a MSic@sddlmZddlmZddlZddlmmZddl m Z m Z ddl m Z dZddZd d Zd d ZdddZddZdddZGdddZGdddeeZdddZdddZdS) )update_wrapper)NumberN)DictAnyis_tensor_likegox?cstdd|Dstdtdd|Dsttd|D]$}t|tjr>t|j|jdqdq>fdd|D}tj |Stj |S) a Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`. - numbers.Number instances (scalars) are upcast to tensors having the same size and type as the first tensor passed to `values`. If all the values are scalars, then they are upcasted to scalar Tensors. Args: values (list of `numbers.Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `numbers.Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ css |]}t|pt|tVqdSN)r isinstancer.0vr U/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/distributions/utils.py sz broadcast_all..zqInput arguments must all be instances of numbers.Number, torch.Tensor or objects implementing __torch_function__.css|]}t|VqdSrrr r r rr)dtyperdevicecs*g|]"}t|r|ntj|fiqSr )rtorchtensorr optionsr r %sz!broadcast_all..) all ValueErrordictrget_default_dtyper Tensorrrbroadcast_tensors)valuesvalue new_valuesr rr broadcast_all s   r"cCsBtjr.ttj|||dtj|||dStj|||dS)Nr)r_C_get_tracing_statenormalzerosonesemptynormal_)shaperrr r r_standard_normal+s  r+cCs0|dkr |S|jd| d}||dS)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)r,)r*reshapesum)r dimZrequired_shaper r r_sum_rightmost3sr0FcCs|rt|Stj|ddS)a Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. r,)r/)rsigmoidFsoftmax)logits is_binaryr r rlogits_to_probsAs r6cCs t|jj}|j|d|dS)N)minmax)rfinforepsclamp)probsr;r r r clamp_probsMsr>cCs,t|}|r"t|t| St|S)a$ Converts a tensor of probabilities into logits. For the binary case, this denotes the probability of occurrence of the event indexed by `1`. For the multi-dimensional case, the values along the last dimension denote the probabilities of occurrence of each of the events. )r>rloglog1p)r=r5Z ps_clampedr r rprobs_to_logitsRsrAc@s"eZdZdZddZdddZdS) lazy_propertyz Used as a decorator for lazy loading of class attributes. This uses a non-data descriptor that calls the wrapped method to compute the property on first call; thereafter replacing the wrapped method into an instance attribute. cCs||_t||dSr)wrappedrselfrCr r r__init__fszlazy_property.__init__NcCsX|durt|jSt||}Wdn1s:0Yt||jj||Sr)_lazy_property_and_propertyrCr enable_gradsetattr__name__)rEinstanceZobj_typer r r r__get__js   (zlazy_property.__get__)N)rJ __module__ __qualname____doc__rFrLr r r rrB_srBc@seZdZdZddZdS)rGzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks cCs t||Sr)propertyrFrDr r rrFysz$_lazy_property_and_property.__init__N)rJrMrNrOrFr r r rrGssrGcCs|jd}tjsH|| ks&||krHtd|d| d|ddtj||jd}||dd|dk}|d|f}|S) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. r,zdiag (z) provided is outside [z, r7z].r.)r*rr#r$rarangerview)matdiagnrR tril_maskvecr r rtril_matrix_to_vec}s " rYcCsdd| dd|dd|jddt||ddd}t|jj}tjst|||krt d|jddd t |tj rt| nt|}| |jd dt||f}tj||jd }||dd|dk}||d |f<|S) z Convert a vector or a batch of vectors into a batched `D x D` lower triangular matrix containing elements from the vector in row order. r7r,g?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.NrQ.)r*absrr:rr;r#r$roundrr rlong new_zerosSizerRrrS)rXrUrVr;rTrRrWr r rvec_to_tril_matrixsH"" rb)F)F)r)r) functoolsrnumbersrrtorch.nn.functionalnn functionalr2typingrrtorch.overridesrZeuler_constantr"r+r0r6r>rArBrPrGrYrbr r r rs