Zj& pSrSSKrSSKrSSKJr SSKJr SSKJ r J r SSK J r J r SSKJrJrJrJrJr SS KJrJr SS KJrJr S \\\4S \\\4S \4SjrSS\S\\\\4-\\-\\-S \\\\4-\\-\\-S\\-S-S \4 Sjjr"SS\SS9r \"SS\55r!S/r"g)z"Image processor class for Pixtral.N) functional)TorchvisionBackend) BatchFeature get_size_dict)group_images_by_shapereorder_images)ChannelDimension ImageInputPILImageResamplingSizeDictget_image_size) ImagesKwargsUnpack) TensorTypeauto_docstring image_size patch_sizereturncUup#[U[[45(aUOX4upEUS- U-S-nUS- U-S-nXv4$)a, Calculate the number of image tokens given the image size and patch size. Args: image_size (`tuple[int, int]`): The size of the image as `(height, width)`. patch_size (`tuple[int, int]`): The patch size as `(height, width)`. Returns: `int`: The number of image tokens. ) isinstancetuplelist)rrheightwidth patch_height patch_widthnum_width_tokensnum_height_tokenss څ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/pixtral/image_processing_pixtral.py_num_image_tokensr"sZMF.8eT].S.S ZdYqL k1A5! 4q8  .. input_imagesizeinput_data_formatc[U[[45(aUOX4upE[U[[45(aUOX"4upg[X5up[ X- X- 5n U S:aB[ [ R"X- 55n[ [ R"X- 55n [X4Xg45upX-X-4$)a Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: input_image (`ImageInput`): The image to resize. size (`int` or `tuple[int, int]`): Max image size an input image can be. Must be a dictionary with the key "longest_edge". patch_size (`int` or `tuple[int, int]`): The patch_size as `(height, width)` to use for resizing the image. If patch_size is an integer, `(patch_size, patch_size)` will be used input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `tuple`: The target (height, width) dimension of the output image after resizing. r) rrrrmaxintmathfloorr") r$r%rr& max_height max_widthrrrrratior rs r!get_resize_output_image_sizer/1s0%/teT]$C$CD$J.8eT].S.S ZdYqL";BMF #U%6 7E qyTZZ/0DJJu}-.*;VOlMh*i'  +-=-K KKr#c4\rSrSr%Sr\\\4\-\S'Sr g)PixtralImageProcessorKwargsYz patch_size (`Union[dict[str, int], int]` *optional*, defaults to `{"height": 16, "width": 16}`): Size of the patches in the model, used to calculate the output image size. rN) __name__ __module__ __qualname____firstlineno____doc__dictstrr)__annotations____static_attributes__r3r#r!r1r1Ys S#X$$r#r1F)totalc^\rSrSr\R r/SQr/SQrSSS.r SS0r Sr Sr Sr SrSr\rS S /rS \\4U4S jjr\S \S \\S\4U4Sjj5rS%SSS\S\SSSS4 U4SjjjrS \SS \\\\4SS4SjrS%S \SS\S\SSS\S\S\S\ S\S\ \\ -S-S \ \\ -S-S!\S-S"\!\"-S-S\#\!\4\-S-S\4S#jjr$S$r%U=r&$)&PixtralImageProcessorb)g3<4'?gwg M?gy{ ?)gB91?gwt.?g U?rr longest_edgeiT pixel_values image_sizeskwargsc &>[TU]"S0UD6 g)Nr3)super__init__)selfrF __class__s r!rIPixtralImageProcessor.__init__rs "6"r#imagesrc &>[TU]"U40UD6$N)rH preprocess)rJrMrFrKs r!rP PixtralImageProcessor.preprocessusw!