+ QV-j ^RIt^RIt^RIHt^RIHtHt^RIHt^RI H t H t ^RI t ^RI t^RIHtHtHtHtHtHtHtHtHt^RIHtHtHtHtHtHt^RI H!t!]!4'd^RI"t#^RI$t#]#PJPLt']!4'd^R I(H)t)H*t*^R I+H,t,^R I-H.t.]'P^],P`]'Pb],Pb]'Pd],Pd]'Pf],Pf]'Ph],Ph]'Pj],Pj/t6]6Po4UUu/uFwrWbK uppt8M/t6/t8]!4'd^RI9t9]Pt!];4t<] R ]PzR ]>R ,]>]Pz,]>R ,3,t?!RR]4t@!RR]4tA]B]C]D]C,]>]B,,3,tERtF!RR]4tGRtHRtIRRltJRtKRtLRtMRRltNRKRRlltORKR R!lltPRKR"R#lltQR$R%ltRRLR&R'lltSRLR(R)lltTRLR*R+lltUR,R-ltVR.R/ltW]@P3R0R1lltYR2R3ltZR4R5lt[R6R7lt\R8R9lt]RLR:R;llt^]!!RMR<7RLR=R>ll4t_RLR?R@llt`RNRARBllta!RCRD4tbRERFltcRGRHltd]!4!RIRJ44teR#uuppi)ON)Iterable) dataclassfields)BytesIO)AnyUnion) ExplicitEnumis_numpy_arrayis_torch_availableis_torch_tensoris_torchvision_availableis_vision_availableloggingrequires_backendsto_numpy)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDIMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDOPENAI_CLIP_MEANOPENAI_CLIP_STD)requires) ImageReadMode decode_image)InterpolationMode) pil_to_tensorPIL.Image.Image torch.Tensorc]tRt^UtRtRtRtR#)ChannelDimensionchannels_first channels_lastN)__name__ __module__ __qualname____firstlineno__FIRSTLAST__static_attributes__r#i/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/image_utils.pyr r Us E Dr+r c]tRt^ZtRtRtRtR#)AnnotationFormatcoco_detection coco_panopticr#N)r$r%r&r'COCO_DETECTION COCO_PANOPTICr*r#r+r,r.r.Zs %N#Mr+r.cn\4;'d%\V\PP4#N)r isinstancePILImageimgs&r, is_pil_imager:bs"  E EZSYY__%EEr+c"]tRt^ftRtRtRtRtR#) ImageTypepillowtorchnumpyr#N)r$r%r&r'r6TORCHNUMPYr*r#r+r,r<r<fs C E Er+r<c\V4'd\P#\V4'd\P#\ V4'd\P #\R\V4 24h)zUnrecognized image type ) r:r<r6r r@r rA ValueErrortypeimages&r,get_image_typerGlsXE}}ue /U }= >>r+cd\V4;'g\V4;'g \V4#r4)r:r r r8s&r,is_valid_imagerIvs'   K Ks 3 K Ks7KKr+c$V^8dQhR\/#)images)list)formats"r, __annotate__rOzsEEDEr+c~T;'d5\;QJdRV4F 'dK R# R#!RV44#)c38"TFp\V4xK R#5ir4)rI).0rFs& r, *is_valid_list_of_images..{sDVE.//VFTall)rLs&r,is_valid_list_of_imagesrXzs3  D DccDVDccDcDcDVDDDr+c~\V^,\4'dVUUu.uF qFq"NK K upp#\V^,\P4'd\P!V^R7#\V^,\ P 4'd\ P!V^R7#R#uuppi)raxis)dimN)r5rMnpndarray concatenater>Tensorcat) input_listsublistitems& r,concatenate_listre~s*Q-&&$.CJ747JCC JqM2:: . .~~jq11 JqM5<< 0 0yy++ 1DsB9c\V\\34'd VFp\V4'dKR# R#\ V4'gR#R#)FT)r5rMtuple valid_imagesrI)imgsr9s& r,rhrhsC$u &&C$$ D ! ! r+cb\V\\34'd\V^,4#R#)rF)r5rMrgrIr8s&r, is_batchedrks%#e}%%c!f%% r+cDV^8dQhR\PR\/#)rKrFreturn)r]r^bool)rNs"r,rOrOs552::5$5r+cVP\P8XdR#\P!V4^8;'d\P!V4^8*#)zN Checks to see whether the pixel values have already been rescaled to [0, 1]. F)dtyper]uint8minmaxrEs&r,is_scaled_imagertsA {{bhh 66%=A  4 4"&&-1"44r+cFV^8dQhR\R\\,/#)rKexpected_ndimsrm)intrM ImageInput)rNs"r,rOrOs###D.KF 8dE]33F#%FTc3V"TFp\V4;'gV'*xK! R#5ir4rXrs& r,rSr%YRXh'1AA\ARX))z*Could not make a flat list of images from r5rMrgrWrXr:r{rIrC)rLrvimg_listr9s&& r,make_flat_list_of_imagesrsI" 6D%=)) CKFKCCCKFK K K CYRXYCCCYRXY Y Y$*?FhshF??