tjdZddlZddlZddlmZdZGddeeZGdd e e Z d Z d Z dd ZddZdZdZdZdddZdZdZdZddZdZdS)zKAssorted utilities, which do not need anything other than torch and stdlib.N) _dtypes_implcxt|trdS t|n#t$rYdSwxYwdS)NFT) isinstancestrlen Exception)seqs P/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/_numpy/_util.py is_sequencer sR#su C uu 4s ) 77ceZdZdS) AxisErrorN__name__ __module__ __qualname__r rrDrrceZdZdS)UFuncTypeErrorNrrrr rrrrrcJ| |j|kr||}|SN)dtypeto)tensorrs r cast_if_neededrs* V\U225!! Mrctj|jdkr+|tjj}|S)N)r _categoryrrdefault_dtypes float_dtype)xs r cast_int_to_floatr$&s<ag&&** DD,..: ; ; Hrcf| |cxkr|ksntd|d||dkr||z }|S)Nzaxis z) is out of bounds for array of dimension r)r)axndimargnames r normalize_axis_indexr).sY ER    $    SSSTSSTTT Avv d  IrFct|ttfvr' tj|g}n#t $rYnwxYwtfd|D}|sdt ttt|t |kr$rtddtd|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` c3:K|]}t|VdSr)r)).0r&r(r's r z'normalize_axis_tuple..[s0HHR%b$88HHHHHHrzrepeated axis in `z ` argumentz repeated axis) typetuplelistoperatorindex TypeErrorrsetmapint ValueError)axisr'r(allow_duplicates `` r normalize_axis_tupler:7s< Dzz%&& N4(()DD    D  HHHHH4HHH H HD .s3s3~~#6#6773t99DD  .E'EEEFF F_-- - Ks7 AAc^||St|dkrtd|dS)Nrzdoes not handle tuple axisr)rNotImplementedErrorr8s r allow_only_single_axisr>ds3 |  4yyA~~!">??? 7Nrctttfvrftt|z}t |t |fdt |D}|S)Nc:g|]}|vrdntSr)next)r,r&r8shape_its r z expand_shape..ss+ K K KR"**QQ$x.. K K Kr)r.r0r/rr:iterrange) arr_shaper8out_ndimshaperCs ` @r expand_shaperJlsw Dzz$&&w4yy3y>>)H h / /DIH K K K K K5?? K K KE Lrc|-d|z}||}n*t|j|}||}|S)NrA)expand contiguousrJrIreshape)rr8r'rIs r apply_keepdimsrOwsV |t u%%0022V\400&& Mrr=cH|td|D}|dfS||fS)z#Flatten the arrays if axis is None.Nc3>K|]}|VdSr)flatten)r,ars r r-z$axis_none_flatten..s*77 777777rrr/)r8tensorss r axis_none_flattenrVs7 |77w77777z}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_implrr3r)t target_dtyperXcan_casts r typecast_tensorr^s,)H 8AG\7 ; ; ;  A17 A A A A6= A A A    !\ * **rc>tfd|DS)Nc3:K|]}t|VdSr)r^)r,r[rXr\s r r-z#typecast_tensors..s/LLqL'::LLLLLLrrT)rUr\rXs ``r typecast_tensorsras* LLLLLGLLL L LLrc tj|}n7#t$r*}d|dt|d}t |d}~wwxYw|S)Nzfailed to convert z! to ndarray. Internal error is: .)torch as_tensorr rr<)objremesgs r _try_convert_to_tensorrisj(%% (((TCTT3q66TTT!$'''( Ms A %AA c4t|tjr|}ntj}tjt jtj t|}tj|n#tj|wxYwt||}||j z }|dkr | d|z|j z} t|}n#t$rd}YnwxYw|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" coercive function. Does not handle wrapper ndarrays (those should be handled in the ndarray-aware layer prior to invoking this function). rrAF)rrdTensorget_default_dtypeset_default_dtyperget_default_dtype_forfloat32rirr'viewrIboolr7clone)rfrcopyndminr default_dtype ndim_extras r _coerce_to_tensorrws!6#u|$$ 3 /11   B5= Q QRRR 3+C00F  #M 2 2 2 2E #M 2 2 2 2FE * *F$JA~~TJ.=>>Dzz    Ms"BB C00 C?>C?cddlm}t|dkrtSt|dkrp|d}t ||r|jSt |t r:g}|D]&}t|}||'t |S|St |t s$tdt|j t|S)zHConvert all ndarrays from `inputs` to tensors. (other things are intact)r)ndarrayrz#Expected inputs to be a tuple, got ) _ndarrayryrr7rrr/ndarrays_to_tensorsappendAssertionErrorr.r)inputsryinput_result sub_input sub_results r r{r{s!!!!!! 6{{a|| V   fg & & =  & & F# * * 0;;  j))))== M&%((  Md6ll6KMM #6***rr)NF)NFr)__doc__r1rdrr r7 IndexErrorrr3 RuntimeErrorrrr$r)r:r>rJrOrVr^rarirwr{rrr rsRQ      J        Y       ****Z&*+++@MMM;;;;|+++++r