Zj\ SSKrSSKrSSKrSSKJr SSKJr SSK J r SSK J r J r SSKJrJrJrJrJrJr SSKJrJr SS KJrJr "S S \S S 9rSrS"S\S\S\S-S\S-S\\\44 SjjrS#S\S\S\ \!\4S-S\\\44Sjjr"SSS\S\\\44Sjr#S\$\$SS\\\44Sjr%S\$\$SS\4Sjr&S\$\$SSS4Sjr'\"S S!\55r(S!/r)g)$N) functional)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STD ImageInputPILImageResamplingSizeDictmake_nested_list_of_images) ImagesKwargsUnpack) TensorTypeauto_docstringcB\rSrSr%Sr\\S'\\\ 4\S'\\S'Sr g)SmolVLMImageProcessorKwargs,aZ do_image_splitting (`bool`, *optional*, defaults to `True`): Whether to split the image into sub-images concatenated with the original image. They are split into patches such that each patch has a size of `max_image_size["height"]` x `max_image_size["width"]`. max_image_size (`Dict`, *optional*, defaults to `{"longest_edge": 364}`): Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key "longest_edge". return_row_col_info (`bool`, *optional*, defaults to `False`): Whether to return the row and column information of the images. do_image_splittingmax_image_sizereturn_row_col_infoN) __name__ __module__ __qualname____firstlineno____doc__bool__annotations__dictstrint__static_attributes__rڅ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/smolvlm/image_processing_smolvlm.pyrr,s#cN"r%rF)totaliheightwidthmin_lenmax_lenreturncUc [X5OUnX- nX:aUn[X- 5nUS-S:waUS- nO"X:aUn[X-5nUS-S:waUS- n[X5n[X5nX4$)a Get the output size of the image after resizing given a dictionary specifying the max and min sizes. Args: height (`int`): Height of the input image. width (`int`): Width of the input image. min_len (`int`, *optional*, defaults to 1): Minimum size of the output image. max_len (`int`, *optional*, defaults to the maximum size of the image): Maximum size of the output image. Returns: The output size of the image after resizing. rmaxr#)r(r)r*r+ aspect_ratios r&&_resize_output_size_rescale_to_max_lenr3?s"%,Oc& G>L U)* A:? aKF F)* 19> QJE !F  E =r%cUc [X5OUnX- nX:aX:aUn[X- 5nOX:aX:aUn[X-5n[US5n[US5nX4$)a Get the output size of the image after resizing given a dictionary specifying the max and min sizes. Args: height (`int`): Height of the input image. width (`int`): Width of the input image. max_len (`Dict[str, int]`, *optional*, defaults to the maximum size of the image): Defines the maximum dimensions of the image. Returns: The output size of the image after resizing. r/r0)r(r)r+r2s r&+_resize_output_size_scale_below_upper_boundr5dsr%,Oc& G>L 5?U)* F,F)*^F qME =r%image torch.Tensorresolution_max_sidecbURSSup#[X#US9up#[X#[S9up#X#4$)a Get the output size of the image after resizing given a dictionary specifying the max and min sizes. Args: image (`torch.Tensor`): Image to resize. resolution_max_side (`int`): The longest edge of the image will be resized to this value. The shortest edge will be resized to keep the input aspect ratio. Returns: The output size of the image after resizing. Nr+)shaper3r5MAX_IMAGE_SIZE)r6r8r(r)s r&get_resize_output_image_sizer>s>KK$MF;6RefMF?WefMF =r% images_listztorch.Tensor|np.ndarrayc/nUH*nUH!nURURSS5 M# M, [SU55n[SU55nXE4$)z@ Get the maximum height and width across all images in a batch. r:Nc3*# UH oSv M g7f)rNr.0sizes r& 'get_max_height_width..s5!Wc3*# UH oSv M g7fr/NrrBs r&rErFs4 G rG)appendr<r1)r? image_sizesimagesr6 max_height max_widths r&get_max_height_widthrOsaKE   u{{23/ 0555J4 44I  ""r%c`UHnU(dM USRSs $ [S5e)zg Get the number of channels across all images in a batch. Handle empty sublists like in [[], [image]]. rzNo images found in the batch.)r< ValueErrorr?rLs r&get_num_channelsrSs4 6!9??1% % 4 55r%z torch.devicecFUHnU(dM USRs $ g)zz Get the device from the first non-empty element in a nested list of images. Handle empty sublists like in [[], [image]]. rN)devicerRs r&get_device_from_imagesrVs#  6!9## #r%c t^\rSrSr\R r\r\ r SS0r SS0r Sr SrSrSrSrSrSr\rSS/rS \\4U4S jjr\S \S \\S \4U4S jj5rS1S \S\S \4SjjrS2SSS\SSS S4U4Sjjjr S2S \!RDS\#\$\4SS4Sjjr%S2S\!RDS\SS4Sjjr&S3S\!RDS\'\\4S\S\(4Sjjr)S \*\*SS\(S\SSS \(S!