cCibASrSSKJr SSKJrJr SSKrSSKJ r J r J r SSK J r JrJr SSKJrJrJrJrJrJrJrJrJrJrJr SS KJrJrJr SS K J!r! \RD"\#5r$\!"S S 9"S S\ 55r%S/r&g)z Image processor class for LeViT.)Iterable)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)get_resize_output_image_sizeresizeto_channel_dimension_format) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)requires)vision)backendsc!^\rSrSrSrS/rSS\RSSSSS\\ 4 S\ S\ \ \ \4S \S \ S \ \ \ \4S \ S \\\4S\ S\ \\\\4S\ \\\\4SS4U4Sjjjr\RSS4S\R(S\ \ \4S \S\ \\ \4S\ \\ \4S\R(4 Sjjr\"5SSSSSSSSSSS\R0S4 S\S\ \ S\ \ \ \4S \ \S \ \ S \ \ \ \4S \ \ S \ \S\ \ S\ \\\\4S\ \\\\4S\ \S\S\ \\ \4S\4Sjj5rSrU=r$)LevitImageProcessor0a Constructs a LeViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_edge"] * height / width, size["shortest_edge"])`. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether or not to center crop the input to `(crop_size["height"], crop_size["width"])`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired image size after `center_crop`. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Controls 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`): Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`list[int]`, *optional*, defaults to `[0.485, 0.456, 0.406]`): 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 (`list[int]`, *optional*, defaults to `[0.229, 0.224, 0.225]`): 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_size do_rescalerescale_factor do_normalize image_mean image_stdreturnc  >[T U]"S0U D6 UbUOSS0n[USS9nUbUOSSS.n[USS9nXlX lX0lX@lXPlX`lXpl Xl U bU O[Ul U bXlg[Ulg) N shortest_edgeFdefault_to_squareheightwidthr& param_name)super__init__r r"r#r$r%r&r'r(r)r r*rr+) selfr"r#r$r%r&r'r(r)r*r+kwargs __class__s j/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/levit/image_processing_levit.pyr9LevitImageProcessor.__init__[s "6"'tos-CTU;!*!6IsUX*DY&/&;AUimage data_formatinput_data_formatc [USS9nSU;a'[SUS-5n[XSUS9n U SU SS.nS U;dS U;a[S UR 535e[ U4US US 4UUUS .UD6$) a Resize an image. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_edge"] * height / width, size["shortest_edge"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Fr0r.g$I$I?)r#r1rBrr2r3r4zFSize dict must have keys 'height' and 'width' or 'shortest_edge'. Got )r#r$rArB)r intr ValueErrorkeysr ) r:r@r#r$rArBr; size_dictr. output_sizes r=r LevitImageProcessor.resizezsD"$%@ d "d?.C CDM6UVgK$/q>KNKI 9 $y(@XYbYgYgYiXjk   H%y'9:#/     r?imagesreturn_tensorsc `UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnU bU O UR n U bU O UR n U bU O URn UbUO URn[USS9nUbUO URn[USS9n[U5n[U5(d [S5e[UUU U U UUUUUS9 UVs/sHn[U5PM nnU(a([!US5(a["R%S5 Uc['US5nU(aUVs/sHoR)XXNS 9PM nnU(aUVs/sHoR+XUS 9PM nnU(aUVs/sHoR-XUS 9PM nnU (aUVs/sHoR/XXS 9PM nnUVs/sH n[1XUS 9PM nnS U0n[3UU S 9$s snfs snfs snfs snfs snfs snf) a Preprocess an image or batch of images to be used as input to a LeViT model. Args: images (`ImageInput`): Image or batch of images 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 output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. 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 output image after center cropping. Crops images to (crop_size["height"], crop_size["width"]). do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Factor to rescale the image pixel values by. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image pixel values by `image_mean` and `image_std`. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Mean to normalize the image pixel values by. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Standard deviation to normalize the image pixel values by. 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 (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`): 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. 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. Fr0r&r5zkInvalid 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$rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)rB)input_channel_dimr!)data tensor_type)r"r$r%r'r(r)r*r+r#r r&rrrFrrrlogger warning_oncerr center_croprescale normalizer r)r:rKr"r#r$r%r&r'r(r)r*r+rLrArBr@rOs r= preprocessLevitImageProcessor.preprocesssnH"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYTU;!*!6IDNN !) D )&1F##:  &!)%!) 6<vay I  kqrkqbgkk%xk]kqFr kqrkqbg&&uK\&]kqFr lrslrchll5L]l^lrFs ououfku)aou  ou ntej 'N_ `nt  '>BB?=sst s$;HHH)H!H&.H+) r&r%r)r'r"r*r+r$r(r#)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBICUBICr rboolrdictstrrErfloatrr9npndarrayrr rFIRSTrrrrV__static_attributes__ __classcell__)r<s@r=rr0s%N(()-'9'A'A#.2,3!>S=QVVtCH~&V% V  V DcN+ VVc5j)VVU5(5/#9:;VE%%"89:V VVF(:'A'A>BDH 5 zz5 38n5 % 5 eC)9$9:; 5 $E#/?*?$@A 5  5 n%&%))-15)-.2%)*.'+>B=A/3(8(>(>DHCCCCD>CCtCH~& CC -. CC ! CCDcN+CCTNCC!CCtnCCU5(5/#9:;CCE%%"89:CC!,CC&CC$E#/?*?$@ACC !CC'CCr?r)'r\collections.abcrtypingrrnumpyrcimage_processing_utilsrrr image_transformsr r r image_utilsr rrrrrrrrrrutilsrrrutils.import_utilsr get_loggerrXrQr__all__r7r?r=rrs'$"UU    JI*   H % ;DC,DC DCN ! !r?