+ jc&Rt^RIt^RIt^RIHt^RIHt^RIH t H t ^RI H t H t ^RIHtHtHtHtHt^RIHtHt^RIHtHtR R ltRR R llt!R R]RR7t]!RR]44tR.tR#)z"Image processor class for Pixtral.N) functional)TorchvisionBackend) BatchFeature get_size_dict)group_images_by_shapereorder_images)ChannelDimension ImageInputPILImageResamplingSizeDictget_image_size) ImagesKwargsUnpack) TensorTypeauto_docstringcV^8dQhR\\\3,R\\\3,R\/#) image_size patch_sizereturn)tupleint)formats"u/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/pixtral/image_processing_pixtral.py __annotate__rs2//%S//uS#X/SV/cVwr#\V\\34'dTMW3wrEV^, V,^,pV^, V,^,pWv3#)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. ) isinstancerlist)rrheightwidth patch_height patch_widthnum_width_tokensnum_height_tokenss&& r_num_image_tokensr%sZMF.8eT].S.S ZdYqL k1A5! 4q8  ..rc V^8dQhR\R\\\\3,,\\,,\\,,R\\\\3,,\\,,\\,,R\\ ,R,R\/#)r input_imagesizerinput_data_formatNr)r rrrstrr)rs"rrr1s%L%L%L c3h $s) +eCj 8%LeCHo%S 1E#J>%L--4 %L  %Lrc\V\\34'dTMW3wrE\V\\34'dTMW"3wrg\W4wr\ W, W, 4p V ^8dK\ \ P!W, 44p\ \ P!W, 44p \W3Wg34wrW,W,3#)a Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: input_image (`ImageInput`): The image to resize. size (`int` or `tuple[int, int]`): Max image size an input image can be. Must be a dictionary with the key "longest_edge". patch_size (`int` or `tuple[int, int]`): The patch_size as `(height, width)` to use for resizing the image. If patch_size is an integer, `(patch_size, patch_size)` will be used input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `tuple`: The target (height, width) dimension of the output image after resizing. ) rrrr maxrmathfloorr%) r'r(rr) max_height max_widthr!r"rr ratior$r#s &&&& rget_resize_output_image_sizer21s0%/teT]$C$CD$J.8eT].S.S ZdYqL";BMF #U%6 7E qyTZZ/0DJJu}-.*;VOlMh*i'  +-=-K KKrc0a]tRt^YtoRtV3RltRtVtR#)PixtralImageProcessorKwargsz 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. cL<V^8dQh/S[S[S[3,S[,;R&#)rr)dictr*r)r __classdict__s"rr(PixtralImageProcessorKwargs.__annotate__Ys S#X$$ rN)__name__ __module__ __qualname____firstlineno____doc____annotate_func____static_attributes____classdictcell__)r7s@rr4r4Ysrr4F)totalcaa]tRt^bto]P t.ROt.ROtR^R^/t RR/t Rt Rt Rt RtRt]tRR.tV3RlV3R llt]V3R lV3R ll4tRV3R lV3R llltV3RlRltRV3RlRlltRtVtV;t#)PixtralImageProcessorrr longest_edgeiT pixel_values image_sizesc0<V^8dQhRS[S[,/#)rkwargs)rr4)rr7s"rr"PixtralImageProcessor.__annotate__rs##(C!D#rc (<\SV`!R/VBR#)Nr9)super__init__)selfrI __class__s&,rrMPixtralImageProcessor.