bizfddlZddlZddlmZddlmZddlmZddlm Z m Z ddl Z ddl Z ddlmZmZmZmZmZmZmZmZmZddlmZmZmZmZmZmZerqddlZ ddl!Z e j"j#Z$erQdd l%m&Z&e$j'e&j(e$j)e&j)e$j*e&j*e$j+e&j+e$j,e&j,e$j-e&j-iZ.niZ.erddl/Z/ej0e1Z2e d e j3d e4d e4e j3e4d fZ5Gd d eZ6GddeZ7e8e9e:e9ze4e8zfZ;dZ<GddeZ=dZ>dZ?de4fdZ@dZAdZBdZCde j3deDfdZEdSde:de4e5fdZF dSde4e5e5zde:de5fd ZG dSde4e5e5zde:de4e5fd!ZHde j3fd"ZI dTde j3d#e:eJe:d$fzdzde6fd%ZKdTde j3d&e6e9zdzde:fd'ZLdTde j3d(e6dzdeJe:e:ffd)ZMd*eJe:e:fd+e:d,e:deJe:e:ffd-ZNd.e8e9e4eJzfdeDfd/ZOd.e8e9e4eJzfdeDfd0ZPd1ee8e9e4eJzfdeDfd2ZQd1ee8e9e4eJzfdeDfd3ZRdTde e9d fd4eSdzdd fd5ZT dTde e4eJe9d fd4eSdzde d e4d e4e4d ffd6ZU dUd7eDdzd8eSdzd9eDdzd:eSe4eSzdzd;eSe4eSzdzdeDdzd?e8e9e:fdzd@eDdzdAe8e9e:fdzdBe dCdDe dEfdFZVGdGdHZWdIe7dJeJe7d$fd1e4e8ddfdKZXdLe4e9dMe4e9fdNZYedOPGdQdRZZdS)VN)Iterable) dataclass)BytesIO)OptionalUnion) ExplicitEnumis_numpy_arrayis_torch_availableis_torch_tensoris_torchvision_availableis_vision_availableloggingrequires_backendsto_numpy)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDIMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDOPENAI_CLIP_MEANOPENAI_CLIP_STD)InterpolationModezPIL.Image.Imagez torch.TensorceZdZdZdZdS)ChannelDimensionchannels_first channels_lastN)__name__ __module__ __qualname__FIRSTLASTS/root/projects/butler/venv/lib/python3.11/site-packages/transformers/image_utils.pyrrOs E DDDr#rceZdZdZdZdS)AnnotationFormatcoco_detection coco_panopticN)rrrCOCO_DETECTION COCO_PANOPTICr"r#r$r&r&Ts%N#MMMr#r&c\tot|tjjSN)r isinstancePILImageimgs r$ is_pil_imager2\s!  EZSY_%E%EEr#ceZdZdZdZdZdS) ImageTypepillowtorchnumpyN)rrrr.TORCHNUMPYr"r#r$r4r4`s C E EEEr#r4ct|r tjSt|r tjSt |r tjStdt|)NzUnrecognized image type ) r2r4r.r r8r r9 ValueErrortypeimages r$get_image_typer?fsgE}ue =U == > >>r#c\t|pt|pt|Sr,)r2r r r0s r$is_valid_imagerAps*    Ks 3 3 Ks7K7KKr#imagesc8|otd|DS)Nc34K|]}t|VdSr,)rA.0r>s r$ z*is_valid_list_of_images..us*DDE.//DDDDDDr#all)rBs r$is_valid_list_of_imagesrJts$  DcDDVDDDDDDr#c,t|dtr d|DSt|dtjrtj|dSt|dt jrt j|dSdS)Nrcg|] }|D]}| Sr"r")rFsublistitems r$ z$concatenate_list..zs%CCC7CC4CCCCr#axis)dim)r-listnpndarray concatenater6Tensorcat) input_lists r$concatenate_listrZxs*Q-&&,CCJCCCC JqM2: . .,~jq1111 JqM5< 0 0,y++++,,r#ct|ttfr|D]}t|sdSnt |sdSdS)NFT)r-rStuple valid_imagesrA)imgsr1s r$r]r]sd$u &&  C$$ uu  D ! !u 4r#cht|ttfrt|dSdS)NrF)r-rSr\rAr0s r$ is_batchedr`s/#e}%%&c!f%%% 5r#r>returnc|jtjkrdStj|dkotj|dkS)zV Checks to see whether the pixel values have already been rescaled to [0, 1]. Frr)dtyperTuint8minmaxr=s r$is_scaled_imagergs? {bhu 6%==A  4"&--1"44r#expected_ndimsc Ft|r|St|r|gSt|rP|j|dzkrt |}n0|j|kr|g}n!t d|dzd|d|jd|St dt |d)a Ensure that the output is a list of images. If the input is a single image, it is converted to a list of length 1. If the input is a batch of images, it is converted to a list of images. Args: images (`ImageInput`): Image of images