&4-((-DV-L-L q " "fQinn&FM !9>>^a/ /(.CH(3C(CC Cf   6;;.#@8O ;;.1, ,<  A&J KK@ Ds F,/F2c~V^8dQhR\\,\,R\R\\,/#r~r)rNs"r,rOrOs:$v$v  z )$v$v *$vr+c0\V\\34'dw\;QJdRV4F 'dK RM RM !RV44'd=\;QJdRV4F 'dK RM RM !RV44'dV#\V\\34'd\ V4'dn\ V^,4'gV^,P V8XdV.#V^,P V^,8XdVUu.uFp\V4NK up#\V4'dK\ V4'gVP V8XdV..#VP V^,8Xd \V4.#\R4huupi)aO Ensure that the output is a nested list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of list of images or a list of 4d array of images. c3N"TFp\V\\34xK R#5ir4rrs& r,rS-make_nested_list_of_images..rrFTc3V"TFp\V4;'gV'*xK! R#5ir4rrs& r,rSrrrz]Invalid input type. Must be a single image, a list of images, or a list of batches of images.r)rLrvrFs&& r,make_nested_list_of_imagesrs* 6D%=)) CKFKCCCKFK K K CYRXYCCCYRXY Y Y &4-((-DV-L-L q " "fQinn&F8O !9>>^a/ /-34VEDKV4 4f   6;;.#@H:  ;;.1, ,L> ! t uu5sFc8V^8dQhR\P/#rKrm)r]r^)rNs"r,rOrOs2::r+c\V4'g\R\V4 24h\4'dA\ V\ P P 4'd\P!V4#\V4#)zInvalid image type: ) rIrCrDrr5r6r7r]arrayrr8s&r,to_numpy_arrayrsY #  /S {;<<C!A!Axx} C=r+cV^8dQhR\PR\\\R3,,R,R\/#)rKrF num_channels.Nrm)r]r^rwrgr )rNs"r,rOrO s?$A$A ::$A%(5c?%:T%A$A$Ar+crVeTMRp\V\4'dV3MTpVP^8Xd^^r2MBVP^8Xd^^r2M-VP^8Xd^^r2M\RVP 24hVPV,V9dLVPV,V9d4\ P RVP R24\P#VPV,V9d\P#VPV,V9d\P#\R4h)a7 Infers the channel dimension format of `image`. Args: image (`np.ndarray`): The image to infer the channel dimension of. num_channels (`int` or `tuple[int, ...]`, *optional*, defaults to `(1, 3)`): The number of channels of the image. Returns: The channel dimension of the image. z(Unsupported number of image dimensions: z4The channel dimension is ambiguous. Got image shape z. Assuming channels are the first dimension. Use the [input_data_format](https://huggingface.co/docs/transformers/main/internal/image_processing_utils#transformers.image_transforms.rescale.input_data_format) parameter to assign the channel dimension.z(Unable to infer channel dimension format) r5rwr{rCshapeloggerwarningr r(r))rFr first_dimlast_dims&& r,infer_channel_dimension_formatr s $0#;<L&0s&C&CL?L zzQ8 q8 q8CEJJ<PQQ {{9-%++h2G<2WB5;;-PJ K  %%% Y < /%%% X , .$$$ ? @@r+ctV^8dQhR\PR\\,R,R\/#)rKrFinput_data_formatNrm)r]r^r strrw)rNs"r,rOrOGs8FFbjjFEUX[E[^bEbFnqFr+cVf \V4pV\P8XdVP^, #V\P8XdVP^, #\ RV 24h)ar Returns the channel dimension axis of the image. Args: image (`np.ndarray`): The image to get the channel dimension axis of. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the image. If `None`, will infer the channel dimension from the image. Returns: The channel dimension axis of the image. Unsupported data format: )rr r(r{r)rC)rFrs&&r,get_channel_dimension_axisrGsd :5A,222zzA~ .33 3zzA~ 01B0CD EEr+cV^8dQhR\PR\R,R\\\3,/#)rKrF channel_dimNrm)r]r^r rgrw)rNs"r,rOrO]s=DD"**D3Cd3JDV[\_ad\dVeDr+c(Vf \V4pV\P8Xd'VPR,VPR,3#V\P8Xd'VPR,VPR,3#\ RV 24h)a] Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of. channel_dim (`ChannelDimension`, *optional*): Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. Returns: A tuple of the image's height and width. r)rr r(rr)rC)rFrs&&r,get_image_sizer]s{4U; &,,,{{2 B// (-- -{{2 B//4[MBCCr+c V^8dQhR\\\3,R\R\R\\\3,/#)rK image_size max_height max_widthrm)rgrw)rNs"r,rOrOus@!!c3h!!! 38_ !r+cVwr4W, pW$, p\WV4p\W7,4p\WG,4p W3#)a Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio. Important, even if image_height < max_height and image_width < max_width, the image will be resized to at least one of the edges be equal to max_height or max_width. For example: - input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50) - input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400) Args: image_size (`tuple[int, int]`): The image to resize. max_height (`int`): The maximum allowed height. max_width (`int`): The maximum allowed width. )rrrw) rrrheightwidth height_scale width_scale min_scale new_height new_widths &&& r,#get_image_size_for_max_height_widthrusH,MF&L#KL.IV'(JE%&I   r+c\V^8dQhR\\,R\\,/#)rKvaluesrm)rrrM)rNs"r,rOrOs"88x}8c8r+cP\V!Uu.uFp\V4NK up#uupi)zG Return the maximum value across all indices of an iterable of values. )ziprs)rvalues_is& r,max_across_indicesrs$+.v, 7,hCM, 77 7s#cV^8dQhR\\R\P3,,R\\ ,R\\ ,/#)rKrLrrrm)rMrr]r^rr rw)rNs"r,rOrOsA # # ~rzz12 3 #HKN^H^ # #Y #r+c.V\P8Xd+\VUu.uFq"PNK up4wr4pWE3#V\P8Xd+\VUu.uFq"PNK up4wrEpWE3#\ RV 24huupiuupi)z@ Get the maximum height and width across all images in a batch. z"Invalid channel dimension format: )r r(rrr)rC)rLrr9_rrs&& r,get_max_height_widthrs ,222#5F6SFSyyF6S#T y  "" .33 3#5F6SFSyyF6S#T q  ""=>O=PQRR 7T6Ss B BchV^8dQhR\\\\,3,R\/#rK annotationrmdictrrMrgrn)rNs"r,rOrOs(  4TE\8I3J t r+c\V\4'doRV9dhRV9da\VR,\\34'd>\ VR,4^8Xg%\VR,^,\4'dR#R#)image_id annotationsTFr5rrMrglenrs&r,"is_valid_annotation_coco_detectionrsi:t$$ * $ Z ' z-04- @ @  =) *a /:j>WXY>Z\`3a3a r+chV^8dQhR\\\\,3,R\/#rr)rNs"r,rOrOs(  $sD5L7H2I d r+c\V\4'dvRV9doRV9dhRV9da\VR,\\34'd>\ VR,4^8Xg%\VR,^,\4'dR#R#)r segments_info file_nameTFrrs&r,!is_valid_annotation_coco_panopticrsq:t$$ * $ z ) : % z/2T5M B B  ?