a h@sdZddlZddlZddlmZddZGdddeeZGd d d e e Z d d Z d dZ d'ddZd(ddZddZddZddZddddZddZdd Zd!d"Zd)d#d$Zd%d&ZdS)*zLAssorted utilities, which do not need anything other then torch and stdlib. N) _dtypes_implcCs4t|trdSz t|Wnty.YdS0dS)NFT) isinstancestrlen Exception)seqr @/var/www/auris/lib/python3.9/site-packages/torch/_numpy/_util.py is_sequences   r c@s eZdZdS) AxisErrorN__name__ __module__ __qualname__r r r r r sr c@s eZdZdS)UFuncTypeErrorNr r r r r rsrcCs |dur|j|kr||}|SN)dtypeto)tensorrr r r cast_if_needed s rcCs$t|jdkr |tj}|S)N)rZ _categoryrrZdefault_dtypesZ float_dtype)xr r r cast_int_to_float'srcCs@| |kr|ks,ntd|d||dkr<||7}|S)Nzaxis z) is out of bounds for array of dimension r)r )axndimargnamer r r normalize_axis_index/s rFcst|ttfvr4zt|g}Wnty2Yn0tfdd|D}|stttt |t|krrt ddnt d|S)a Normalizes an axis argument into a tuple of non-negative integer axes. This handles shorthands such as ``1`` and converts them to ``(1,)``, as well as performing the handling of negative indices covered by `normalize_axis_index`. By default, this forbids axes from being specified multiple times. Used internally by multi-axis-checking logic. Parameters ---------- axis : int, iterable of int The un-normalized index or indices of the axis. ndim : int The number of dimensions of the array that `axis` should be normalized against. argname : str, optional A prefix to put before the error message, typically the name of the argument. allow_duplicate : bool, optional If False, the default, disallow an axis from being specified twice. Returns ------- normalized_axes : tuple of int The normalized axis index, such that `0 <= normalized_axis < ndim` csg|]}t|qSr )r.0rrrr r \z(normalize_axis_tuple..zrepeated axis in `z ` argumentz repeated axis) typetuplelistoperatorindex TypeErrorrsetmapint ValueError)axisrrZallow_duplicater r r normalize_axis_tuple8s r.cCs(|dur |St|dkr td|dS)Nrzdoes not handle tuple axisr)rNotImplementedErrorr-r r r allow_only_single_axises  r1csTtttfvrftt|}t|t|fddt|D}|S)Ncs g|]}|vrdntqSr)nextrr-Zshape_itr r r!tr"z expand_shape..)r#r%r$rr.iterrange)Z arr_shaper-Zout_ndimshaper r4r expand_shapems r8cCs:|dur d|}||}nt|j|}||}|S)Nr2)expand contiguousr8r7Zreshape)rr-rr7r r r apply_keepdimsxs   r;r0cGs.|dur"tdd|D}|dfS||fSdS)z#Flatten the arrays if axis is None.Ncss|]}|VqdSr)flatten)rarr r r r"z$axis_none_flatten..rr$)r-tensorsr r r axis_none_flattensrAcCs>tj}||j||ds4td|jd|d|dt||S)aDtype-cast tensor to target_dtype. Parameters ---------- t : torch.Tensor The tensor to cast target_dtype : torch dtype object The array dtype to cast all tensors to casting : str The casting mode, see `np.can_cast` Returns ------- `torch.Tensor` of the `target_dtype` dtype Raises ------ ValueError if the argument cannot be cast according to the `casting` rule )castingzCannot cast array data from z to z according to the rule '')rZ can_cast_implrr(r)t target_dtyperBZcan_castr r r typecast_tensors rFcstfdd|DS)Nc3s|]}t|VqdSr)rF)rrDrBrEr r r>r"z#typecast_tensors..r?)r@rErBr rGr typecast_tensorssrHc CsVzt|}WnBtyP}z*d|dt|d}t|WYd}~n d}~00|S)Nzfailed to convert z! to ndarray. Internal error is: .)torchZ as_tensorrrr/)objreZmesgr r r _try_convert_to_tensors rMc Cst|tjr|}n>t}tttjzt|}Wt|n t|0t ||}||j }|dkr| d||j }|r| }|S)aThe core logic of the array(...) function. Parameters ---------- obj : tensor_like The thing to coerce dtype : torch.dtype object or None Coerce to this torch dtype copy : bool Copy or not ndmin : int The results as least this many dimensions is_weak : bool Whether obj is a weakly typed python scalar. Returns ------- tensor : torch.Tensor a tensor object with requested dtype, ndim and copy semantics. Notes ----- This is almost a "tensor_like" coersion function. Does not handle wrapper ndarrays (those should be handled in the ndarray-aware layer prior to invoking this function). rr2)rrJZTensorZget_default_dtypeZset_default_dtyperZget_default_dtype_forfloat32rMrrviewr7clone)rKrcopyZndminrZ default_dtypeZ ndim_extrar r r _coerce_to_tensors    rRcGsddlm}t|dkrtSt|dkrz|d}t||rB|jSt|trtg}|D]}t|}||qTt|S|Snt|tsJt|SdS)zHConvert all ndarrays from `inputs` to tensors. (other things are intact)r)ndarrayrN) Z_ndarrayrSrr,rrr$ndarrays_to_tensorsappend)inputsrSZinput_resultZ sub_inputZ sub_resultr r r rTs       rT)N)NF)NFr)__doc__r&rJrr r, IndexErrorr r( RuntimeErrorrrrrr.r1r8r;rArFrHrMrRrTr r r r s&   -     8