&3F33r#Nimagez torch.Tensorr%rresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonec >UR(aURUR4nOFUR(a*UR(aURUR4nO [S5eUR(a*UR(aURUR4nO [S5e[ XUS9n[ T U]"U4[USUSS9US.UD6$)a% Resize an image. The longest edge of the image is resized to size["longest_edge"], with the aspect ratio preserved. Output dimensions are aligned to patch_size. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dict containing the longest possible edge of the image. patch_size (`SizeDict`): Patch size used to calculate the size of the output image. resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): Resampling filter to use when resizing the image. z@size must contain either 'longest_edge' or 'height' and 'width'.z-patch_size must contain 'height' and 'width'.)r%rrrrB)r%rS)rCrr ValueErrorr/rHresizer ) rJrRr%rrSrF size_tuplepatch_size_tuple output_sizerKs r!rVPixtralImageProcessor.resizeys,   ++T->->?J [[TZZ++tzz2J_` `   !1!1 * 1 1:3C3CD LM M25Vfg w~   Ak!nMX` dj  r#c 8[SU55[SU554n[X5VVs/sHCupE[RRR USUSUS- SUSUS- 4S9PME nnn[R "U5$s snnf)a; Pads images to form a batch of same shape. Args: pixel_values (`list[torch.Tensor]`): A list of pixel values, each of shape (channels, height, width). image_sizes (`list[tuple[int, int]]`): A list of (height, width) for each image. Returns: `torch.Tensor`: Stacked and padded images. c3*# UH oSv M g7f)rNr3.0ss r! :PixtralImageProcessor._pad_for_batching..s3{!1{c3*# UH oSv M g7f)rNr3r]s r!r`ras8S{!1{rbrr)pad)r(ziptorchnnrrdstack)rJrDrE max_shapeimgr%paddeds r!_pad_for_batching'PixtralImageProcessor._pad_for_batchings"3{33S8S{8S5ST !; ;  HH   # #Ca1Q1GIVWL[_`a[bLb-c # d;  {{6""  sA B do_resizedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stddisable_groupingreturn_tensorsc \[U=(d URSS9n[S0UD6n[XS9unn0nUR 5H$unnU(aUR UUUUS9nUUU'M& [ UU5n[UU S9unn[[U55Vs/sH nUUSPM nn0nUR 5H8unnU(aURUU5nURUXxXU 5nUUU'M: [ UU5nURUUS9n[UUS.U S9$s snf) NT)default_to_square)rv)rRr%rrSr)rDrE)data tensor_typer3) rrr ritemsrVr rangelen center_croprescale_and_normalizerlr)rJrMrnr%rSrorprqrrrsrtrurvrwrrF patch_size_sdgrouped_imagesgrouped_images_indexresized_images_groupedshapestacked_imagesresized_imagesibatch_image_sizesprocessed_images_groupedprocessed_images padded_imagess r! _preprocess!PixtralImageProcessor._preprocesssy$#:#@TXY  .:. /DV/o,,!#%3%9%9%; !E>!%(t X`"-"-; "5 ) &< ((>@TU/D^fv/w,,AFsK_G`AabAaA1!4Q7Aab#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N/= $U + &<**BDXY..))/ "/@QR`n  #csD)r3rO)'r4r5r6r7r BICUBICrSrtrurr%ryrnrqrsdo_convert_rgbr1 valid_kwargsmodel_input_namesrrIrr rrPr rVrrr)rlboolfloatr:rr9rr< __classcell__)rKs@r!r?r?bs!))H4J4I,J D !DIJLN.L'7#(C!D#44v>Y7Z4_k44OS % % %  % L %  % % N#>*#%S/*#  #N8<3 ^$3 3  3 L 3  3 3 3 3 3 DK'$.3 4;&-3 +3 j(4/3 cNX-43 " #3 3 r#r?rO)#r8r*rftorchvision.transforms.v2rtvFimage_processing_backendsrimage_processing_utilsrrimage_transformsrr image_utilsr r r r rprocessing_utilsrrutilsrrrr)r"rr:r/r1r?__all__r3r#r!rs) 7;AEee4//%S//uS#X/SV/08< %L%L c3h $s) +eCj 8%LeCHo%S 1E#J>%L--4 %L  %LP%,e%H .H H V # #r#