\+S"\(S#\+\*\+-S-S$\+\*\+-S-S%\(S-S&\(S-S\#\$\4S-S'\(S-S(\(S-S)\$\,-S-S \4 S*jr-U4S+jr.S,\S-\S.\#4S/jr/S0r0U=r1$)4SmolVLMImageProcessor longest_edgeiilTF pixel_valuespixel_attention_maskkwargsc &>[TU]"S0UD6 g)Nr)super__init__)selfr] __class__s r&r`SmolVLMImageProcessor.__init__s "6"r%rLr,c &>[TU]"U40UD6$N)r_ preprocess)rarLr]rbs r&rf SmolVLMImageProcessor.preprocesssw!&3F33r%expected_ndimsc6URU5n[XS9$)z3 Prepare a nested images structure for processing. )rh) fetch_imagesr)rarLrhs r&_prepare_images_structure/SmolVLMImageProcessor._prepare_images_structures ""6*)&PPr%Nr6r7rDresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonec >UR(a[XRS9nOFUR(a*UR(aURUR4nO [ S5e[ TU]"U[USUSS94SU0UD6$)a Resize an image. The longest edge of the image is resized to size.longest_edge, with the shortest edge resized to keep the input aspect ratio. Can also be used with size.height and size.width. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Size of the output image. resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): Resampling filter to use when resizing the image. )r8zJsize must be a dictionary with key 'longest_edge' or 'height' and 'width'.rr/r(r)rm)rZr>r(r)rQr_resizer )rar6rDrmr]new_sizerbs r&rpSmolVLMImageProcessor.resizesv$   3EO`O`aH [[TZZ TZZ0Hij jw~eXXa[QR %Tr_grkqrrr%rc"UR5upEpgSupUS=p/n Xj:dX{:a[R"Xj- 5n [R"X{- 5nURXU S9RXU S9R 5R XESX5R SSSSS 5n XnnURU[UUS 9US 9n[R"XRS54SS 9n OS upURS5n U /U-n U/U-nXU4$)a Split an image into squares of side max_image_size and the original image resized to max_image_size. That means that a single image becomes a sequence of images. This is a "trick" to spend more compute on each image with no changes in the vision encoder. 1) If one side of the original image is larger than `max_image_size`, resize it to `max_image_size` while preserving the aspect ratio. 2) Divide the resulting image into `ceil(height / max_image_size)` x `ceil(width / max_image_size)` sub-images of the same size each (image_size, image_size). Typically, 364x364. 3) Returns the list of the crops and the original image, in addition to the number of splits for the height and the width. Args: images (`torch.Tensor`): Images to split. max_image_size (`Dict[str, int]`): Maximum size of the output image. If the image is larger than this size, it will be split into patches of this size, and the original image will be concatenated with the patches, resized to max_size. resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): Resampling filter to use when resizing the image. )r.rrZ)rDsteprr.r/rrorm)dim)rr) rDmathceilunfold contiguousviewpermuterpr torchcat unsqueeze)rarLrrm batch_size num_channelsr(r) height_dim width_dimrMrNframes num_splits_h num_splits_wglobal_image_heightglobal_image_widths r& split_images"SmolVLMImageProcessor.split_imagess2.39++-/ & $ !/!??   %"399V%89L99U%67L j K BjJJAq!Q' 7A!3 [[(;CUVai!FYY(8(8(;AXXE-.FYYv?@CZZF ^YYv?@CZZF-.EIIe=>AXXE67{{5({CCr% padded_sizefillreturn_pixel_maskc^URSSnUSUS- nUSUS- nUS:dUS:a[SUSUS35eXR:waSSXv4n[R"XUSS9nSn U(a?