cCiHSrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r SSKJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJr \"5(aSSKr\R@"\!5r""S S \5r#S /r$g) z'Image processor class for EfficientNet.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)rescaleresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc#N^\rSrSrSrS/rSS\RRSSSSSSSSS4 S\ S \ \ \ \ 4S \S \ S \ \ \ \ 4S \\ \4S\ S\ S\ S\ \\\\4S\ \\\\4S\ SS4U4Sjjjr\RSS4S\R(S \ \ \ 4S \S\ \\ \4S\ \\ \4S\R(4 SjjrS!S\R(S\\ \4S\ S\ \\ \4S\ \\ \44 Sjjr\"5SSSSSSSSSSSSS\R2S4S\S\ \ S \ \ \ \ 4S \ \ S \ \ \ \ 4S\ \ S \ \S\ \ S\ \ S\ \\\\4S\ \\\\4S\ \ S\ \\ \4S\S\ \\ \4S\RR4 Sjj5rS rU=r$)"EfficientNetImageProcessor.a Constructs a EfficientNet 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": 346, "width": 346}`): Size of the image after `resize`. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling` filter, *optional*, defaults to 0): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_center_crop (`bool`, *optional*, defaults to `False`): 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": 289, "width": 289}`): 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. rescale_offset (`bool`, *optional*, defaults to `False`): Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. 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. include_top (`bool`, *optional*, defaults to `True`): Whether to rescale the image again. Should be set to True if the inputs are used for image classification. pixel_valuesTNFgp? do_resizesizeresampledo_center_crop crop_sizerescale_factorrescale_offset do_rescale do_normalize image_mean image_std include_topreturnc  0>[TU]"S0U D6 UbUOSSS.n[U5nUbUOSSS.n[USS9nXlX lX0lX@lXPlXlX`l Xpl Xl U bU O[Ul U bU O[UlXlg)NiZ)heightwidthi!r# param_name)super__init__rrr r!r"r#r&r$r%r'r r(r r)r*)selfrr r!r"r#r$r%r&r'r(r)r*kwargs __class__s x/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/efficientnet/image_processing_efficientnet.pyr3#EfficientNetImageProcessor.__init__Ws "6"'tc-JT"!*!6IsUX*DZ&/&;AV&image data_formatinput_data_formatc [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)a Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.NEAREST`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.NEAREST`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Returns: `np.ndarray`: The resized image. r-r.zFThe `size` dictionary must contain the keys `height` and `width`. Got )r r!r;r<)r ValueErrorkeysr )r4r:r r!r;r<r5 output_sizes r7r !EfficientNetImageProcessor.resize{syFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r9scaleoffsetc <[U4X$US.UD6nU(aUS- nU$)a~ Rescale an image by a scale factor. If `offset` is `True`, the image has its values rescaled by `scale` and then offset by 1. If `scale` is 1/127.5, the image is rescaled between [-1, 1]. image = image * scale - 1 If `offset` is `False`, and `scale` is 1/255, the image is rescaled between [0, 1]. image = image * scale Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. offset (`bool`, *optional*): Whether to scale the image in both negative and positive directions. 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. )rBr;r<)r )r4r:rBrCr;r<r5rescaled_images r7r "EfficientNetImageProcessor.rescales9>!  K\ `f  +a/Nr9imagesreturn_tensorsc UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnU bU O UR n U bU O UR n U bU O URn U bU O URn 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[+US5nU(a!UVs/sHnUR-UX4US9PM nnU(a!UVs/sHnUR/UUUS9PM nnU(a!UVs/sHnUR1UXUS 9PM nnU (a!UVs/sHnUR3UXUS 9PM nnU (a"UVs/sHnUR3USU US 9PM nnUVs/sHn[5UUUS 9PM nnS U0n[7UUS 9$s snfs 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`): 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`. rescale_offset (`bool`, *optional*, defaults to `self.rescale_offset`): Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. 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. include_top (`bool`, *optional*, defaults to `self.include_top`): Rescales the image again for image classification if set to True. 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#r/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#rr 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:rBrCr<)r:meanstdr<)input_channel_dimr)data tensor_type)rr!r"r&r$r%r'r(r)r*r rr#rrr>rrrlogger warning_oncerr center_cropr normalizer r)r4rHrr r!r"r#r&r$r%r'r(r)r*rIr;r<r:rNs r7 preprocess%EfficientNetImageProcessor.preprocesssN"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^+9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN %0%vay I  $#E %dYj k#  pvpvgl  u9Pa bpv   $ $E ~`q$  $#EU^op#  $#EU Ufg#  ou ntej '{N_ `nt  '>BBa=     s*I'I,(I1I68I; JJ) r#r"r'r&rr(r)r*r!r$r%r )TNN)__name__ __module__ __qualname____firstlineno____doc__model_input_namesPILImageNEARESTboolrdictstrintrrfloatlistr3npndarrayrr r rFIRSTrrrT__static_attributes__ __classcell__)r6s@r7rr.s$L(()-'*yy'8'8$.2,3$!:>9= !'!'tCH~&!'% !'  !' DcN+ !'c5j)!'!'!'!'U5$u+#567!'E%e"456!'!' !'!'P(:'A'A>BDH . zz. 38n. % . eC)9$9:; . $E#/?*?$@A .  . h>BDH &zz&S%Z & & eC)9$9:; & $E#/?*?$@A &P%&%))-)-.2%)*.)-'+:>9=&*;?(8(>(>DH#ZCZCD>ZCtCH~& ZC ! ZCDcN+ZCTNZC!ZC!ZCtnZCU5$u+#567ZCE%e"456ZCd^ZC!sJ!78ZC &!ZC"$E#/?*?$@A#ZC$ %ZC'ZCr9r)%rZtypingrrnumpyreimage_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrrrutilsrrrrr\ get_loggerrVrPr__all__r1r9r7rrsu."UULL    _^   H %@C!3@CF ( (r9