S\ \;S\ \S\ \;S\ \S\ SS\ \ \?\'4S \ \4S!jj5r@S4S"S#S$\ \>S%S#4S&jjrAS'\\S%\=\4S(jrBS)\=S#S%\C\>4S*jrDS+\ \ RS#4S,\>S%\=\ \ RS#44S-jrF"S.S/\ S0S19rG\("S2S3\55rHg)5)Iterable)deepcopy) lru_cachepartial)AnyOptional TypedDictUnionN)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbget_resize_output_image_sizeget_size_with_aspect_ratiogroup_images_by_shapereorder_images) ChannelDimension ImageInput ImageTypeSizeDictget_image_size#get_image_size_for_max_height_widthget_image_typeinfer_channel_dimension_formatmake_flat_list_of_imagesvalidate_kwargsvalidate_preprocess_arguments)Unpack) TensorTypeauto_docstringis_torch_availableis_torchvision_availableis_torchvision_v2_availableis_vision_availablelogging)is_rocm_platform)PILImageResampling)pil_torch_interpolation_mapping) functional maxsize do_rescalerescale_factor do_normalize image_mean image_stddo_padsize_divisibilitydo_center_crop crop_size do_resizesizeresampler(return_tensors data_formatc[UUUUUUUUUU U U S9 U bU S:wa [S5eU [R:wa [S5eg)z Checks validity of typically used arguments in an `ImageProcessorFast` `preprocess` method. Raises `ValueError` if arguments incompatibility is caught. ) r.r/r0r1r2r3r4r5r6r7r8r9Nptz6Only returning PyTorch tensors is currently supported.z6Only channel first data format is currently supported.)r ValueErrorrFIRST)r.r/r0r1r2r3r4r5r6r7r8r9r:r;s `/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/image_processing_utils_fast.py"validate_fast_preprocess_argumentsrAKsk*"%!+%  !n&<QRR&,,,QRR-tensor torch.TensoraxisreturnclUcUR5$URUS9$![a Us$f=f)z> Squeezes a tensor, but only if the axis specified has dim 1. )rE)squeezer>)rCrEs r@ safe_squeezerIvs@ |~~~~4~((  s $ 33valuescN[U6Vs/sHn[U5PM sn$s snf)zG Return the maximum value across all indices of an iterable of values. )zipmax)rJvalues_is r@max_across_indicesrOs$+.v, 7,hCM, 77 7s"imagesc`[UVs/sHoRPM sn5up#nX44$s snf)z@ Get the maximum height and width across all images in a batch. )rOshape)rPimg_ max_height max_widths r@get_max_height_widthrWs3 22O992OPA9  ""3Ps+image patch_sizec/n[U[RS9up4[SX15H9n[SXA5H&nUSS2XUU-2XfU-24nUR U5 M( M; U$)a Divides an image into patches of a specified size. Args: image (`Union[np.array, "torch.Tensor"]`): The input image. patch_size (`int`): The size of each patch. Returns: list: A list of Union[np.array, "torch.Tensor"] representing the patches. ) channel_dimrN)rrr?rangeappend)rXrYpatchesheightwidthijpatchs r@divide_to_patchesrdsrG"56F6L6LMMF 1f )q%,A!QZ/^1CCDE NN5 !-* NrBc~\rSrSr%\\\S'\\\\ 4\S'\\\S'\\ S\S'\\\S'\\\\ 4\S'\\\S '\\ \ \ 4\S '\\\S '\\ \ \ \ 4\S '\\ \ \ \ 4\S '\\\S'\\ \\ 4\S'\\\S'\\ \\4\S'\S\S'\\\S'Srg)DefaultFastImageProcessorKwargsr7r8default_to_squarer(F.InterpolationModer9r5r6r.r/r0r1r2do_convert_rgbr:r;input_data_format torch.devicedevicedisable_groupingN)__name__ __module__ __qualname____firstlineno__rbool__annotations__dictstrintr floatlistr r__static_attributes__rprBr@rfrfs~ 4S> ""~%uHIJJTN"S#X''U3:.