LjdZddlZddlZddlmZdZGddeeZGdd e e Z d Z d Z dd Zdd ZdZdZdZdddZdZdZdZddZdZy)zKAssorted utilities, which do not need anything other then torch and stdlib.N) _dtypes_implc\t|try t|y#t$rYywxYw)NFT) isinstancestrlen Exception)seqs Q/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/_numpy/_util.py is_sequencer s4#s C  s  ++c eZdZy) AxisErrorN__name__ __module__ __qualname__r rrrrc eZdZy)UFuncTypeErrorNrrrr rrrrrcJ| |j|k7r|j|}|SN)dtypeto)tensorrs r cast_if_neededrs& V\\U25! Mrctj|jdkr-|jtjj }|S)N)r _categoryrrdefault_dtypes float_dtype)xs r cast_int_to_floatr$&s;agg&* DD,,.:: ; HrcX| |cxkr|ksntd|d||dkr||z }|S)Nzaxis z) is out of bounds for array of dimension r)r)axndimargnames r normalize_axis_indexr).s? ER $ %t#LTFSTT Av d  IrcFt|ttfvr tj|g}tfd|D}|sJt ttt|t |k7rrtddtd|S#t $rYnwxYw)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` c38K|]}t|ywr)r)).0r&r(r's r z'normalize_axis_tuple..[sHR%b$8Hzrepeated axis in `z ` argumentz repeated axis) typetuplelistoperatorindex TypeErrorrsetmapint ValueError)axisr'r(allow_duplicates `` r normalize_axis_tupler;7s< Dz%& NN4()D H4H HD s3s3~#673t9D 1'*EF F_- - K   sB B B cF||St|dk7r td|dS)Nrzdoes not handle tuple axisr)rNotImplementedErrorr9s r allow_only_single_axisr?ds, |  4yA~!">?? 7Nrct|ttfvr|f}t|t|z}t ||}t |}t |Dcgc]}||vrdn t|}}|Scc}w)Nr)r/r1r0rr;iterrangenext) arr_shaper9out_ndimshape_itr&shapes r expand_shaperHlsq Dz$&w4y3y>)H h /DIH;@? KR"*Q$x. 0 KE K L LsA2c|&d|z}|j|j}|St|j|}|j |}|S)Nr)expand contiguousrHrGreshape)rr9r'rGs r apply_keepdimsrNwsS |t u%002 MV\\40& Mrr>c:|td|D}|dfS||fS)z#Flatten the arrays if axis is None.c3<K|]}|jywr)flatten)r,ars r r-z$axis_none_flatten..s7 7srr0)r9tensorss r axis_none_flattenrUs+ |7w77z}rc tj}||j||std|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 '')r can_cast_implrr4r)t target_dtyperWcan_casts r typecast_tensorr]s_,))H AGG\7 ;*177)4~5gYa A   !\ **rc0tfd|DS)Nc38K|]}t|ywr)r])r,rZrWr[s r r-z#typecast_tensors..sLqL':Lr.rS)rTr[rWs ``r typecast_tensorsr`s LGL LLrc tj|}|S#t$r"}d|dt|d}t |d}~wwxYw)Nzfailed to convert z! to ndarray. Internal error is: .)torch as_tensorr rr=)objremesgs r _try_convert_to_tensorrhsS(% M (#C5(J3q6(RST!$''(s A?Ac*t|tjr|}nktj}tjt j tj t|}tj|t||}||jz }|dkDr!|jd|z|jz} t|}|r|j}|S#tj|wxYw#t$rd}Y;wxYw)aThe core logic of the array(...) function. Parameters ---------- obj : tensor_like The thing to coerce dtype : torch.dtype object or None Coerce to this torch dtype copy : bool Copy or not ndmin : int The results as least this many dimensions is_weak : bool Whether obj is a weakly typed python scalar. Returns ------- tensor : torch.Tensor a tensor object with requested dtype, ndim and copy semantics. Notes ----- This is almost a "tensor_like" coercive function. Does not handle wrapper ndarrays (those should be handled in the ndarray-aware layer prior to invoking this function). rrJF)rrcTensorget_default_dtypeset_default_dtyperget_default_dtype_forfloat32rhrr'viewrGboolr8clone)rercopyndminr default_dtype ndim_extras r _coerce_to_tensorrvs6#u||$ //1   B B5== QR 3+C0F  # #M 2FE *F$JA~TJ.=>Dz   M)  # #M 2 s) C* D*D DDcddlm}t|dk(r tSt|dk(r_|d}t ||r |j St |t r0g}|D]}t|}|j| t |S|St |t s!tdt|jt|S)zHConvert all ndarrays from `inputs` to tensors. (other things are intact)r)ndarrayrz#Expected inputs to be a tuple, got ) _ndarrayrxrr8rrr0ndarrays_to_tensorsappendAssertionErrorr/r)inputsrxinput_result sub_input sub_results r rzrzs! 6{a| V  fg &==  &F# * 0;  j) *= M&%( 5d6l6K6K5LM #6**rr)NF)NFr)__doc__r2rcrr r8 IndexErrorrr4 RuntimeErrorrrr$r)r;r?rHrNrUr]r`rhrvrzrrr rsR  J  Y   *Z&*+@M;|+r