[ThSrSSKrSSKrSSKJr Sr"SS\\5r"SS \ \ 5r S r S r SS jrSS jrSrSrSrSS.SjrSrSrSrSSjrSrg)zLAssorted utilities, which do not need anything other then torch and stdlib. N) _dtypes_implch[U[5(ag[U5 g![a gf=f)NFT) isinstancestrlen Exception)seqs J/var/www/auris/envauris/lib/python3.13/site-packages/torch/_numpy/_util.py is_sequencer s7#s C  s $ 11c\rSrSrSrg) AxisErrorN__name__ __module__ __qualname____firstlineno____static_attributes__rr rrrrc\rSrSrSrg)UFuncTypeErrorrNrrrr rrrrrcNUb!URU:waURU5nU$N)dtypeto)tensorrs r cast_if_neededr! s& V\\U25! Mrc[R"UR5S:a.UR[R"5R 5nU$)N)r _categoryrrdefault_dtypes float_dtype)xs r cast_int_to_floatr('s;agg&* DD,,.:: ; Hrc\U*Us=::aU:dO [SUSU35eUS:aX- nU$)Nzaxis z) is out of bounds for array of dimension r)r)axndimargnames r normalize_axis_indexr-/s= ER $ %t#LTFSTT Av  Irc [U5[[4;a[R"U5/n[UVs/sHn[ XAU5PM sn5nU(dP[[[[U555[U5:wa!U(a[SUS35e[S5eU$![ a Nf=fs snf)ar Normalizes an axis argument into a tuple of non-negative integer axes. This handles shorthands such as ``1`` and converts them to ``(1,)``, as well as performing the handling of negative indices covered by `normalize_axis_index`. By default, this forbids axes from being specified multiple times. Used internally by multi-axis-checking logic. Parameters ---------- axis : int, iterable of int The un-normalized index or indices of the axis. ndim : int The number of dimensions of the array that `axis` should be normalized against. argname : str, optional A prefix to put before the error message, typically the name of the argument. allow_duplicate : bool, optional If False, the default, disallow an axis from being specified twice. Returns ------- normalized_axes : tuple of int The normalized axis index, such that `0 <= normalized_axis < ndim` zrepeated axis in `z ` argumentz repeated axis) typetuplelistoperatorindex TypeErrorr-rsetmapint ValueError)axisr+r,allow_duplicater*s r normalize_axis_tupler;8s< Dz%& NN4()D DIDb&r9DI JD s3s3~#673t9D 1'*EF F_- - K   JsB1C1 B>=B>cJUcU$[U5S:wa [S5eUS$)Nrzdoes not handle tuple axisr)rNotImplementedErrorr9s r allow_only_single_axisr?es, |  4yA~!">?? 7Nrc[U5[[4;aU4n[U5[U5-n[ X5n[ U5n[ U5Vs/sHoDU;aSO [U5PM nnU$s snf)Nr)r/r1r0rr;iterrangenext) arr_shaper9out_ndimshape_itr*shapes r expand_shaperHmsn Dz$&w4y3y>)H  /DIH;@? K?R*Q$x. 0?E K L LsA7cUc&SU-nURU5R5nU$[URU5nUR U5nU$)Nr)expand contiguousrHrGreshape)r r9r+rGs r apply_keepdimsrNxsS |t u%002 MV\\40& Mrr>c:Uc[SU55nUS4$X4$)z#Flatten the arrays if axis is None.c3@# UHoR5v M g7fr)flatten).0ars r $axis_none_flatten..s7w wsrr0)r9tensorss r axis_none_flattenrXs) |7w77z}rc [RnU"URXS9(d[SURSUSUS35e[ X5$)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 '')r can_cast_implrr4r!)t target_dtyperZcan_casts r typecast_tensorr`s[,))H AGG\ ;*177)4~5gYa A   ! **rc2^^[UU4SjU55$)Nc3>># UHn[UTT5v M g7fr)r`)rRr]rZr^s r rT#typecast_tensors..sLGqL'::GsrV)rWr^rZs ``r typecast_tensorsrds LGL LLrc[R"U5nU$![a"nSUS[U5S3n[ U5eSnAff=f)Nzfailed to convert z! to ndarray. Internal error is: .)torch as_tensorr rr=)objr emesgs r _try_convert_to_tensorrlsS(% M (#C5(J3q6(RST!$''(s AAAc [U[R5(aUnOo[R"5n[R"[ R "[R55 [U5n[R"U5 [XA5nX4R- nUS:a!URSU-UR-5nU(aUR5nU$![R"U5 f=f)adThe 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). rrJ)rrgTensorget_default_dtypeset_default_dtyperget_default_dtype_forfloat32rlr!r+viewrGclone)rircopyndminr default_dtype ndim_extras r _coerce_to_tensorrys6#u||$$ //1   B B5== QR 3+C0F  # #M 2F *F$JA~TJ.=>  M  # #M 2s 1 C++DcvSSKJn [U5S:Xa [5$[U5S:XajUSn[ X!5(a UR $[ U[ 5(a2/nUHn[U5nURU5 M! [ U5$U$[ U[ 5(de[U5$)zHConvert all ndarrays from `inputs` to tensors. (other things are intact)r)ndarrayr) _ndarrayr{rr8rr r0ndarrays_to_tensorsappend)inputsr{input_result sub_input sub_results r r}r}s! 6{a| V  f & &==  & &F# 0;  j)$= M&%(((("6**rr)NF)NFr)__doc__r2rgrr r8 IndexErrorrr4 RuntimeErrorrr!r(r-r;r?rHrNrXr`rdrlryr}rrr rs  J  Y   *Z&*+@M5p+r