to turn into a list of images. expected_ndims (`int`, *optional*, defaults to 3): Expected number of dimensions for a single input image. If the input image has a different number of dimensions, an error is raised. rz%Invalid image shape. Expected either z or z dimensions, but got z dimensions.z]Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, or torch.Tensor, but got .)r`r2rAndimrSr;r<rBris r$make_list_of_imagesrns& Fxf  ;.1, , ,&\\FF [N * *XFF.8J..P^..K...  whlmshthtwww  r#cRt|ttfr>td|Dr%td|Dr d|DSt|ttfrWt |rHt |ds|dj|kr|S|dj|dzkr d|DSt|r:t |s |j|kr|gS|j|dzkrt|Std|)a Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1. If the input is a nested list of images, it is converted to a flat 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 images or a 4d array of images. c3NK|] }t|ttfV!dSr,r-rSr\rFimages_is r$rGz+make_flat_list_of_images..0KK 8dE]33KKKKKKr#c3:K|]}t|p| VdSr,rJrrs r$rGz+make_flat_list_of_images..2YYh'11A\YYYYYYr#cg|] }|D]}| Sr"r"rFimg_listr1s r$rOz,make_flat_list_of_images..s%???h??s????r#rrcg|] }|D]}| Sr"r"rys r$rOz,make_flat_list_of_images..s%CCCH(CC3CCCCCr#z*Could not make a flat list of images from r-rSr\rIrJr2rlrAr;rms r$make_flat_list_of_imagesr}sX" 6D%=))@ KKFKKK K K@ YYRXYYY Y Y@ @?F????&4-((D-DV-L-LD q " " fQin&F&FM !9>^a/ / /CCCCC Cf    6;.#@#@8O ;.1, , ,<<  J&JJ K KKr#c>t|ttfr4td|Drtd|Dr|St|ttfrXt |rIt |ds|dj|kr|gS|dj|dzkr d|DSt|r.rtr#c3:K|]}t|p| VdSr,rvrrs r$rGz-make_nested_list_of_images..rwr#rrc,g|]}t|Sr")rSrEs r$rOz.make_nested_list_of_images..s444EDKK444r#z]Invalid input type. Must be a single image, a list of images, or a list of batches of images.r|rms r$make_nested_list_of_imagesrsF 6D%=)) KKFKKK K K YYRXYYY Y Y  &4-((5-DV-L-L5 q " " fQin&F&F8O !9>^a/ / /44V444 4f"    6;.#@#@H:  ;.1, , ,LL> ! t u uur#ct|stdt|tr3t |t jjrtj|St|S)NzInvalid image type: ) rAr;r<rr-r.r/rTarrayrr0s r$to_numpy_arrayrsl #  =;S ;;<<<C!A!Ax}} C==r# num_channels.c||nd}t|tr|fn|}|jdkrd\}}n9|jdkrd\}}n(|jdkrd\}}ntd|j|j||vr>|j||vr/t d |jd tjS|j||vr tjS|j||vr tj Std ) a[ 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. Nrrhrh)r)rrz(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) r-intrlr;shapeloggerwarningrr r!)r>r first_dimlast_dims r$infer_channel_dimension_formatrs/$0#;<<L&0s&C&CUL??L zQ" 88 q" 88 q" 88PEJPPQQQ {9--%+h2G<2W2W K5; K K K    %% Y < / /%% X , . .$$ ? @ @@r#input_data_formatc|t|}|tjkr |jdz S|tjkr |jdz St d|)a 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. NrhrUnsupported data format: )rrr rlr!r;)r>rs r$get_channel_dimension_axisrAsf :5AA,222zA~ .3 3 3zA~ D1BDD E EEr# channel_dimc|t|}|tjkr|jd|jdfS|tjkr|jd|jdfSt d|)a Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of. channel_dim (`ChannelDimension`, *optional*): Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. Returns: A tuple of the image's height and width. Nr)rrr rr!r;)r>rs