cCiq8SrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r SSKJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJr SS KJr \R@"\!5r"\"S S 9"S S \55r#S /r$g)zImage processor class for ViT.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)requires)vision)backendscr^\rSrSrSrS/rSS\RSSSSSS4 S\S\ \ \ \ 4S \S \S \ \ \4S \S \ \ \\\4S\ \ \\\4S\ \SS4U4Sjjjr\RSS4S\R$S\ \ \ 4S \S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\"5SSSSSSSSS\R,SS4 S\S\ \S\ \ \ \ 4S \ \S \ \S \ \S \ \S \ \ \\\4S\ \ \\\4S\ \ \ \4S\ \ \4S\ \ \ \4S\ \4Sjj5rSrU=r$)ViTImageProcessor+a Constructs a ViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter 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 the `do_rescale` parameter 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 the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): 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 `IMAGENET_STANDARD_STD`): 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. do_convert_rgb (`bool`, *optional*): Whether to convert the image to RGB. pixel_valuesTNgp? do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbreturnc  >[T U]"S0U D6 UbUOSSS.n[U5nXlX@lX`lX lX0lXPlUbUO[Ul UbUO[Ul Xl g)N)heightwidth)super__init__rr!r$r&r"r#r%r r'r r(r)) selfr!r"r#r$r%r&r'r(r)kwargs __class__s f/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/vit/image_processing_vit.pyr1ViTImageProcessor.__init__Ost "6"'tc-JT""$(  ,(2(>*DZ&/&;AV,image data_formatinput_data_formatc [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)a Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. 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. 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. Returns: `np.ndarray`: The resized image. r-r.zFThe `size` dictionary must contain the keys `height` and `width`. Got )r"r#r9r:)r ValueErrorkeysr )r2r8r"r#r9r:r3 output_sizes r5r ViTImageProcessor.resizeisyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r7imagesreturn_tensorsc &UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnUbUO UR nU bU O UR n U bU O URn UbUO URn[U5n[U5n[U5(d [S5e[UUUUU UUUS9 U (aUVs/sHn[U5PM nnUVs/sHn[U5PM nnU(a([!US5(a["R%S5 U c['US5n U(a UVs/sHnUR)XXLS9PM nnU(a UVs/sHnUR+XU S9PM nnU(a UVs/sHnUR-XXS9PM nnUVs/sH n[/XU S9PM nnS U0n[1UU S 9$s snfs snfs snfs 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`): Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after resizing. resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`): `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. 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 values between [0 - 1]. 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 if `do_normalize` is set to `True`. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use 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. zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r$r%r&r'r(r!r"r#rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r8r"r#r:)r8scaler:)r8meanstdr:)input_channel_dimr )data tensor_type)r!r$r&r#r%r'r(r)r"rrrr<rr rrlogger warning_oncerr rescale normalizer r)r2r@r!r"r#r$r%r&r'r(rAr9r:r) size_dictr8rGs r5 preprocessViTImageProcessor.preprocesssb~"+!6IDNN #-#9Zt '3'?|TEVEV '38+9+E4K^K^#-#9Zt !*!6IDNN +9+E4K^K^'tTYY!$' )&1F##:  &!)%!  9?@nU+F@6<vay I  $#E %( p#  $#E 5Rc d#  $#EUp#  ou ntej 'N_ `nt  '>BBMA=    s$&G5G:#G? H1H H) r)r&r$r!r'r(r#r%r")__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARboolrdictstrintrfloatlistr1npndarrayrr rFIRSTrrrN__static_attributes__ __classcell__)r4s@r5rr+s@(()-'9'B'B,3!:>9=)---tCH~&-% -  - c5j) --U5$u+#567-E%e"456-!- --<(:'B'B>BDH . zz. 38n. % . eC)9$9:; . $E#/?*?$@A .  . `%&%))-15%)*.'+:>9=;?4D4J4JDH)-CCCCD>CCtCH~& CC -. CC TN CC!CCtnCCU5$u+#567CCE%e"456CC!sJ!78CC3 001CC$E#/?*?$@ACC!CC'CCr7r)%rTtypingrrnumpyr]image_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrrrutilsrrrutils.import_utilsr get_loggerrPrIr__all__r/r7r5rks%"UUSS    JI*   H % ;qC*qC qCh  r7