//4. ud5k1233eT%[0122TN"U3 ?344*++c+;&; <== ^ $$tn$rBrfF)totalcz^\rSrSrSrSrSrSrSrSr Sr Sr Sr Sr SrSrSr\R$rSrSrS/r\rSrS\\SS4U4SjjrS>S S S \S S S\SS 4 Sjjr\S>S S S\ \!\!4S \"S S\SS 4 Sjj5r#S S S\$SS 4Sjr%S S S\&\$\'\$4S\&\$\'\$4SS 4Sjr(\)"SS9S?S\"\S\"\&\$\*\$4S\"\&\$\*\$4S\"\S\"\$S\"SS\ 4S jj5r+S!S S\S\$S\S\&\$\*\$4S\&\$\*\$4SS 4S"jr,S S S \-\.\!4SS 4S#jr/S \0S\04S$jr1S\-4S%jr2S@S!\0S&\!S\04S'jjr3SAS \0S(\"\S)\"\&\.\4S\"SSS 4 S*jjr4SBS!\0S(\"\S)\"\&\.\4S\"SS&\!S\*S 4 S+jjr5S?S \"\S,\"\S-\"\S\"\&\$\*\$4S\"\&\$\*\$4S.\"\S\-4S/jjr6SCS\"\S\"\$S\"\S\"\&\$\ \$4S\"\&\$\ \$4S0\"\S \"\S1\"\S,\"\S2\"\&S3S4\"\&\.\74S.\"\4S5jjr8S!\0S\\S\94S6jr:\;S!\0S\\S\94S7j5rU4S<jr?S=r@U=rA$)DBaseImageProcessorFastNTgp? pixel_valueskwargsrFc \>[TU]"S0UD6 URU5nURSUR5nUb#[ X!RSUR 5S9OSUlURSUR5nUb [ USS9OSUlURRHEnURUS5nUb[XU5 M&[X[[XS555 MG [URRR55Ulg)Nr8rhr8rhr6 param_namerp)super__init__filter_out_unused_kwargspopr8rrhr6 valid_kwargsrvsetattrrgetattrr{keys_valid_kwargs_names)selfrr8r6keykwarg __class__s r@rBaseImageProcessorFast.__init__s "6"..v6zz&$)), tzzBUW[WmWm7n o JJ{DNN; MVMby[Ihl$$44CJJsD)E 5)8GDt,D#EF 5$((9(9(I(I(N(N(P#Q rBrXrDr8 interpolationrj antialiasc FUbUO[RRnUR(aDUR(a3[ UR 5SSURUR5nOUR(a%[UURS[RS9nOUR(aDUR(a3[UR 5SSURUR5nOJUR(a*UR(aURUR4nO[SUS35e[ R"R%5(a!['5(aUR)XX45$[R*"XX4S9$)a Resize an image to `(size["height"], size["width"])`. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`. Returns: `torch.Tensor`: The resized image. NF)r8rhrlzjSize must contain 'height' and 'width' keys, or 'max_height' and 'max_width', or 'shortest_edge' key. Got .rr)FInterpolationModeBILINEAR shortest_edge longest_edgerr8rrr?rUrVrr_r`r>torchcompiler is_compilingr'compile_friendly_resizeresize)rrXr8rrrnew_sizes r@rBaseImageProcessorFast.resizes<,*7)B H[H[HdHd   $"3"32 RS!""!!H   3''"'"2"8"8 H __:5::<;Ldoo_c_m_mnH [[TZZ TZZ0H6  >> & & ( (-=-?-?//Z Zxx}ZZrBrcUR[R:XaUR5S- n[R "XX#S9nUS-n[R "US:SU5n[R "US:SU5nUR5R[R5nU$[R "XX#S9nU$)zk A wrapper around `F.resize` so that it is compatible with torch.compile when the image is a uint8 tensor. rr) dtyperuint8rzrrwhereroundto)rXrrrs r@r.BaseImageProcessorFast.compile_friendly_resize$s ;;%++ %KKMC'EHHUM_ECKEKK S%8EKK 1e4EKKM$$U[[1E HHUM_E rBscalec X-$)z Rescale an image by a scale factor. image = image * scale. Args: image (`torch.Tensor`): Image to rescale. scale (`float`): The scaling factor to rescale pixel values by. Returns: `torch.Tensor`: The rescaled image. rp)rrXrrs r@rescaleBaseImageProcessorFast.rescale9s $}rBmeanstdc 0[R"XU5$)a Normalize an image. image = (image - image_mean) / image_std. Args: image (`torch.Tensor`): Image to normalize. mean (`torch.Tensor`, `float` or `Iterable[float]`): Image mean to use for normalization. std (`torch.Tensor`, `float` or `Iterable[float]`): Image standard deviation to use for normalization. Returns: `torch.Tensor`: The normalized image. )r normalize)rrXrrrs r@r BaseImageProcessorFast.normalizeMs*{{5,,rBr+r,r0r1r2r.r/rnrmcU(a=U(a6[R"X&S9SU- -n[R"X6S9SU- -nSnX#U4$)Nrng?F)rrC)rr0r1r2r.r/rns r@!_fuse_mean_std_and_rescale_factor8BaseImageProcessorFast._fuse_mean_std_and_rescale_factordsI ,j@C.DXYJ Y>#BVWIJj00rBrPc URUUUUUURS9upVnU(a/URUR[R S9XV5nU$U(aUR X5nU$)z Rescale and normalize images. )r0r1r2r.r/rn)r)rrnrrrfloat32r)rrPr.r/r0r1r2s r@rescale_and_normalize,BaseImageProcessorFast.rescale_and_normalizeusx-1,R,R%!!)== -S- ) z ^^FIIEMMI$BJZF \\&9F rBc URb URc[SUR535e[R "XSUS45$)aj Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Args: image (`"torch.Tensor"`): Image to center crop. size (`dict[str, int]`): Size of the output image. Returns: `torch.Tensor`: The center cropped image. z=The size dictionary must have keys 'height' and 'width'. Got r_r`)r_r`r>rr center_crop)rrXr8rs r@r"BaseImageProcessorFast.center_cropsQ& ;; $**"4\]a]f]f]h\ijk k}}U(^T']$CDDrBc[U5$)z Converts an image to RGB format. Only converts if the image is of type PIL.Image.Image, otherwise returns the image as is. Args: image (ImageInput): The image to convert. Returns: ImageInput: The converted image. )r)rrXs r@r%BaseImageProcessorFast.convert_to_rgbse$$rBcURcU$URH4nX!;dM [RSUS35 URU5 M6 U$)z: Filter out the unused kwargs from the kwargs dictionary. z!This processor does not use the `z ` parameter. It will be ignored.) unused_kwargslogger warning_oncer)rr kwarg_names r@r/BaseImageProcessorFast.filter_out_unused_kwargssW    %M,,J###&G |Ss$tu :&- rBexpected_ndimsc[XS9$)z Prepare the images structure for processing. Args: images (`ImageInput`): The input images to process. Returns: `ImageInput`: The images with a valid nesting. r)r)rrPrs r@_prepare_images_structure0BaseImageProcessorFast._prepare_images_structures(NNrBrkrlc[U5nU[R[R[R4;a[ SU35eU(aUR U5nU[R:Xa[R"U5nO8U[R:Xa$[R"U5R5nURS:XaURS5nUc [U5nU[R :Xa!UR#SSS5R5nUbUR%U5nU$)NzUnsupported input image type rr )rrPILTORCHNUMPYr>rr pil_to_tensorr from_numpy contiguousndim unsqueezerrLASTpermuter)rrXrkrlrn image_types r@_process_image%BaseImageProcessorFast._process_images$E* immY__iooN Nu E   0 5 5 5MM!Q*557E  HHV$E rBc VURXS9n[URX#US9n[U5S:=(a [ US[ [ 45nU(a)UVV s/sHoV s/sH o"U 5PM sn PM n nn U $UV s/sH o"U 5PM n n U $s sn fs sn nfs sn f)aZ Prepare image-like inputs for processing. Args: images (`ImageInput`): The image-like inputs to process. do_convert_rgb (`bool`, *optional*): Whether to convert the images to RGB. input_data_format (`str` or `ChannelDimension`, *optional*): The input data format of the