3jn%SrSSKJr SSKrSSKJrJrJrJrJ r SSK r SSK J r J r J r SSKJrJrJrJrJrJr /SQrS S jr\"5qS\S 'S S jrS!S jrS!S jrS"S#SjjrS"S$SjjrS%Sjr\"S\S9r S"S&Sjjr!S"S'Sjjr"S(S)Sjjr#S(S*Sjjr$S"S+Sjjr%S(S*Sjjr&S"S,Sjjr'S"S,Sjjr(S"S,Sjjr)S-S.Sjjr*S(S/Sjjr+S(S0Sjjr,S1Sjr-g)2z8The testing package contains testing-specific utilities.) annotationsN)AnyOptionalSequenceTypeVarcast)float16float32float64) BaseError DeviceError ImageError ShapeErrorTypeCheckErrorValueCheckError) KORNIA_CHECKKORNIA_CHECK_DM_DESCKORNIA_CHECK_IS_COLORKORNIA_CHECK_IS_GRAYKORNIA_CHECK_IS_IMAGEKORNIA_CHECK_IS_LIST_OF_TENSORKORNIA_CHECK_IS_TENSORKORNIA_CHECK_LAFKORNIA_CHECK_SAME_DEVICEKORNIA_CHECK_SAME_DEVICESKORNIA_CHECK_SHAPEKORNIA_CHECK_TYPE KORNIA_UNWRAPr r rrrrare_checks_enableddisable_checks enable_checksboolc^[R"SS5nUbUR5S;$g)a$Determine if checks should be enabled. Returns: True if checks should be enabled, False otherwise. Checks are enabled by default in debug mode (normal Python execution). Checks are disabled when: - Running with `python -O` (optimized mode) - Environment variable KORNIA_CHECKS=0 is set KORNIA_CHECKSN)1trueyesonT)osgetenvlower)env_vars K/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/core/check.py_should_enable_checksr.?s/ii.G}}"<<< _KORNIA_CHECKS_ENABLEDc[$)zCheck if validation is currently enabled. Returns: True if checks are enabled, False otherwise. Example: >>> are_checks_enabled() True r0r/r-rrVs  "!r/cSqg)aCDisable all Kornia validation checks for production. Note: This function has no effect if checks were disabled at import time (via `python -O` or KORNIA_CHECKS=0). The module-level flag is evaluated once at import time for performance. Example: >>> disable_checks() >>> are_checks_enabled() False FNr2r3r/r-r r cs #r/cSqg)zmEnable all Kornia validation checks. Example: >>> enable_checks() >>> are_checks_enabled() True TNr2r3r/r-r!r!ts "r/cV[RR5(d [(dgSUS:Xa"USSnUR[ U5*S-SnO8SUS:Xa!USSnURS[ U5S- nOUnURn[ U5[ U5:wacU(a[[ U5n[ U5nSUSUS 3nUS US 3- n[ UR5n US U 3- nUbUS U3- n[UU US9eg[[ U55Hn XJn U R5(dM[U 5n XZU :wdM3U(aJSU SU SXZS 3nUS US 3- n[ UR5n US U 3- nUbUS U3- n[UU US9e g g)a0Check whether a tensor has a specified shape. The shape can be specified with a implicit or explicit list of strings. The guard also check whether the variable is a type `Tensor`. Args: x: the tensor to evaluate. shape: a list with strings with the expected shape. msg: optional custom message to append to error. raises: bool indicating whether an exception should be raised upon failure. Raises: ShapeError: if the input tensor does not have the expected shape and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_SHAPE(x, ["B", "C", "H", "W"]) # implicit True >>> x = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_SHAPE(x, ["2", "3", "H", "W"]) # explicit True T*rNz#Shape dimension mismatch: expected z dimensions, got z.  Expected shape:   Actual shape:   actual_shapeexpected_shapeFzShape mismatch at dimension z : expected , got ) torchjit is_scriptingr0shapelenlistrrange isnumericint) xrEmsgraisesshape_to_checkx_shape_to_check expected_dims actual_dims error_msg x_shape_listidim_dims r-rrs> 99 ! ! # #%% eAhqr77CJ;?