj |||f|j d}t||||}|S)zX Constructs a batch of lower triangular matrices from a given input tensor `p`. rrrNshaper)roperatormulrmeshgridrstackZrepeat_interleaverZtileconcatZargsortzerosrZscatter_nd_addreshape)Z p_flattendimlast_dim flatten_shape sample_shapediagZshape0 output_shaper-r.maskZ lower_indicesZrepeated_lower_indicesZindex0Z index_matrixZsorted_indicesmatrixr#r#r$vec_to_tril_matrixCs2rFmatr rBintreturnc Cs|jdd}|jd}|| ks||kr&td|d| d|ddtt|t|\}}|||k}||||dd }||f7}t||j}t|||}|S) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. Nrzdiag (z) provided is outside [z, rz].r)r6 ValueErrorrr9r broadcast_tor1r=) rGrBZ out_shaper+r-r.r3Zvec_lenZvecr#r#r$tril_matrix_to_vecjs "  rMcsxeZdZUdZded<ded<ded<ded < ddfdd ZdddZdddZgfdddZd ddZ Z S)!rab The LKJCholesky class represents the LKJ distribution over Cholesky factors of correlation matrices. This class implements the LKJ distribution over Cholesky factors of correlation matrices, as described in Lewandowski, Kurowicka, and Joe (2009). It supports two sampling methods: "onion" and "cvine". Args: dim (int): The dimension of the correlation matrices. concentration (float, optional): The concentration parameter of the LKJ distribution. Default is 1.0. sample_method (str, optional): The sampling method to use, either "onion" or "cvine". Default is "onion". Example: .. code-block:: python >>> import paddle >>> dim = 3 >>> lkj = paddle.distribution.LKJCholesky(dim=dim) >>> sample = lkj.sample() >>> sample.shape [3, 3] r concentrationrrrHr>Literal['onion', 'cvine'] sample_methodr?onionfloatrINonec stst|dtttjjfdt|dttt ttjjfd| |r,||_ t |j |_ n ||\|_ t|_ ||_|jdksHtd|dt|jtsVtd|dtret|j dksetd ||_|j j}||f}|j d |jd}tj|jd |j j d }|d krttjd|j d |g}|d }|dd |} t|| |_n(|dkrttd ||jd |jd g} |d| } t| | |_ntdt ||dS)Nr>rrNrz3Expected dim greater than or equal to 2. Found dim=.z)Expected dim to be an integer. Found dim=rz,The arg of `concentration` must be positive.?rrrR)rrZcvinez-`method` should be one of 'cvine' or 'onion'.)!r rrHr rZpirValuerSlisttupleZ_validate_argsrNrrZ _to_tensorZget_default_dtyper>rK isinstance TypeErrorallrPr6rr;r<rr _betar4rLsuper__init__) selfr>rNrPZ batch_shapeZ event_shapeZ marginal_concoffsetZ beta_conc1Z beta_conc0Z offset_trilZ beta_conc __class__r#r$r_sf      zLKJCholesky.__init__rA Sequence[int]c Cs|j|d}tj|||jdd}||jddd}|dddddf}gt |j ddd|j R}tj ||jd }tj ||gdd }t||}t|jj} tjdtj|d dd | d } |t| 7}|S) zGenerate a sample using the "onion" method. Args: sample_shape (tuple): The shape of the samples to be generated. Returns: w (Tensor): The Cholesky factor of the sampled correlation matrix. rrT)rZkeepdim.rNrJr5rr)min)r]samplerrZrandnZ _extend_shaperr)ZnormrYr6r>r<r;sqrtfinfotinycliprZ diag_embed) r`rAyZu_normalZ u_hypersphereZu_hypersphere_otherZ zero_shapeZzero_rowweps diag_elemsr#r#r$_onions   "$zLKJCholesky._onionc CsB|j|d}d|d}|jdkr|d}|j|jdd}|ttj|d}t|jj dkr<||jj d9}| |f}t ||j|||d}t |jj}t j|d|d|d}|d}t jt d|dd} ddg| jdddg} t jjj| dd df| d d d } |t |j d|j d7}|| }|S) zGenerate a sample using the "cvine" method. Args: sample_shape (tuple): The shape of the samples to be generated. Returns: r (Tensor): The Cholesky factor of the sampled correlation matrix. rrrrJr)remax)r>.NZconstantrQ)padmodevalue)r]rfrr>rr7r8lenrNr6r=rFrrhrrirjZcumprodrgndimnnZ functionalrqeye) r`rAZ beta_sampleZpartial_correlationr?r@rmrzZz1m_cumprod_sqrtZ pad_widthZz1m_cumprod_sqrt_shiftedr#r#r$_cvinesB     zLKJCholesky._cvinecCsdt|ts td|jdkr||}n||}t|}||jj ||j |j g| |S)z6Generate a sample using the specified sampling method.z%sample shape must be Sequence object.rR) rZrr[rProrzrXextendrNr6r>r=)r`rAresrCr#r#r$rf?s     zLKJCholesky.samplersc Cstj|dddddddf}tjd|jd|jjd }d|jdd|j|}tj|t|dd }|jd}|jd |}t ||}t |d |}d |t t j } | ||} || S) a}Compute the log probability density of the given Cholesky factor under the LKJ distribution. Args: value (Tensor): The Cholesky factor of the correlation matrix for which the log probability density is to be computed. Returns: log_prob (Tensor): The log probability density of the given Cholesky factor under the LKJ distribution. rrrJ)raZaxis1Zaxis2.rNrrrrV) rr'rr>rNrrrrrr%rr) r`rsrnorderZunnormalized_log_pdfZdm1alpha denominator numeratorZ pi_constantZnormalize_termr#r#r$log_probOs   zLKJCholesky.log_prob)rrQrR)r>rHrNrSrPrOrIrT)rArdrIr )rsr rIr ) __name__ __module__ __qualname____doc____annotations__r_rorzrfr __classcell__r#r#rbr$rs  D &9)r)r#r)rGr rBrHrIr )" __future__rrr7collections.abcr functoolsrtypingrrrZpaddle.base.data_feederrrZpaddle.base.frameworkr Zpaddle.distributionr Zpaddle.distribution.betar Zpaddle.frameworkr r Zpaddle._typing.dtype_liker__all__r%r/r4rFrM Distributionrr#r#r#r$s.           '