o oi@snddlmZmZmZmZmZddlZddlmZm Z m Z d#de dee ddfddZ d ded fd ddfd eeeeeefeefd edeedeeeefdeedeejdeejdefddZd#ded edeeeefddfddZ   d$ded edeeeefde deddf ddZ  d%deeeeedffd e deejdeejdef d!d"ZdS)&)AnyListOptionalTupleUnionN)Tensor as_tensortensor batch_size same_on_batchreturncCsLt|tr |dkstd|d|dur"t|ts$td|ddSdS)z0Check valid batch_size and same_on_batch params.rz.`batch_size` shall be a positive integer. Got .Nz&`same_on_batch` shall be boolean. Got ) isinstanceintAssertionErrorbool)r r r^/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/utils/param_validation.py_common_param_checks rginfjointfactornamecenterboundscheckdevicedtypecCs(|dur td}|durt}t|tst|||d}|dkrg|dkr2td|d|d|dus:|durEtd|d |d|d td d g|j|j d|}| |d|d j ||d}nt |||d}|dur|dkr~t ||||S|dkrt||||Std|d|S)z9Check inputs and compute the corresponding factor bounds.NcpurrrzIf z2 is a single number, it must be non negative. Got r z<`center` and `bounds` cannot be None for single number. Got z, gg?rsingularz methods 'z' not implemented.)torchrget_default_dtyperrr dim ValueErrorrepeatrclamptor_joint_range_check_singular_range_checkNotImplementedError)rrrrrrr factor_boundrrr _range_bound!s.   $   r. ranged_factorcCs|dur tdtdf}|dkr[t|dkr[|d|dkr(|d|dks5t|d|d|d |d|dkrL|dkrL|dksYnt|d |d |dSt|d |d |d )zHCheck if bounds[0] <= ranged_factor[0] <= ranged_factor[1] <= bounds[1].N-infrr!r r out of bounds. Expected inside , got r z[0] should be smaller than z[1] got z7 should be a tensor with length 2 whose values between z. Got )floatr%lenr& TypeError)r/rrrrrr*Ks 0r*F2d skip_nonemodecCs|dkrd}n|dkrd}ntd||r|durdS|dur)tdtdf}|d krXt||krX|D]}|d |krG|d ksUnt|d |d |d q7dSt|d|d|d|)zcCheck if bounds[0] <= ranged_factor[0] <= bounds[1] and bounds[0] <= ranged_factor[1] <= bounds[1].r6r 3dz%'mode' shall be either 2d or 3d. Got Nr0rr!rr1r2r z1 should be a float number or a tuple with length z whose values between z.Got )r&r3r%r4r5)r/rrr7r8dim_sizefrrrr+Ys( r+ input_range. target_sizecCst|dg}t|trt|jdks t|jdkrHt|dkrH|dkr+td||dj||dt ddg||d}||dd}|St|jdkrdt|dkrd||ddj||d}|St|jdkrt||dkr| dddj||dt ddg||d}|S|j|kr|j||d}|Stdt |d|t|t t fr|dkrtd|t | |g||d|dd}|St|tt frt|dkrt|dt t frt|dt t frt |||d|dd}|St|tt fr!t||dkr!td d |Dr!t d d |D||d}|St|tt frEt||dkrEtd d |DrEt |||d}|Std|d|d|d)aNGiven target_size, it will generate the corresponding (target_size, 2) range tensor for element-wise params. Example: >>> degree = tensor([0.2, 0.3]) >>> _tuple_range_reader(degree, 3) # read degree for yaw, pitch and roll. tensor([[0.2000, 0.3000], [0.2000, 0.3000], [0.2000, 0.3000]]) r rr!zCIf input_range is only one number it must be a positive number. GotrzDegrees must be a z; tensor for the degree range for independent operation.Got cs|] }t|ttfVqdSN)rr3r.0xrrr z&_tuple_range_reader..cSsg|]}| |fqSrr)rCsrrr sz'_tuple_range_reader..csr@rA)rtuplelistrBrrrrErFz]If not pass a tensor, it must be float, (float, float) for isotropic operation or a tuple of z floats or z/ (float, float) for independent operation. Got r )r#Sizerrr4shaper&r'r)r unsqueezerJr3rrIallr5)r=r>rr target_shapeinput_range_tmprrr_tuple_range_readerws~ ( 30 - (      rQrA)NFr6)NN)typingrrrrrr# kornia.corerrr rrrr3strrrr.r*r+rQrrrrsp  (* !