bCi;SrSSKJrJr SSKrSSKJrJrJ r SSK J r J r SSK JrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJr SS KJr \"5(aSSK r \RB"\"5r#\"S S 9"S S \55r$S /r%g)zImage processor class for DeiT.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)resizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availablelogging)requires)vision)backendsc^\rSrSrSrS/rSS\RRSSSSSSS4 S\ S\ \ \ \ 4S \S \ S \ \ \ \ 4S \\ \4S \ S\ S\ \\\\4S\ \\\\4SS4U4Sjjjr\RSS4S\R(S\ \ \ 4S \S\ \\ \4S\ \\ \4S\R(4 Sjjr\"5SSSSSSSSSSS\R0S4 S\S\ \ S\ \ \ \ 4S \ \ S \ \ \ \ 4S \ \ S \ \S\ \ S\ \\\\4S\ \\\\4S\ \\ \4S\S\ \\ \4S\RR4Sjj5rSrU=r$)DeiTImageProcessor/a# Constructs a DeiT 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 `preprocess`. size (`dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): Size of the image after `resize`. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling` filter, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`. crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`. 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_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. 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. pixel_valuesTNgp? do_resizesizeresampledo_center_crop crop_sizerescale_factor do_rescale do_normalize image_mean image_stdreturnc  ">[T U]"S0U D6 UbUOSSS.n[U5nUbUOSSS.n[USS9nXlX lX0lX@lXPlXplX`l Xl U bU O[Ul U bXlg[Ulg)N)heightwidthr% param_name)super__init__rr!r"r#r$r%r'r&r(r r)r r*) selfr!r"r#r$r%r&r'r(r)r*kwargs __class__s h/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/deit/image_processing_deit.pyr5DeiTImageProcessor.__init__Ts "6"'tc-JT"!*!6IsUX*DZ&/&;AVimage data_formatinput_data_formatc [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)a Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Returns: `np.ndarray`: The resized image. r.r/zFThe `size` dictionary must contain the keys `height` and `width`. Got )r"r#r=r>)r ValueErrorkeysr )r6r<r"r#r=r>r7 output_sizes r9r DeiTImageProcessor.resizetsyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r;imagesreturn_tensorsc UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnU bU O UR n U bU O UR n U bU O URn UbUO URn[U5nUbUO URn[USS9n[U5n[U5(d [S5e[UUU U U UUUUUS9 UVs/sHn[U5PM nnU(a([!US5(a["R%S5 Uc['US5n/nUHpnU(aUR)XXNS9nU(aUR+XUS9nU(aUR-XUS 9nU (aUR/XXS 9nUR1U5 Mr UVs/sH n[3XUS 9PM nnS U0n[5UU S 9$s 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 `resize`. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): PILImageResampling filter to use if resizing the image Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the image after center crop. If one edge the image is smaller than `crop_size`, it will be padded with zeros and then cropped 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. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - `None`: 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: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) 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. r%r1zkInvalid 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!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.)r<r"r#r>)r<r"r>)r<scaler>)r<meanstdr>)input_channel_dimr )data tensor_type)r!r#r$r'r&r(r)r*r"rr%rrr@rrrlogger warning_oncerr center_croprescale normalizeappendr r)r6rDr!r"r#r$r%r'r&r(r)r*rEr=r>r< all_imagesrKs r9 preprocessDeiTImageProcessor.preprocesss3B"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYT"!*!6IDNN !) D )&1F##:  &!)%!) 6<vay I  E %X s((uXi(j 5Zk li'   e $$$ # (N_ `#  '>BBG=: s <G3G8) r%r$r(r'r!r)r*r#r&r")__name__ __module__ __qualname____firstlineno____doc__model_input_namesPILImageBICUBICboolrdictstrintrrfloatlistr5npndarrayr r rFIRSTrrrT__static_attributes__ __classcell__)r8s@r9rr/sB(()-'*yy'8'8#.2,3!:>9=WWtCH~&W% W  W DcN+ Wc5j)WWWU5$u+#567WE%e"456W WWH(:'A'A>BDH . zz. 38n. % . eC)9$9:; . $E#/?*?$@A .  . `%&%))-)-.2%)*.'+:>9=;?(8(>(>DHECECD>ECtCH~& EC ! ECDcN+ECTNEC!ECtnECU5$u+#567ECE%e"456EC!sJ!78EC&EC$E#/?*?$@AEC !EC'ECr;r)&rZtypingrrnumpyreimage_processing_utilsrrrimage_transformsr r image_utilsr r r rrrrrrrrutilsrrrrutils.import_utilsrr\ get_loggerrVrMr__all__r3r;r9rss&"UUC    _^*   H % ;zC+zC zCz r;