cCi:6SrSSKJrJr SSKrSSKJrJrJ r SSK J r J r SSK JrJrJrJrJrJrJrJrJrJrJr SSKJrJrJr \R:"\5r"S S \5r S /r!g) zImage processor class for Pvt.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)resizeto_channel_dimension_format) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsloggingcZ^\rSrSrSrS/rSS\RSSSSS4S\S\ \ \ \ 4S \S \S \ \ \4S \S \ \ \\\4S\ \ \\\4SS4U4Sjjjr\RSS4S\R$S\ \ \ 4S \S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\"5SSSSSSSSS\R,S4 S\S\ \S\ \ \ \ 4S \ \S \ \S \ \S \ \S \ \ \\\4S\ \ \\\4S\ \ \ \4S\ \ \4S\ \ \ \44Sjj5rSrU=r$)PvtImageProcessor*a Constructs a PVT 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_DEFAULT_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_DEFAULT_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. pixel_valuesTNgp? do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stdreturnc  >[T U]"S0U D6 UbUOSSS.n[U5nXlX@lX`lX lX0lXPlUbUO[Ul UbXl g[Ul g)N)heightwidth) super__init__rrr r"rrr!r r#r r$) selfrrrr r!r"r#r$kwargs __class__s f/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/pvt/image_processing_pvt.pyr,PvtImageProcessor.__init__Ksp "6"'tc-JT""$(  ,(2(>*DY&/&;AUimage data_formatinput_data_formatc [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)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 )rrr4r5)r ValueErrorkeysr )r-r3rrr4r5r. output_sizes r0r PvtImageProcessor.resizedsyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r2imagesreturn_tensorsc UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnUbUO UR nU bU O UR n UbUO URn[U5n [U5n[U5(d [S5e[UUUUU UUUS9 UVs/sHn[U5PM nnU(a([US5(a[R!S5 U c[#US5n U(a UVs/sHnUR%XXLS9PM nnU(a UVs/sHnUR'XU S9PM nnU(a UVs/sHnUR)XXS9PM nnUVs/sH n[+XU S9PM nnS U0n[-XS 9$s 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. zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r r!r"r#r$rrrrzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r3rrr5)r3scaler5)r3meanstdr5)input_channel_dimr)data tensor_type)rr r"rr!r#r$rrrrr7rrrlogger warning_oncerr rescale normalizer r)r-r;rrrr r!r"r#r$r<r4r5 size_dictr3rBs r0 preprocessPvtImageProcessor.preprocesss*x"+!6IDNN #-#9Zt '3'?|TEVEV '38+9+E4K^K^#-#9Zt !*!6IDNN 'tTYY!$' )&1F##:  &!)%! 6<vay I  $#E %( p#  $#E 5Rc d#  $#EUp#  ou ntej 'N_ `nt  'BBG=    sG0GG >GG)r"r rr#r$rr!r)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARboolrdictstrintrfloatlistr,npndarrayr r rFIRSTrrrI__static_attributes__ __classcell__)r/s@r0rr*s<(()-'9'B'B,3!:>9=VVtCH~&V% V  V c5j) VVU5$u+#567VE%e"456V VV:(:'B'B>BDH . zz. 38n. % . eC)9$9:; . $E#/?*?$@A .  . `%&%))-15%)*.'+:>9=;?4D4J4JDH|C|CD>|CtCH~& |C -. |C TN |C!|Ctn|CU5$u+#567|CE%e"456|C!sJ!78|C3 001|C$E#/?*?$@A|C'|Cr2r)"rOtypingrrnumpyrXimage_processing_utilsrrrimage_transformsr r image_utilsr r r rrrrrrrrutilsrrr get_loggerrKrDr__all__r*r2r0resd%"UUC    JI   H %gC*gCT  r2