+ , 1Z ?@[\]@^`d5e5e r+c~V^8dQhR\\\\\,3,,R\ /#rKrrmrrrrMrgrn)rNs"r,rOrOs2OO(4TE\@Q;R2SOX\Or+cj\;QJdRV4F 'dK R# R#!RV44#)c38"TFp\V4xK R#5ir4)rrRanns& r,rS3valid_coco_detection_annotations..sN+31#66+rUFTrVrs&r, valid_coco_detection_annotationsrs+ 3N+N33N3N3N+N NNr+c~V^8dQhR\\\\\,3,,R\ /#rr)rNs"r,rOrOs2NN$sD5L?P:Q1RNW[Nr+cj\;QJdRV4F 'dK R# R#!RV44#)c38"TFp\V4xK R#5ir4)rrs& r,rS2valid_coco_panoptic_annotations..sM#055rUFTrVrs&r,valid_coco_panoptic_annotationsrs+ 3MM33M3M3MM MMr+c\V^8dQhR\\R3,R\R,RR/#)rKrFrtimeoutNrmrrfloat)rNs"r,rOrOs5** '' (* T\**r+c \\R.4\V\4'Ed:VP R4'gVP R4'dJ\ P P\\P!WRR7P44pM\PPV4'd!\ P PV4pMVP R4'dVPR4^,p\ P"!VP%44p\ P P\V44pM5\V\ P P 4'g \+R 4h\ P,P/V4pVP1R 4pV# \&dp\)RT R T 24hR p?ii;i) a Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionhttp://https://Trfollow_redirects data:image/,Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got . Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager5r startswithr6r7openrhttpxgetcontentospathisfilesplitbase64 decodebytesencode ExceptionrC TypeErrorImageOpsexif_transposeconvert)rFrb64es&& r,rrs j8*-%   I & &%*:*::*F*FIINN7599U^b+c+k+k#lmE WW^^E " "IINN5)E .. C(+ ((8 ws|4 syy / / D   LL ' ' .E MM% E L  ijoipp~@~AB sA G G*G%%G*)backendsc\V^8dQhR\\R3,R\R,RR/#)rKrFrrNrmrr)rNs"r,rOrOs5* * '' (* T\* * r+c,^RIp\V\4'EdiVPR4'gVPR4'de\P !WRR7P pVP!\V4VPR7p\V\PR7#\PPV4'd\V\PR7#VPR4'dVP!R 4^,p\"P$!VP'44pTP!\T4TPR7p\T\PR7#\V\,P.P.4'd:\,P0P3V4p\5VP7R 44#\9R 4h \(dp\+R T R T 24hRp?ii;i)aP Loads `image` directly to a `torch.Tensor` using torchvision. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `torch.Tensor`: A `[C, H, W]` uint8 tensor in RGB channel order. NrrTr)rp)moderrrrrz`Incorrect format used for image. Should be a URL, a local path, a base64 string, or a PIL image.)r>r5rrrrr frombuffer bytearrayrqrrrrrrrrrrrrCr6r7rr rr r)rFrr>rawbufr s&& r,load_image_as_tensorrs"%   I & &%*:*::*F*F))ETJRRC""9S>EC-*;*;< < WW^^E " "M,=,=> > .. C(+ ((8 ""9S>EC-*;*;< < E399?? + + ++E2U]]5122 n    ijoipp~@~AB s$G22 H=HHc V^8dQhR\\\\R3,R\R,R\R\R,\\R,,3,/#)rKrLrrNrm)rrMrgrr)rNs"r,rOrO,sU33 $s$55 63AF3 d#45tDAR??X]H^H^DK=)&& I_?r+Nc VPV4\V4'dVP4p\V\P 4'dVf,\VP ^,\P4pVP^8Xd,VP^,R9dVP^^^4pV'd V^,pVP\P4p\PPV4#V#)a Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`): The image to convert to the PIL Image format. