o h@sdZddlZddlZddlmZddZGdddeeZGd d d e e Z d d Z d dZ d%ddZd&ddZddZddZddZddddZddZdd Zd'd!d"Zd#d$ZdS)(zLAssorted utilities, which do not need anything other then torch and stdlib. N) _dtypes_implcCs2t|trdSzt|WdStyYdSw)NFT) isinstancestrlen Exception)seqr H/var/www/html/ai/venv/lib/python3.10/site-packages/torch/_numpy/_util.py is_sequence s   r c@ eZdZdS) AxisErrorN__name__ __module__ __qualname__r r r r r r c@r )UFuncTypeErrorNrr r r r rrrcCs |dur|j|kr||}|SN)dtypeto)tensorrr r r cast_if_neededs rcCs$t|jdkr|tj}|S)N)r _categoryrrdefault_dtypes float_dtype)xr r r cast_int_to_float%srcCs@| |kr |ksntd|d||dkr||7}|S)Nzaxis z) is out of bounds for array of dimension r)r )axndimargnamer r r normalize_axis_index-s r"Fcst|ttfvrzt|g}Wn tyYnwtfdd|D}|s@tt|t|kr@rZsz(normalize_axis_tuple..zrepeated axis in `z ` argumentz repeated axis) typetuplelistoperatorindex TypeErrorrset ValueError)axisr r!allow_duplicater r%r normalize_axis_tuple6s r1cCs(|dur|St|dkrtd|dS)Nrzdoes not handle tuple axisr)rNotImplementedErrorr/r r r allow_only_single_axiscs  r4csTtttfvr ftt|}t|t|fddt|D}|S)Ncs g|] }|vr dntqSr)nextr#r/shape_itr r r&rs z expand_shape..)r'r)r(rr1iterrange) arr_shaper/out_ndimshaper r7r expand_shapeks r>cCs<|durd|}||}|St|j|}||}|S)Nr5)expand contiguousr>r=reshape)rr/r r=r r r apply_keepdimsvs rBr3cGs*|durtdd|D}|dfS||fS)z#Flatten the arrays if axis is None.Ncss|]}|VqdSr)flatten)r$arr r r sz$axis_none_flatten..rr()r/tensorsr r r axis_none_flattensrHcCs>tj}||j||dstd|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_implrr,r)t target_dtyperIcan_castr r r typecast_tensors  rOcstfdd|DS)Nc3s|] }t|VqdSr)rO)r$rLrIrMr r rEsz#typecast_tensors..rF)rGrMrIr rPr typecast_tensorssrQcCsvt|tjr |}nt|}t|j}tj||d}t||}||j}|dkr3| d||j }|r9| }|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). )rrr5) rtorchTensor as_tensorrget_default_dtype_forrrr viewr=clone)objrcopyndminr torch_dtype ndim_extrar r r _coerce_to_tensors     r]cGsddlm}t|dkrtSt|dkr<|d}t||r!|jSt|tr:g}|D] }t|}||q*t|S|St|tsCJt|S)zHConvert all ndarrays from `inputs` to tensors. (other things are intact)r)ndarrayr) _ndarrayr^rr.rrr(ndarrays_to_tensorsappend)inputsr^input_result sub_input sub_resultr r r r`s      r`r)NF)NFr)__doc__r*rRrr r. IndexErrorr r, RuntimeErrorrrrr"r1r4r>rBrHrOrQr]r`r r r r s&    -     9