r$get_image_sizerWsz4U;; &,,,{2 B// (- - -{2 B//B[BBCCCr# image_size max_height max_widthc|\}}||z }||z }t||}t||z}t||z} || fS)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. )rer) rrrheightwidth height_scale width_scale min_scale new_height new_widths r$#get_image_size_for_max_height_widthros_,MFE&Le#KL+..IVi'((JEI%&&I y  r# annotationct|trfd|vrbd|vr^t|dttfrWXY>Z\`3a3a /t 5r#ct|trjd|vrfd|vrbd|vr^t|dttfr.s+NN31#66NNNNNNr#rHrs r$ valid_coco_detection_annotationsrs NN+NNN N NNr#c4td|DS)Nc34K|]}t|VdSr,)rrs r$rGz2valid_coco_panoptic_annotations..s+MM#055MMMMMMr#rHrs r$valid_coco_panoptic_annotationsrs MMMMM M MMr#timeoutc ttdgt|tr\|ds|drHt jttj ||dj }ntj |r t j|}n|dr|dd} t!j|}t jt|}nU#t&$r}t)d |d |d }~wwxYwt|t jjst+d t j|}|d }|S)a3 Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionzhttp://zhttps://T)rfollow_redirectsz data:image/,rzIncorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got z. Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager-str startswithr.r/openrhttpxgetcontentospathisfilesplitbase64 decodebytesencode Exceptionr; TypeErrorImageOpsexif_transposeconvert)r>rb64es r$rrsj8*---%   I & & %*:*::*F*F INN759UG^b+c+c+c+k#l#lmmEE W^^E " " INN5))EE .. , C((+ (88 ws||44    BjoBB@BB sy / /  D    L ' ' . .E MM% E LsAE"" F,FFct|ttfrMt|r0t|dttfrfd|DSfd|DSt |S)aLoads images, handling different levels of nesting. Args: images: A single image, a list of images, or a list of lists of images to load. timeout: Timeout for loading images. Returns: A single image, a list of images, a list of lists of images. rc,g|]}fd|DS)c2g|]}t|SrrrFr>rs r$rOz*load_images...s&QQQEZw777QQQr#r")rF image_grouprs r$rOzload_images..s/lllVaQQQQ[QQQlllr#c2g|]}t|Srrrs r$rOzload_images..s&KKK5Jug666KKKr#r)r-rSr\rr)rBrs `r$ load_imagesrs&4-((3 v;; L:fQi$?? Llllleklll lKKKKFKKK K&'2222r# do_rescalerescale_factor do_normalize image_mean image_stddo_padpad_sizedo_center_crop crop_size do_resizesizeresamplePILImageResampling interpolationrc |r|td|r|td|r||td|r|td| | td| r| | | tddSdSdS)a Checks validity of typically used arguments in an `ImageProcessor` `preprocess` method. Raises `ValueError` if arguments incompatibility is caught. Many incompatibilities are model-specific. `do_pad` sometimes needs `size_divisor`, sometimes `size_divisibility`, and sometimes `size`. New models and processors added should follow existing arguments when possible. Nz=`rescale_factor` must be specified if `do_rescale` is `True`.zgDepending on the model, `size_divisor` or `pad_size` or `size` must be specified if `do_pad` is `True`.zP`image_mean` and `image_std` must both be specified if `do_normalize` is `True`.z<`crop_size` must be specified if `do_center_crop` is `True`.zbOnly one of `interpolation` and `resample` should be specified, depending on image processor type.zO`size` and `resample/interpolation` must be specified if `do_resize` is `True`.)r;) rrrrrrrrrrrrrs r$validate_preprocess_argumentsrs.Zn,XYYY  (" u   m+y/@klllY)+WXXX X%9 p   l$*0D Hajkkkll0D0DHaHar#ceZdZdZdZddZdZdejde e zdejfd Z dd Z d Z ddZddZdZdZddZdS)ImageFeatureExtractionMixinzD Mixin that contain utilities for preparing image features. ct|tjjtjfs/t |s"t dt|ddSdS)Nz Got type zU which is not supported, only `PIL.Image.Image`, `np.ndarray` and `torch.Tensor` are.)r-r.r/rTrUr r;r<selfr>s r$_ensure_format_supportedz4ImageFeatureExtractionMixin._ensure_format_supported-sq%#)/2:!>?? X]H^H^ &DKK&&&     r#Nc||t|r|}t|tjr|%t|jdtj}|jdkr&|j ddvr| ddd}|r|dz}| tj }tj|S|S)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. Nrrhrrr)rr r7r-rTrUflatfloatingrlr transposeastyperdr.r/ fromarray)rr>rescales r$ to_pil_imagez(ImageFeatureExtractionMixin.to_pil_image4s %%e,,, 5 ! ! "KKMME eRZ ( ( .$UZ]BK@@zQ5;q>V#;#;1a00 $ LL**E9&&u-- - r#c||t|tjjs|S|dS)z Converts `PIL.Image.Image` to RGB format. Args: image (`PIL.Image.Image`): The image to convert. r)rr-r.r/rrs r$ convert_rgbz'ImageFeatureExtractionMixin.convert_rgbRsE %%e,,,%11 L}}U###r#r>scalerac6||||zS)z7 Rescale a numpy image by scale amount )r)rr>rs r$rz#ImageFeatureExtractionMixin.rescale`s" %%e,,,u}r#Tc||t|tjjrt j|}t |r|}|%t|jdtj n|}|r3| | tj d}|r"|j dkr|ddd}|S)a Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first dimension. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to convert to a NumPy array. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise. channel_first (`bool`, *optional*, defaults to `True`): Whether or not to permute the dimensions of the image to put the channel dimension first. Nrp?rhrr)rr-r.r/rTrr r7rintegerrrfloat32rlr)rr>r channel_firsts r$rz*ImageFeatureExtractionMixin.to_numpy_arraygs %%e,,, eSY_ - - $HUOOE 5 ! ! "KKMME;B?*UZ]BJ777PW  FLLbj!9!99EEE  -UZ1__OOAq!,,E r#c||t|tjjr|St |r|d}nt j|d}|S)z Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to expand. rrP)rr-r.r/r unsqueezerT expand_dimsrs r$rz'ImageFeatureExtractionMixin.expand_dimssq %%e,,, eSY_ - - L 5 ! ! 2OOA&&EEN5q111E r#Fc||t|tjjr||d}n|rt|t jr4||t j d}n7t|r(|| d}t|t jrt|t js,t j ||j }t|t js,t j ||j }nt|rddl}t||js;t|t jr|j|}n|j|}t||js;t|t jr|j|}n|j|}|jdkr-|jddvr||ddddfz |ddddfz S||z |z S)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)rrrNrhr)rr-r.r/rrTrUrrr r floatrrcr6rW from_numpytensorrlr)rr>meanstdrr6s r$ normalizez%ImageFeatureExtractionMixin.normalizes6 %%e,,, eSY_ - - ?''t'<> eRZ ( ( ,dBJ// :x~~,,U[99c2:.. 8hsmm**5;77 U # # , LLLdEL11 .dBJ//.