#45 b s77#3SZ!^477 N 33 /M./K=m_L]^i]jjmnI -eWB7 7I=L +L>: :ItC5\) )$   3'( )#%~~ $i  # %:1#[VTdTgShhkl 1%;; #AGG} / ~>> ?4u-I !-#( +*, r/c[RR5(d [(dgU(dU(aUcSnOUn[ U5egg)aCheck any arbitrary boolean condition. Args: condition: the condition to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: BaseError: if the condition is not met and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x = torch.rand(2, 3, 3) >>> KORNIA_CHECK(x.shape[-2:] == (3, 3), "Invalid homography") True TzValidation condition failedF)rBrCrDr0r ) conditionrLrMrRs r-rrsF. 99 ! ! # #%%  {9  I& & r/c[X5$)zUnwrap an optional contained value that may or not be present. Args: maybe_obj: the object to unwrap. typ: expected type after unwrap. )r) maybe_objtyps r-rrs  r/T)boundc[RR5(d [(dg[ X5(dU(a[RR5(aSnUbUSU3- n[ U5e[ U[ 5(d UROSRSU55n[[U55nSUSUS3nUbUSU3- n[ U[U5US 9eg g) aCheck the type of an aribratry variable. Args: x: any input variable. typ: the expected type of the variable. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeCheckError: if the input variable does not match with the expected and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> KORNIA_CHECK_TYPE("foo", str, "Invalid string") True TzType mismatch.r=z | c38# UHoRv M g7fN)__name__).0ts r- $KORNIA_CHECK_TYPE..5s^wsvno_i_isvszType mismatch: expected rA. actual_type expected_typeF) rBrCrDr0 isinstancertuplerajoinstrtype)rKr[rLrMrRexpected_type_str type_names r-rrs. 99 ! ! # #%% a   yy%%'', ?4u-I$Y//9C38N8NCLLTYT^T^^wsv^wTw!QL 67H6IPY{Z[\ ?4u-I$ $Q"%  r/c[RR5(d [(dg[ U[R 5(dU(a[RR5(aSnUbUSU3- n[ U5e[[U55nSUS3nUbUSU3- n[ U[U5[R S9egg)aCheck the input variable is a Tensor. Args: x: any input variable. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeCheckError: if the input variable does not match with the expected and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x = torch.rand(2, 3, 3) >>> KORNIA_CHECK_IS_TENSOR(x, "Invalid tensor") True TzType mismatch: expected Tensor.r=z$Type mismatch: expected Tensor, got rfrgF) rBrCrDr0rjTensorrrmrn)rKrLrMrRrps r-rrCs. 99 ! ! # #%% a & & yy%%''= ?4u-I$Y// QL B9+QO ?4u-I$ $Q"',,  r/cV[RR5(d [(dg[ U[ 5=(a [ SU55nU(dJU(aBS[U5RS3n[U[U5[ [RS9egg)aNCheck the input variable is a List of Tensors. Args: x: Any sequence of objects raises: bool indicating whether an exception should be raised upon failure. Raises: TypeCheckError: if the input variable does not match with the expected and raises is True. Return: True if the input is a list of Tensors, otherwise return False. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x = torch.rand(2, 3, 3) >>> KORNIA_CHECK_IS_LIST_OF_TENSOR(x, raises=False) False >>> KORNIA_CHECK_IS_LIST_OF_TENSOR([x]) True Tc3V# UHn[U[R5v M! g7fr`)rjrBrr)rbds r-rd1KORNIA_CHECK_IS_LIST_OF_TENSOR..s -USTajELL.I.ISTs')z*Type mismatch: expected list[Tensor], got rfrgF) rBrCrDr0rjrGallrnrarrr)rKrM are_tensorsrRs r-rrus6 99 ! ! # #%%Q%U#-UST-U*UK  DT!WEUEUDVVWXI  G"5<<0   r/cJ[RR5(d [(dgURUR:waZU(aRSnUSURS3- nUSUR3- n[ UURUR/URS9egg)aCheck whether two tensor in the same device. Args: x: first tensor to evaluate. y: second