__init__rs "6"rc<<V^8dQhRS[RS[S[,RS[/#)rimagesrIr)r rr4r)rr7s"rrrJvs)444v>Y7Z4_k4rc &<\SV`!V3/VB#N)rL preprocess)rNrRrIrOs&&,rrU PixtralImageProcessor.preprocessusw!&3F33rc 2<V^8dQhRRRS[RS[RRRR/#)rimage torch.Tensorr(rresample7PILImageResampling | tvF.InterpolationMode | int | Noner)r )rr7s"rrrJys>% % % %  % L %  % rc <VP'dVPVP3pMHVP'd,VP'dVPVP3pM \R4hVP'd,VP'dVPVP3pM \R4h\ WVR7p\ S V`!V3R\V^,V^,R7RV/VB#)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(rr()rr rZ)rErr ValueErrorr2rLresizer ) rNrXr(rrZrI size_tuplepatch_size_tuple output_sizerOs &&&&&, rr^PixtralImageProcessor.resizeys,    ++T->->?J [[[TZZZ++tzz2J_` `    !1!1!1 * 1 1:3C3CD LM M25Vfg w~   Ak!nM X` dj  rc^<V^8dQhRS[R,RS[S[S[S[3,,RR/#)rrFrYrGr)rrr)rr7s"rrrJs;##>*#%S/*#  #rc  l\RV44\RV443p\W4UUu.uF^wrE\PPP V^V^,V^,, ^V^,V^,, 3R7NK` ppp\P !V4#uuppi)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. c32"TF q^,xK R#5i)Nr9.0ss& r :PixtralImageProcessor._pad_for_batching..s3{!1{c32"TF q^,xK R#5i)Nr9rgs& rrjrks8S{!1{rl)pad)r,ziptorchnnrrostack)rNrFrG max_shapeimgr(paddeds&&& r_pad_for_batching'PixtralImageProcessor._pad_for_batchings"3{33S8S{8S5ST !; ;  HH   # #Ca1Q1GIVWL[_`a[bLb-c # d;  {{6""  sA$B0c<<V^8dQhRS[R,RS[RS[RRRS[RS[R S[R S[R S[R S[S[S[,,R ,RS[S[S[,,R ,RS[R ,RS[S[,R ,RS[S[S[3,S[,R ,RS[/#)rrRrY do_resizer(rZr[do_center_crop crop_size do_rescalerescale_factor do_normalize image_meanN image_stddisable_groupingreturn_tensorsrr) rboolr floatr*rr6rr)rr7s"rrrJs3 3 ^$3 3  3 L 3  3 3 3 3 3 DK'$.3 4;&-3 +3 j(4/3 cNX-43 " #3 rc p\T;'g VPRR7p\R /VBp\WR7wpp/pVP 4F(wppV'dVP VVVVR7pVVV&K* \ VV4p\VV R7wppVUu.uFpVPR RNK pp/pVP 4F9wppV'dVPVV4pVPVWxWV 4pVVV&K; \ VV4pVPVVR7p\RVRV/V R7#uupi) T)default_to_square)r)rXr(rrZN)rFrGrFrG)data tensor_typer9) rrr ritemsr^rshape center_croprescale_and_normalizerwr)rNrRrzr(rZr{r|r}r~rrrrrrrI patch_size_sdgrouped_imagesgrouped_images_indexresized_images_groupedkeystacked_imagesresized_imagesrXbatch_image_sizesprocessed_images_groupedprocessed_images padded_imagess&&&&&&&&&&&&&&&, r _preprocess!PixtralImageProcessor._preprocessss$#:#@#@TXY  .:. /DV/o,,!##1#7#7#9 C!%(t X`"-"+9 "3 ' $: ((>@TU/D^fv/w,,;IJ>%U[[->J#% #1#7#7#9 C!%!1!1.)!L!77 LV_N-; $S ) $:**BDXY..))/  -@QR`n  #KsD3r9)g3<4'?gwg M?gy{ ?)gB91?gwt.?g U?rT)r:r;r<r=r BICUBICrZrrrr(rrzr}rdo_convert_rgbr4 valid_kwargsmodel_input_namesrMrrUr^rwrr@rA __classcell__)rOr7s@@rrDrDbs!))H4J4IB,J D !DIJLN.L'7##444% % N##03 3 3 rrDrT)r>r-rqtorchvision.transforms.v2rtvFimage_processing_backendsrimage_processing_utilsrrimage_transformsrr image_utilsrr r r r processing_utilsr rutilsrrr%r2r4rD__all__r9rrrsl) 7;AEee4//(%LP%,e%H .H H V # #r