U 9%e@sdZddlZddlZddlmZddZGdddeeZGd d d e e Z d d Z d dZ d%ddZd&ddZddZddZddZddddZddZdd Zd'd!d"Zd#d$ZdS)(zLAssorted utilities, which do not need anything other then torch and stdlib. N) _dtypes_implcCs6t|trdSz t|Wntk r0YdSXdS)NFT) isinstancestrlen Exception)seqr Q/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/_numpy/_util.py is_sequence s  r c@s eZdZdS) AxisErrorN__name__ __module__ __qualname__r r r r r sr c@s eZdZdS)UFuncTypeErrorNr r r r r rsrcCs |dk r|j|kr||}|SN)dtypeto)tensorrr r r cast_if_neededs rcCs$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|ttfkr6zt|g}Wntk r4YnXtfdd|D}|stt|t|krr|tddntd|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 Zsz(normalize_axis_tuple..zrepeated axis in `z ` argumentz repeated axis) typetuplelistoperatorindex TypeErrorrset ValueError)axisrrZallow_duplicater r r normalize_axis_tuple6sr+cCs(|dkr |St|dkr td|dS)Nrzdoes not handle tuple axisr)rNotImplementedErrorr*r r r allow_only_single_axiscs  r.csTtttfkrftt|}t|t|fddt|D}|S)Ncs g|]}|krdntqSr)nextrr*Zshape_itr r r!rsz expand_shape..)r"r$r#rr+iterrange)Z arr_shaper*Zout_ndimshaper r1r expand_shapeks r5cCs:|dkr d|}||}nt|j|}||}|S)Nr/)expand contiguousr5r4Zreshape)rr*rr4r r r apply_keepdimsvs   r8r-cGs.|dkr"tdd|D}|dfS||fSdS)z#Flatten the arrays if axis is None.Ncss|]}|VqdSr)flatten)rarr r r sz$axis_none_flatten..rr#)r*tensorsr r r axis_none_flattensr>cCs>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_dtyper?Zcan_castr r r typecast_tensors rCcstfdd|DS)Nc3s|]}t|VqdSr)rC)rrAr?rBr r r;sz#typecast_tensors..r<)r=rBr?r rDr typecast_tensorssrEcCsvt|tjr|}n$t|}t|j}tj||d}t||}||j}|dkrf| d||j }|rr| }|S)aThe core logic of the array(...) function. Parameters ---------- obj : tensor_like The thing to coerce dtype : torch.dtype object or None Coerce to this torch dtype copy : bool Copy or not ndmin : int The results as least this many dimensions is_weak : bool Whether obj is a weakly typed python scalar. Returns ------- tensor : torch.Tensor a tensor object with requested dtype, ndim and copy semantics. Notes ----- This is almost a "tensor_like" coersion function. Does not handle wrapper ndarrays (those should be handled in the ndarray-aware layer prior to invoking this function). )rrr/) rtorchZTensorZ as_tensorrZget_default_dtype_forrrrviewr4clone)objrcopyZndminrZ torch_dtypeZ ndim_extrar r r _coerce_to_tensors    rKcGsddlm}t|dkrtSt|dkrz|d}t||rB|jSt|trtg}|D]}t|}||qTt|S|Snt|tst t|SdS)zHConvert all ndarrays from `inputs` to tensors. (other things are intact)r)ndarrayrN) Z_ndarrayrLrr)rrr#ndarrays_to_tensorsappendAssertionError)inputsrLZinput_resultZ sub_inputZ sub_resultr r r rMs       rM)N)NF)NFr)__doc__r%rFrr r) IndexErrorr r' RuntimeErrorrrrrr+r.r5r8r>rCrErKrMr r r r s$   -     9