[R "US SSS2SS24[R S 9n SU SUS2SUS24'X4$) Nr:rr/zzPadding dimensions are negative. Please make sure that the padded size is larger than the original size. Got padded size: z, original size: .constant)r padding_mode.)dtype)r<rQtvFpadr zeros_likeint64) rar6rrr original_sizepadding_bottom padding_rightpadding pixel_masks r&rSmolVLMImageProcessor.padSs BC( $Q-*::#Aq)99 A !233>-?PQ^P__`b   '!];GGGEJOE ))%Q1 *=U[[QJABJ)q))+=]1-=+== >  r% do_resize do_rescalerescale_factor do_normalize image_mean image_stddo_padrrdisable_groupingreturn_tensorsc [USUS9unn0nUR5H"unnU(aURUX4S9nUUU'M$ [UUSS9n[USUS9unn0nU (a0n0nUR5H=unnUR UU SUS9nUR UXS9unnnUUU'UUU'UUU'M? [UUSS9n[UUSS9n[UUSS9n[ U5H(unnUVV s/sHnUHn U PM M sn nUU'M* OUR5H*unnURU[U SU SS9US9nUUU'M, [UUSS9nUVs/sHnS /[U5-PM nnUVs/sHnS /[U5-PM nn[USUS9unn0n!UR5HunnURUXVXxU 5nUU!U'M! [U!USS9nU (a[S U55n"[U5un#n$[U5n%[U5n&[R"[U5U"/U%U#U$4Q7S U&06n'[R"[U5U"/U#U$4Q7S U&06n([ U5H<unn[ U5H'un)n UR!U U#U$45uU'UU)4'U(UU)4'M) M> U'nU (aUW(S .n*OHUS :Xa>S[R""UVs/sHn[R""U5PM sn50n*OSU0n*[%U*US9n+U (a UU+S'UU+S'U+$s sn nfs snfs snfs snf)z* Process a batch of images for the model. T) is_nestedrrw)rrZ)rrmro)r6rDrmrc38# UHn[U5v M g7fre)len)rCimages_s r&rE4SmolVLMImageProcessor._preprocess..s N=M'W=MsrU)r[r\ptr[)data tensor_typerowscols)ritemsrprrr enumerater rrescale_and_normalizer1rOrSrVrzerosrstackr),rarLrrDrmrrrrrrrrrrrr]grouped_imagesgrouped_images_indexresized_images_groupedr<stacked_imagesresized_imagessplit_images_grouped rows_grouped cols_groupedrrprocessed_imagesi group_imagessublistr6processed_images_groupedmax_num_imagesrMrNrrUprocessed_images_paddedpixel_attention_masksjrencodings, r& _preprocess!SmolVLMImageProcessor._preprocessqsC.0E d5E0 ,,"$%3%9%9%; !E>!%^T!U,: "5 )&<((>@T`de/D d=M0 ,, " LL)7)=)=)?%~!%!?!?"N>$BX"@".2->->">.?.*d/=$U+&* U#&* U#*@ ..BDXdhi !,0DPTUD!,0DPTUD#,-=#><!77 LV_N/= $U + &< **BDXdhi  N=M NNN$89I$J !J +,<=L+,<=F&+kk$%' I6' ' # %*KK$%%i(% % ! ''78 6 )& 1HAuQUQYQY I6RN+AqD13HA3N!29 7  $4NcdD t #"EKKSc0dScV1DSc0d$efD"$45DT~F #HV #HV C'_FEV1es5L5$L;M+ M cl>[TU]5nURSS5 URSS5 U$)N_valid_processor_keysr)r_to_dictpop)ra encoder_dictrbs r&rSmolVLMImageProcessor.to_dicts7w( 0$7.5r%r(r) images_kwargscURSUR5nURSUR5nURSUR5nS=n=pU(Ga[ XUSS9up[ X[ S9upX!- n X!:aP[R"X%S- 5US-n [X*- 5n [R"XS- 5US-n OTX:aO[R"XS- 5US-n [X-5n [R"X%S- 5US-n US=pW U :dW U:a8[R"X- 5n[R"W U- 5n X-S-nXxU 4$)aG A utility that returns number of image patches for a given image size. Args: height (`int`): Height of the input image. width (`int`): Width of the input image. images_kwargs (`dict`) Any kwargs to override defaults of the image processor. Returns: `int`: Number of patches per image. rrrDrrZr;r/) getrrrDr3r5r=ryrzr#)rar(r)rrrrD num_patchesnum_rowsnum_colsr2 resized_widthresized_heightrMrNs r&get_number_of_image_patches1SmolVLMImageProcessor.get_number_of_image_patchess+../CTE\E\]&**+;T=P=PQ  3,-- -h B6Z^_mZnoMFG_mnMF >L $ %2P*P QTbcqTr r !$U%9!:!%6>4R+R!SVdesVt!t!%6>4R+R!SVdesVt!t #F$9 : $ %2P*P QTbcqTr r %3N%C CJ *mi.G99^%@A99]Y%>?&1A5 h..r%r)rre)rT)2rrrrr LANCZOSrmr rr rrDrrrrdo_convert_rgbrrrr valid_kwargsmodel_input_namesrr`rr rrfr#rkr rprTensorr!r"rrtuplerrlistfloatrrrrr$ __classcell__)rbs@r&rXrXs!))H'J%I G $D$c*NIJLN F.L')?@#(C!D#44v>Y7Z4_k44Q QCQXbQOS sssL s  ss>OS 92 92S#X92L 92~OS D||D"%DL DF"& !||!38_! !  !rrOrSrVrX__all__rr%r&rs.  7;2E5/ ,e  MQ" ""&)Dj"?BTz" 38_"L?C &*38nt&; 38_>  38_0 #d40I+J&K #PUVY[^V^P_ #6$t,E'F"G6C6$T.-A(B$~$Z/.Z/Z/z # #r%