+5+D11DD'5<--Dc5<00 ,c2:..,*%*3//CC&%,s++C :??u{1~77DD$//3qqq$}3EE EDLC' 'r#c||n tj}||t|tjjs||}t|trt|}t|tst|dkr|r*t|tr||fn|d|df}n|j \}}||kr||fn||f\}} t|tr|n|d} || kr|S| t| | z|z } } |8|| krtd|d|| |krt|| z| z |} } ||kr| | fn| | f}| ||S)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`. Nrrz max_size = zN must be strictly greater than the requested size for the smaller edge size = )r)rBILINEARrr-r.r/rrSr\rrrr;resize) rr>rrdefault_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longs r$rz"ImageFeatureExtractionMixin.resizes< (3889K9T %%e,,,%11 -%%e,,E dD ! ! ;;D dC  [CIINN  ['1$'<'<Td||47DQRGBT % v16&ufoovuo t.8s.C.C&Pdda#/// L&93?RUY?Y\a?a;b;b8 '#666(G(GG@DGG (**.1(Y2F2Q.R.RT\8 05 8,,hPYEZ||D8|444r#cV||t|ts||f}t|st|tjrN|jdkr||}|jddvr|jddn|jdd}n|j d|j df}|d|dz dz}||dz}|d|dz dz}||dz}t|tj j r| ||||fS|jddv}|sWt|tjr| ddd}t|r|ddd}|dkr-||dkr!|dkr||dkr|d||||fS|jddt|d|dt|d|dfz} t|tjrt j|| } n$t|r|| } | d|dz dz} | |dz} | d |dz dz} | |dz}|| d| | | |f<|| z }|| z }|| z }|| z }| dtd|t%| jd|td|t%| jd |f} | S) a Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). rrrrN.r)rr)rr-r\r rTrUrlrrrr.r/croprpermuterf zeros_like new_zerosre)rr>r image_shapetopbottomleftrightr  new_shape new_imagetop_pad bottom_padleft_pad right_pads r$ center_cropz'ImageFeatureExtractionMixin.center_cropsd %%e,,,$&& $1a7{1~- bMKN2q8{1~- AF #wz)8I+==> w'   Qs9?2#6???QPST]TcdfTginPoPoAo o r#c||t|tjjr||}|dddddddfS)a Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first. Nr)rr-r.r/rrs r$flip_channel_orderz.ImageFeatureExtractionMixin.flip_channel_order]s` %%e,,, eSY_ - - /''..ETTrT111aaaZ  r#rc||ntjj}||t |tjjs||}|||||||S)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`. N)rexpandcenter translate fillcolor)r.r/NEARESTrr-rrotate)rr>anglerr4r5r6r7s r$r9z"ImageFeatureExtractionMixin.rotatens} (3889J %%e,,,%11 -%%e,,E|| HVFicl   r#r,)NT)F)NTN)NrNNN)rrr__doc__rrrrTrUrrrrrrrr0r2r9r"r#r$rr(s < $ $ $RZ  @(2(2(2(2(hA5A5A5A5FIIIV!!!"      r#rannotation_formatsupported_annotation_formatsc||vrtdtd||tjurt |std|tjurt |stddSdS)NzUnsupported annotation format: z must be one of zInvalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.zInvalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.)r;formatr&r)rr*r)r<r=rs r$validate_annotationsr@s  <<<q6qqSoqqrrr,;;;/ << B  ,:::.{;; M ;:  r#valid_processor_keyscaptured_kwargsct|t|}|r5d|}td|ddSdS)Nz, zUnused or unrecognized kwargs: rk)set differencejoinrr)rArB unused_keysunused_key_strs r$validate_kwargsrIspo&&11#6J2K2KLLKL;//JJJJKKKKKLLr#T)frozenceZdZUdZdZedzed<dZedzed<dZedzed<dZ edzed<dZ edzed<dZ edzed<d Z dS) SizeDictz> Hashable dictionary to store image size information. Nrr longest_edge shortest_edgerrcht||rt||Std|d)NzKey z not found in SizeDict.)hasattrgetattrKeyError)rkeys r$ __getitem__zSizeDict.__getitem__s> 4   &4%% %:c:::;;;r#) rrrr;rr__annotations__rrMrNrrrTr"r#r$rLrLsFC$JE3:#L#*### $M3:$$$!Jd !!! IsTz   <<<<rjsZ  $$$$$$!!!!!!""""""""                       --!! -<<<<<<  &(9(G  "$5$9  '):)C  &(9(A  &(9(A  &(9(A + ''+-'LLL  H % %rz>48I3JDQSQ[L\^bcq^rr | $$$$$|$$$ c39tDz112FFF  ???LLLEDEEEE,,,    52:5$5555###D 4TE\8I3J t     $sD5L7H2I d     O(4TE\@Q;R2SOX\OOOON$sD5L?P:Q1RNW[NNNN''eC!223'edl'Vg''''VQU33 $s$55 63AF3 d#45tDAR