bCi>SrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r Jr SSKJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJr SS K J!r! \"5(aSSK"r"\RF"\$5r%\!"S S 9"S S \55r&S /r'g)z#Image processor class for ConvNeXT.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict) center_cropget_resize_output_image_sizeresizeto_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/rSSS\RSSSSS4 S\S\ \ \ \ 4S \ \ S \S \S \\ \ 4S \S\ \\ \\ 4S\ \\ \\ 4SS4U4Sjjjr\R"SS4S\R&S\ \ \ 4S \ S \S\ \\ \4S\ \\ \4S\R&4Sjjr\"5SSSSSSSSSS\R.S4 S\S\ \S\ \ \ \ 4S \ \ S \ \S \ \S \ \ S \ \S\ \\ \\ 4S\ \\ \\ 4S\ \\ \4S\S\ \\ \4S\R6R64Sjj5rSrU=r$)ConvNextImageProcessor4a Constructs a ConvNeXT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`): Resolution of the output image after `resize` is applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. Can be overridden by `size` in the `preprocess` method. crop_pct (`float` *optional*, defaults to 224 / 256): Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be overridden by `crop_pct` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` 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 `do_rescale` 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 `rescale_factor` 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_resizesizecrop_pctresample do_rescalerescale_factor do_normalize image_mean image_stdreturnc  >[T U]"S0U D6 UbUOSS0n[USS9nXlX lUbUOSUlX@lXPlX`lXpl UbUO[Ul U bXl g[Ul g)N shortest_edgeFdefault_to_squareg?)super__init__rr#r$r%r&r'r(r)r r*rr+) selfr#r$r%r&r'r(r)r*r+kwargs __class__s p/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/convnext/image_processing_convnext.pyr4ConvNextImageProcessor.__init__[s "6"'tos-CTU;" $,$8i  $,((2(>*DZ&/&;AVimage data_formatinput_data_formatc [USS9nSU;a[SUR535eUSnUS:a:[X- 5n [ XSUS9n [ S UU UUUS.UD6n[ S UX4UUS.UD6$[ U4X4UUUS .UD6$) ab Resize an image. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary of the form `{"shortest_edge": int}`, specifying the size of the output image. If `size["shortest_edge"]` >= 384 image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"] / crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. crop_pct (`float`): Percentage of the image to crop. Only has an effect if size < 384. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. Fr0r.z6Size dictionary must contain 'shortest_edge' key. Got r/)r$r1r=)r;r$r&r<r=)r;r$r<r=)r$r&r<r=r2)r ValueErrorkeysintr r r ) r5r;r$r%r&r<r=r6r.resize_shortest_edge resize_sizes r8r ConvNextImageProcessor.resizews>TU; $ &UVZV_V_VaUbcd d_- 3 #&}'?#@ 6E]nK !'"3   E#3'"3    #3!'"3    r:imagesreturn_tensorsc UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnUbUO UR nU bU O UR n U bU O URn UbUO URn[USS9n[U5n[U5(d [S5e[UUUU U UUUS9 UVs/sHn[U5PM nnU(a([US5(a[ R#S5 U c[%US5n U(a!UVs/sHnUR'XXEU S9PM nnU(a UVs/sHnUR)XU S9PM nnU(a UVs/sHnUR+XXS 9PM nnUVs/sH n[-XU S 9PM 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`): Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. crop_pct (`float`, *optional*, defaults to `self.crop_pct`): Percentage of the image to crop if size < 384. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. 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. 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: - 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. Fr0zkInvalid 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.)r;r$r%r&r=)r;scaler=)r;meanstdr=)input_channel_dimr")data tensor_type)r#r%r&r'r(r)r*r+r$rrrr?rrrlogger warning_oncerr rescale normalizer r)r5rEr#r$r%r&r'r(r)r*r+rFr<r=r;rLs r8 preprocess!ConvNextImageProcessor.preprocesssIB"+!6IDNN '38'38#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYTU;)&1F##:  &!)%! 6<vay I   $ $E Xdu$  $#E 5Rc d#  $#EUp#  ou ntej 'N_ `nt  'BBK=   sGG(GG!/G&) r%r)r'r#r*r+r&r(r$)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARboolrdictstrrAfloatrlistr4BICUBICnpndarrayrr rFIRSTrrPILImagerR__static_attributes__ __classcell__)r7s@r8r r 4s!F(()-$('9'B'B,3!:>9=WWtCH~&W5/ W % W  Wc5j)WWU5$u+#567WE%e"456W WWB(:'A'A>BDHCzzC38nC C % C eC)9$9:; C$E#/?*?$@AC CJ%&%))-$(15%)*.'+:>9=;?(8(>(>DHECECD>ECtCH~& EC 5/ EC -. ECTNEC!ECtnECU5$u+#567ECE%e"456EC!sJ!78EC&EC$E#/?*?$@AEC EC'ECr:r )(rXtypingrrnumpyraimage_processing_utilsrrrimage_transformsr r r r image_utilsr rrrrrrrrrrutilsrrrrutils.import_utilsrrd get_loggerrTrNr __all__r2r:r8rqs*"UU    _^*   H % ;MC/MC MC` $ $r: