cCiNSrSSKrSSKrSSKJrJr SSKrSSKJ r J r J r SSK J r JrJr SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJ r J!r! \!RD"\#5r$\ "5(aSSK%r%"S S \ 5r&S /r'g) z!Image processor class for Gemma3.N)OptionalUnion)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc&0^\rSrSrSrSS/rSS\RSSSSSSSSSS4 S\S \ \ \ \ 4S \S \S \ \ \4S \S\ \ \\\4S\ \ \\\4S\ \S\ \S\ \ S\ \ S\ \SS4U4SjjjrS S\R$S\ S\ S\S\ \ \ \4S\ \ \ \44 SjjrS S\\R$S\S\ S\ S\S\ \ \ \4S\ \ \ \44Sjjr\"5SSSSSSSSS\R.SSSSSS4S\S\ \S \ \ \ \ 4S \ \S \ \S \ \S \ \S\ \ \\\4S\ \ \\\4S\ \ \ \4S\ \S\ \ \ \4S\ \S\ \S\ \ S\ \ S\ \S\R6R64$Sjj5rSrU=r$)!Gemma3ImageProcessor5a Constructs a SigLIP image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image by the specified mean and standard deviation. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. do_pan_and_scan (`bool`, *optional*): Whether to apply `pan_and_scan` to images. pan_and_scan_min_crop_size (`int`, *optional*): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*): Minimum aspect ratio to activate pan and scan. pixel_values num_cropsTNgp? do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbdo_pan_and_scanpan_and_scan_min_crop_sizepan_and_scan_max_num_crops"pan_and_scan_min_ratio_to_activatereturnc >[TU]"S0UD6 UbUOSSS.n[USS9nUbUO[nUbUO[nXlX lX0lX@lXPl X`l Xpl Xl Xl XlXlXlXlg)N)heightwidthT)default_to_square)super__init__rr r r!r"r#r$r%r&r'r(r)r*r+r,r-)selfr!r"r#r$r%r&r'r(r)r*r+r,r-kwargs __class__s l/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/gemma3/image_processing_gemma3.pyr6Gemma3ImageProcessor.__init___s" "6"'tc-JTT:#-#9Z?U !*!6Ir?r1r2 num_crops_w num_crops_h crop_size_w crop_size_hicrop_positions_wcrop_positions_hpos_hpos_w image_cropss r: pan_and_scan!Gemma3ImageProcessor.pan_and_scans:6'u-  ?~ BB djj#)=>?Kc$**U-O"PQS^_Ka-K8FKK ~ BB djj#)=>?Kc$**V-P"QRT_`Ka-K8FKK$))E$789 $))F$89:  { (+E EI5:;5GH5G!O5GH5:;5GH5G!O5GH  0 5 5 5%.$5$56F$Y$YLE5eek1155;;N3NNO$Y %.$5$56F$Y$YLE5a!44eek>Q6QQR$Y  IH sH5H2H.!H$imagesc /n/n UHFn URU UUUUUS9n URU /U -5 U R[U 55 MH X4$)N)r=r+r,r-r>r?)rWextendappendlen) r7rYr*r+r,r-r>r?pas_images_listr r= pas_imagess r: _process_images_for_pan_and_scan5Gemma3ImageProcessor._process_images_for_pan_and_scanss E**+E+E3U'"3 +J  " "E7Z#7 8   S_ -))r<return_tensorsc UbUO URnUbUO URn[USSS9nUbUO URnUbUO URnUbUO UR nUbUO UR nUbUO URnU bU O URn U bU O URn UbUO URnUbUO URnUbUO URnUbUO URnURU5n[U5n[!U5(d [#S5e[%UUUUU UUUS9 U (aUVs/sHn['U5PM nnUVs/sHn[)U5PM nnU(a([+US5(a[,R/S5 U c[1US5n U(aUR3UUUUUU U S9unnOUVs/sHnSPM nn/nUHknU(aUS US nn[5UUU4XLS 9nU(aUR7UXlS 9nU(aUR9UXU S 9n[;UXS9nUR=U5 Mm UUS.n[?UU S9$s snfs snfs snf)a; Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. do_pan_and_scan (`bool`, *optional*, defaults to `self.do_pan_and_scan`): Whether to apply `pan_and_scan` to images. pan_and_scan_min_crop_size (`int`, *optional*, defaults to `self.pan_and_scan_min_crop_size`): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*, defaults to `self.pan_and_scan_max_num_crops`): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*, defaults to `self.pan_and_scan_min_ratio_to_activate`): Minimum aspect ratio to activate pan and scan. r"F) param_namer3zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r$r%r&r'r(r!r"r#rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)rYr*r+r,r-r>r?r1r2)r=r"r#r?)r=scaler?)r=meanstdr?)input_channel_dim)rr )data tensor_type) r!r"rr#r$r%r&r'r(r)r*r+r,r- fetch_imagesrr ValueErrorrr rrlogger warning_oncerr`r rescale normalizer r\r)r7rYr!r"r#r$r%r&r'r(rbr>r?r)r*r+r,r-r=r _processed_imagesr1r2ris r: preprocessGemma3ImageProcessor.preprocesssV"+!6IDNN 'tTYYTfN'38#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN +9+E4K^K^-<-H/dNbNb*D*P &VZVuVu #+E*P &VZVuVu # 2= /88 + ""6*)&1F##:  &!)%!  9?@nU+F@6<vay I   $ E E /+E+E3U'"3!F! FI%++FqFI+E $XW vuo 5 ljSd'0{hE  # #E *!$!1yI>BBcA=0,s;I!I& I+) r)r&r*r$r!r'r(r,r+r-r#r%r")NN)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARboolrdictstrrCrfloatlistr6npndarrayrrWr`rFIRSTrrPILImagers__static_attributes__ __classcell__)r9s@r:rr5s%N(5)-'9'B'B,3!:>9=)-*.4848>B#U#UtCH~&#U% #U  #U c5j) #U#UU5$u+#567#UE%e"456#U!#U"$#U%-SM#U%-SM#U-5UO#U !#U#UV?CDHPzzP%(P%( P -2 P eC)9$9:; P$E#/?*?$@APr?CDH*RZZ **%( * %( * -2 *eC)9$9:;*$E#/?*?$@A*2%&%))-15%)*.'+:>9=;?2B2H2HDH)-*.4848>B%fCfCD>fCtCH~& fC -. fC TN fC!fCtnfCU5$u+#567fCE%e"456fC!sJ!78fC./fC$E#/?*?$@AfC!fC"$fC %-SM!fC"%-SM#fC$-5UO%fC& 'fC'fCr<r)(ryrKrDtypingrrnumpyrimage_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrrrrutilsrrrr get_loggerrurmrr__all__r4r<r:rs( "UU    _^   H %aC-aCH " "r<