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. r)r/r r?r5r]r^flatfloatingr{r transposeastyperqr6r7 fromarray)r.rFrescales&&&r, to_pil_image(ImageFeatureExtractionMixin.to_pil_image{s %%e, 5 ! !KKME eRZZ ( ($UZZ]BKK@zzQ5;;q>V#;1a0 LL*E99&&u- - r+c VPV4\V\PP4'gV#VP R4#)zo Converts `PIL.Image.Image` to RGB format. Args: image (`PIL.Image.Image`): The image to convert. r)r/r5r6r7r r-s&&r, convert_rgb'ImageFeatureExtractionMixin.convert_rgbs; %%e,%11L}}U##r+cd<V^8dQhRS[PRS[S[,RS[P/#)rKrFscalerm)r]r^rrw)rN __classdict__s"r,rO(ImageFeatureExtractionMixin.__annotate__s.RZZ  r+c 4VPV4W,#)z' Rescale a numpy image by scale amount )r/)r.rFr>s&&&r,r7#ImageFeatureExtractionMixin.rescales %%e,}r+c VPV4\V\PP4'd\P !V4p\ V4'dVP4pVf,\VP^,\P4MTpV'd0VPVP\P4R4pV'd%VP^8XdVP^^^4pV#)a{ Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first dimension. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to convert to a NumPy array. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise. channel_first (`bool`, *optional*, defaults to `True`): Whether or not to permute the dimensions of the image to put the channel dimension first. p?)r/r5r6r7r]rr r?r2integerr7r5float32r{r4)r.rFr7 channel_firsts&&&&r,r*ImageFeatureExtractionMixin.to_numpy_arrays %%e, eSYY__ - -HHUOE 5 ! !KKME;B?*UZZ]BJJ7PW LLbjj!99EE UZZ1_OOAq!,E r+c VPV4\V\PP4'dV#\ V4'dVP ^4pV#\ P!V^R7pV#)z Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to expand. rZ)r/r5r6r7r unsqueezer] expand_dimsr-s&&r,rK'ImageFeatureExtractionMixin.expand_dimsse %%e, eSYY__ - -L 5 ! !OOA&E NN5q1E r+c VPV4\V\PP4'dVP VRR7pMV'd\V\ P 4'd1VPVP\ P4R4pM1\V4'd!VPVP4R4p\V\ P 4'd\V\ P 4'g0\ P!V4PVP4p\V\ P 4'g0\ P!V4PVP4pM\V4'd^RIp\W%P4'gF\V\ P 4'dVP !V4pMVP"!V4p\W5P4'gF\V\ P 4'dVP !V4pMVP"!V4pVP$^8Xd6VP&^,R9dWR,, VR,, #W, V, #)a Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to normalize. mean (`list[float]` or `np.ndarray` or `torch.Tensor`): The mean (per channel) to use for normalization. std (`list[float]` or `np.ndarray` or `torch.Tensor`): The standard deviation (per channel) to use for normalization. rescale (`bool`, *optional*, defaults to `False`): Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically. T)r7NrDr)NNNNN)r/r5r6r7rr]r^r7r5rFr rrrpr>r` from_numpytensorr{r)r.rFmeanstdr7r>s&&&&& r, normalize%ImageFeatureExtractionMixin.normalizes %%e, eSYY__ - -''t' eRZZ ( (dBJJ//xx~,,U[[9c2::..hhsm**5;;7 U # # dLL11dBJJ// ++D1D <<-Dc<<00c2::..**3/C,,s+C ::?u{{1~7//3}3EE ELC' 'r+c (VeTM\PpVPV4\V\P P 4'gVP V4p\V\4'd \V4p\V\4'g\V4^8XdV'd-\V\4'dW"3MV^,V^,3pMVPwrgWg8:dWg3MWv3wr\V\4'dTMV^,p W8XdV#T \W,V, 4rVe7WZ8:d\RV RV 24hW8d\W[,V , 4TrWg8:dW3MW3pVPW#R7#)a Resizes `image`. Enforces conversion of input to PIL.Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`int` or `tuple[int, int]`): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*, defaults to `None`): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: image: A resized `PIL.Image.Image`. z max_size = zN must be strictly greater than the requested size for the smaller edge size = )r&)r'BILINEARr/r5r6r7r8rMrgrwrr%rCresize) r.rFr%r&default_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longs &&&&&& r,rW"ImageFeatureExtractionMixin.resizesb< (389K9T9T %%e,%11%%e,E dD ! !;D dC CIN '1$'<'<|47DQRGBT % 16uovo .8s.C.Cda#/ L&93?R?Y\a?a;b8'6()(4@@DvG *.1(2F2Q.RT\805 ,hEZ||D|44r+c NVPV4\V\4'gW"3p\V4'g!\V\P 4'dbVP ^8XdVPV4pVP^,R9dVPR,MVPR,pM&VP^,VP^,3pV^,V^,, ^,pWB^,,pV^,V^,, ^,pWb^,,p\V\PP4'dVPWdWu34#VP^,R9pV'gX\V\P 4'dVP^^^4p\V4'dVP^^^4pV^8d0WS^,8:d#V^8dWs^,8:dVRWE1Wg13,#VPRR\V^,V^,4\V^,V^,43,p \V\P 4'd\P !WR7p M"\V4'dVP#V 4p V R,V^,, ^,p W^,,p V R,V^,, ^,p W^,,pVX RW1W13&WK, pW[, pWm, pW}, pV R\^V4\%V PR,V41\^V4\%V PR,V413,p V #) a= Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). :rNN:NrKN.N)rrrr)r/r5rgr r]r^r{rKrr%r6r7cropr4permuters zeros_like new_zerosrr)r.rFr% image_shapetopbottomleftrightrG new_shape new_imagetop_pad bottom_padleft_pad right_pads&&& r, center_crop'ImageFeatureExtractionMixin.center_cropYs %%e,$&&1a71~- bMKN2q81~- AF #w)8+==>    Qs9??2#6??QPST]TcTcdfTginPoAo o r+c VPV4\V\PP4'dVP V4pVRRR1RR3,#)ah Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first. NrNr)r/r5r6r7rr-s&&r,flip_channel_order.ImageFeatureExtractionMixin.flip_channel_ordersL %%e, eSYY__ - -''.ETrT1aZ  r+c  VeTM\PPpVPV4\ V\PP4'gVP V4pVP W#WEWgR7#)a Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees counter clockwise around its centre. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before rotating. Returns: image: A rotated `PIL.Image.Image`. )r&expandcenter translate fillcolor)r6r7NEARESTr/r5r8rotate)r.rFangler&rvrwrxrys&&&&&&&&r,r{"ImageFeatureExtractionMixin.rotatesj (389J9J %%e,%11%%e,E|| Vi  r+r#r4)NT)F)NTN)NrNNN)r$r%r&r'__doc__r/r8r;r7rrKrSrWrprsr{r*__classdictcell__r?s@r,r+r+osQ< $@(2(hA5FIV!"  r+r+cpV^8dQhR\R\\R3,R\\,RR/#)rKannotation_formatsupported_annotation_formats.rrmN)r.rgrMr)rNs"r,rOrOs?'"'(8#(=">d r+cW9d\R\ RV 24hV\PJd\ V4'g \R4hV\P Jd\ V4'g \R4hR#R#)zUnsupported annotation format: z must be one of zInvalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.zInvalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.N)rCrNr.r1rr2r)rrrs&&&r,validate_annotationsrs <:6(BRSoRpqrr,;;;/ <<B  ,:::.