tensor to evaluate. raises: bool indicating whether an exception should be raised upon failure. Raises: DeviceError: if the two tensors are not in the same device and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x1 = torch.rand(2, 3, 3) >>> x2 = torch.rand(1, 3, 1) >>> KORNIA_CHECK_SAME_DEVICE(x1, x2) True Tz5Device mismatch: tensors must be on the same device. z First tensor device: r;z Second tensor device: actual_devicesexpected_deviceF)rBrCrDr0devicer )rKyrMrRs r-rrs0 99 ! ! # #%%xx188 PI 2188*B? ?I 3AHH:> >I !!((3 !   r/c^[RR5(d [(dg[ [ T[ 5=(a [T5S:SU5 [U4SjT55(dfU(a^TVs/sHo3RPM nnSnUSTSRS3- nUS U3- nUbUS U3- n[UUTSRS 9eg gs snf) a3Check whether a list provided tensors live in the same device. Args: tensors: a list of tensors. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: DeviceError: if all the tensors are not in the same device and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x1 = torch.rand(2, 3, 3) >>> x2 = torch.rand(1, 3, 1) >>> KORNIA_CHECK_SAME_DEVICES([x1, x2], "Tensors not in the same device") True Tr8z)Expected a list with at least one elementc3\># UH!nTSRUR:Hv M# g7f)rN)r})rbrKtensorss r-rd,KORNIA_CHECK_SAME_DEVICES..s$>gwqz  AHH,gs),z9Device mismatch: all tensors must be on the same device. z Expected device: rr;z Actual devices: r=rzF) rBrCrDr0rrjrGrFrwr}r )rrLrMrKdevicesrRs` r-rrs0 99 ! ! # #%%GT*@s7|q/@Bmouv >g> > > )01AxxG1TI .wqz/@/@.AD DI -gY7 7ItC5\) & ' 1 1   2sC"c6[RR5(d [(dgURUR:waPU(aHSn[ UR5n[ UR5nUSUS3- nUSU3- n[ UUUS9egg)aCheck whether two tensor have the same shape. Args: x: first tensor to evaluate. y: second tensor to evaluate. raises: bool indicating whether an exception should be raised upon failure. Raises: ShapeError: if the two tensors have not the same shape and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> x1 = torch.rand(2, 3, 3) >>> x2 = torch.rand(2, 3, 3) >>> KORNIA_CHECK_SAME_SHAPE(x1, x2) True Tz2Shape mismatch: tensors must have the same shape. z First tensor shape: r;z Second tensor shape: r>F)rBrCrDr0rErGr)rKr~rMrRrS y_shape_lists r-KORNIA_CHECK_SAME_SHAPErs0 99 ! ! # #%%ww!'' MI=L=L 1,rB BI 2<.A AI)+   r/c[RR5(d [(dg[ UR 5S:dUR SS:wa-U(a%S[ U5S3nUbUSU3- n[U5egg)aCheck whether an image tensor is a color images. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: ImageError: if the input tensor does not have a shape :math:`(3,H,W)` and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> img = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_IS_COLOR(img, "Image is not color") True TzNot a color tensor. Got: rfr;FrBrCrDr0rFrErnrrKrLrMrRs r-rr%sz. 99 ! ! # #%% 177|a1772;!+ 3DG9A>Ir#Z' Y' ' r/cF[RR5(d [(dg[ UR 5S:d,[ UR 5S:a@UR SS:wa-U(a%S[ U5S3nUbUSU3- n[U5eg g) aCheck whether an image tensor is grayscale. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: ImageError: if the tensor does not have a shape :math:`(1,H,W)` or :math:`(H,W)` and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> img = torch.rand(2, 1, 4, 4) >>> KORNIA_CHECK_IS_GRAY(img, "Image is not grayscale") True Trrr8zNot a gray tensor. Got: rfr;Frrs r-rrJs. 99 ! ! # #%% 177|aCLA-!''"