images. device (`torch.device`, *optional*): The device to put the processed images on. expected_ndims (`int`, *optional*): The expected number of dimensions for the images. (can be 2 for segmentation maps etc.) Returns: List[`torch.Tensor`]: The processed images. rrkrlrnr)rrrlen isinstancer{tuple) rrPrkrlrnrprocess_image_partialhas_nested_structure nested_listrSprocessed_imagess r@_prepare_image_like_inputs1BaseImageProcessorFast._prepare_image_like_inputss8///V '   lr!  #6{QW:fQi$PU3W gmngmXc{ S{!6s!;{ Sgm n GMMfs 5c :f M !TnMsB 'B 7B B&B r6rhr;c <Uc0nUb[S 0[XS9D6nUb[S 0[USS9D6n[U[5(a [ U5n[U[5(a [ U5nUc[ R nXS'X'S'X7S'XGS'XWS'XgS'U$) z Update kwargs that need further processing before being validated Can be overridden by subclasses to customize the processing of kwargs. rr6rr8rhr1r2r;rp)rrrr{rrr?)rr8r6rhr1r2r;rs r@_further_process_kwargs.BaseImageProcessorFast._further_process_kwargs(s >F  \m[\D   T={#STI j$ ' 'z*J i & &i(I  *00Kv'{&7"#)|'{ +} rBr7r5r9rir:c  ,[UUUUUUUUU U U U S9 g)z0 validate the kwargs for the preprocess method. ) r.r/r0r1r2r7r8r5r6r9r:r;N)rA)rr.r/r0r1r2r7r8r5r6r9r:r;rs r@_validate_preprocess_kwargs2BaseImageProcessorFast._validate_preprocess_kwargsLs0& +!)%!))# rBc.UR"U/UQ70UD6$N) preprocess)rrPargsrs r@__call__BaseImageProcessorFast.__call__nsv7777rBc <[UR5URS9 URHnURU[ XS55 M! 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It can be used for segmentation maps, depth maps, etc. )rPrkrlrn)r _preprocess)rrPrkrlrnrrs r@r4BaseImageProcessorFast._preprocess_image_like_inputss<00L]1 8888rBroc [XS9unn0nUR5H"unnU(aURUX4S9nUUU'M$ [UU5n[UU S9unn0nUR5H8unnU(aUR UU5nUR UXxXU 5nUUU'M: [UU5nU (a[ R"USS9OUn[SU0U S9$)N)ro)rXr8rr)dimr)data tensor_type) ritemsrrrrrstackr )rrPr7r8rr5r6r.r/r0r1r2ror:rgrouped_imagesgrouped_images_indexresized_images_groupedrRstacked_imagesresized_imagesprocessed_images_groupedrs r@r"BaseImageProcessorFast._preprocesss$0EV/o,,!#%3%9%9%; !E>!%>!j,: "5 )&<((>@TU0E^fv/w,,#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N/= $U +&<**BDXYCQ5;;'7Q?Wg.2B!CQ_``rBcl>[TU]5nURSS5 URSS5 U$)N_valid_processor_keysr)rto_dictr)r encoder_dictrs r@rBaseImageProcessorFast.to_dicts7w( 0$7.5rB)rr6r8)NT)NNNNNN))NNN)NNNr) NNNNNNNNNNNN)Brqrrrsrtr9r1r2r8rhr6r7r5r.r/r0rkr:rr?r;rlrnmodel_input_namesrfrrrrrrur staticmethodrryrrrzrr rrrr{rrrwrxrrrrrrrrr rr rr!rrrrr| __classcell__)rs@r@rrsHJI DIINJNLNN"((K F'(2LMR89R R804 4[4[4[- 4[  4[ 4[l:> S/ 56   (  (--E8E?*+-5(5/) * -  -.r(,:>9=%)*.+/1tn1U5$u+#5671E%e"456 1 TN 1 ! 1(1 11     %e,- U +, 8EE38nE  E.%% % t   OOO  O(*.DH+/ $$!$$E#/?*?$@A $ ( $  $R*.DH+/ * * !* $E#/?*?$@A * ( *  *  n * \$((,,0:>9=26"x "H%"$D> " U5$u+#567 " E%e"456 "./" "L&**.'+;?:>$(#')-(,QU;?26  TN  !  tn  U5%,#678  E%u"567  D>  x   !  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