{;;M <;r+c\V^8dQhR\\,R\\,/#)rKvalid_processor_keyscaptured_kwargs)rMr)rNs"r,rOrOs&LL$s)Ld3iLr+c\V4P\V44pV'd-RPV4p\P RV R24R#R#)z, zUnused or unrecognized kwargs: rzN)set differencejoinrr)rr unused_keysunused_key_strs&& r,validate_kwargsrsJo&11#6J2KLK;/88HJKr+ca]tRtRtoRtRtRtRtRtRt Rt Rt RRlt Rt RtRtR tR tV3R lR ltV3R lRltV3RltRtVtR#)SizeDictiz6 Hashable dictionary to store image size information. NcX\W4'd \W4#\RV R24h)Key z not found in SizeDict.)hasattrgetattrKeyErrorr.keys&&r, __getitem__SizeDict.__getitem__s- 4  4% %cU"9:;;r+cX\W4'd\W4e \W4#V#r4rr)r.rdefaults&&&r,r SizeDict.gets' 4  '$"4"@4% %r+c#"\V4F.p\WP4pVfKVPV3xK0 R#5ir4)rrname)r.fvals& r,__iter__SizeDict.__iter__s4A$'Cffck!s %AAc\VPVPVPVPVP VP 34#r4)hashrr longest_edge shortest_edgerr)r.s&r,__hash__SizeDict.__hash__ s<T[[$**d.?.?ASASUYUdUdfjftftuvvr+cB\W4;'d\W4RJ#r4rrs&&r, __contains__SizeDict.__contains__st!DDgd&8&DDr+cr\W4'g\RV R24h\PWV4R#)rz" is not a valid field of SizeDict.N)rrobject __setattr__)r.rvalues&&&r, __setitem__SizeDict.__setitem__s2t!!T#&HIJ J4e,r+caa\S\4'd\S4S8H#\S\4'd\;QJd .V3Rl\ S44FNK 5M!V3Rl\ S444\;QJd".V3Rl\ S44FNK 58H#!V3Rl\ S4448H#\ #)c3P<"TFp\SVP4xK R#5ir4rr)rRrr.s& r,rS"SizeDict.__eq__..sE 1qvv.. #&c3P<"TFp\SVP4xK R#5ir4r)rRrothers& r,rSrs#O0<1qvv&& r)r5rrrgrNotImplemented)r.rsffr,__eq__SizeDict.__eq__s eT " ":& & eX & &5Et E55Et EEO06t O O06t OJ r+c<V^8dQhRR/#)rKrmrr#)rNr?s"r,rOSizeDict.__annotate__!szr+c\V\\,4'd1\V4pVP\V44\R/VB#\#)Nr#)r5rrupdaterr.rmergeds&& r,__or__SizeDict.__or__!s@ eTH_ - -$ZF MM$u+ &%f% %r+c <V^8dQhRS[/#r)r)rNr?s"r,rOr(sr+c\V\4'd(\V4pVP\V44V#\#r4)r5rrrrs&& r,__ror__SizeDict.__ror__(s3 eT " "%[F MM$t* %Mr+c<V^8dQh/S[R,;R&S[R,;R&S[R,;R&S[R,;R&S[R,;R&S[R,;R&#)rKNrrrrrr)rw)rNr?s"r,rOrsk $J  :*#:$d !Tz r+r#r4)r$r%r&r'r~rrrrrrrrrrrrrrr__annotate_func__r*rrs@r,rrskFE#L $M!J I<  "wE- sr+r)rr4) torchvision) NNNNNNNNNNNN)frrcollections.abcr dataclassesrriortypingrrrr?r]utilsr r r r r rrrrutils.constantsrrrrrrutils.import_utilsr PIL.Imager6 PIL.ImageOpsr7 Resamplingr'torchvision.iorrtorchvision.transformsr!torchvision.transforms.functionalrrz NEAREST_EXACTBOXrVHAMMINGBICUBICLANCZOSpil_torch_interpolation_mappingitemstorch_pil_interpolation_mappingr> get_loggerr$rr^rMrxr r.rrrwAnnotationTyper:r<rGrIrXrerhrkrtr|rrrrrrrrr(rrrrrrrrr)r+rrr)kvs00r,rs $)    )--:8? ""$5$C$C 1 5 5##%6%?%?""$5$=$=""$5$=$=""$5$=$= '#9X8]8]8_&`8_qt8_&`#&(#&(#   H %rzz>48I3JDQSQ[Q[L\^bcq^rr | $|$ c39tDz112F  ?LE,  5#L#LL$vN$ANF,D0!>8brawaw #   ON*Z #$* %* Z3*+^\\ \ ~ 2L == =['as> K