+2B 247)1=Ir#Z' Y' ' r/c[RR5(d [(dg[ UR 5S:dUR SS;a-U(a%S[ U5S3nUbUSU3- n[U5egg) a%Check whether an image tensor is grayscale or color. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: ImageError: if the tensor does not have a shape :math:`(1,H,W)` or :math:`(3,H,W)` and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> img = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_IS_COLOR_OR_GRAY(img, "Image is not color or grayscale") True Trr)r8rz!Not a color or gray tensor. Got: rfr;Frrs r-KORNIA_CHECK_IS_COLOR_OR_GRAYrosz. 99 ! ! # #%% 177|a1772;f4 ;DG9AFIr#Z' Y' ' r/c[RR5(d [(dgU(d[ XU5(dg[R "U5upEUR [R[[[4;a US:US:-nOSU-S- nUS:XW:-nUR5(a [XXE5$g)aCheck whether an image tensor is ranged properly [0, 1] for float or [0, 2 ** bits] for int. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. bits: the image bits. The default checks if given integer input image is an 8-bit image (0-255) or not. Raises: ImageError: if the tensor shape is invalid and raises is True. ValueCheckError: if the tensor value range is invalid and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> img = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_IS_IMAGE(img, "It is not an image") True TFrr8) rBrCrDr0raminmaxdtypebfloat16r r r item_handle_invalid_range)rKrLrMbitsaminamaxinvalid max_int_values r-rrs4 99 ! ! # #%% 7GGq!JDww5>>7GW==!8q)da !8 45||~~$S$== r/ct[RR5(d [(dgUR S5UR S5:Xa$UR S5UR S5:XdU(aUR S5UR S54nUR S5nUR S5nSnUSUSUSUS3- n[ UR 5n[ UR 5n [ UR 5n US US 3- nUS U S 3- nUS U 3- n[UUUS 9egg)aCheck whether the provided descriptors match with a distance matrix. Args: desc1: first descriptor tensor to evaluate. desc2: second descriptor tensor to evaluate. dm: distance matrix tensor to evaluate. raises: bool indicating whether an exception should be raised upon failure. Raises: ShapeError: if the descriptors shape do not match with the distance matrix and raises is True. Note: Checks can be disabled in Python mode using `disable_checks()` or the KORNIA_CHECKS environment variable. In TorchScript-compiled code, checks always run (TorchScript cannot access module-level globals, but the validation logic is fast). When running with `python -O`, Python's optimizer may eliminate some checks. Example: >>> desc1 = torch.rand(4) >>> desc2 = torch.rand(8) >>> dm = torch.rand(4, 8) >>> KORNIA_CHECK_DM_DESC(desc1, desc2, dm) True Trr8z Distance matrix shape mismatch. r:z (from desc1.shape[0]=z, desc2.shape[0]=z) r<r;z desc1 shape: z desc2 shape: r>F)rBrCrDr0sizerGrEr) desc1desc2dmrMr@ desc1_size desc2_sizerR dm_shape_listdesc1_shape_listdesc2_shape_lists r-rrs:4 99 ! ! # #%% WWQZ5::a= (rwwqzUZZ]/J #jjmUZZ];NAJAJ;I $^$44J:,Vghrgssvw I!NM#EKK0 #EKK0  +M?"= =I ?+;*>> lafs = torch.rand(2, 10, 2, 3) >>> KORNIA_CHECK_LAF(lafs) True )BN23)rM)r)lafrMs r-rrs, c#7 GGr/c[U[R5(aUR5OUn[U[R5(aUR5OUnSUSUS3nUbUSU3- nU(a [ UXE4SS9eg)zHandle invalid range cases.z,Value range mismatch: expected [0, 1], got [z, z].r=)gg?) actual_valueexpected_rangeF)rjrBrrrr)rLrMmin_valmax_val min_scalar max_scalarerr_msgs r-rrs $.gu||#D#D'J#-gu||#D#D'Jrs$?" 99 ++ 4( 566 "#" "Up#L   Ct0f/d)X'T+\)X"J"J"J-`3lH2  $/CNb r/