cCi:SrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r SSKJrJrJrJrJrJrJrJrJr SSKJrJrJrJr SS KJr \"5(aSSKr\R@"\!5r"S r#S r$\"S S 9"SS\55r%S/r&g)z#Image processor class for ImageGPT.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)rescaleresizeto_channel_dimension_format) ChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availablelogging)requiresc URn[R"[R"U5SS9n[R"[R"U5SS9n[R"X5nUSS2S4SU-- USSS24-nU$)Naxisr)Tnpsumsquarematmul)aba2b2abds p/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/imagegpt/image_processing_imagegpt.pysquared_euclidean_distancer+-sp A  ! 1 %B  ! 1 %B 1B 1d7 a"fr$'{*A HcfURSS5n[X5n[R"USS9$)Nrrr)reshaper+r argmin)xclustersr)s r*color_quantizer36s- "aA"1/A 99QQ r,)vision)backendsc^\rSrSrSrS/rSSS\RSS4S\\ \ \ \ \ R4S\S\\\\ 4S \S \S \S S4U4S jjjr\RSS4S\ RS\\\ 4S \S\\ \\4S\\ \\4S \ R4 SjjrSS\ RS\\ \\4S\\ \\4S \ R4Sjjr\"5SSSSSSS\R,S4 S\S\\S\\\\ 4S \\S \\S \\S\\ \ \ \ \ R4S\\ \\4S\\ \\4S\\ \\4S \R4R44Sjj5rU4SjrSrU=r$)ImageGPTImageProcessor<a^ Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values" (color clusters). Args: clusters (`np.ndarray` or `list[list[int]]`, *optional*): The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overridden by `clusters` in `preprocess`. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by `do_resize` in `preprocess`. size (`dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): Size of the image after resizing. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in `preprocess`. do_color_quantize (`bool`, *optional*, defaults to `True`): Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`. pixel_valuesNTr2 do_resizesizeresample do_normalizedo_color_quantizereturnc >[TU]"S0UD6 UbUOSSS.n[U5nUb[R"U5OSUlX lX0lX@lXPl X`l g)N)heightwidth) super__init__rr arrayr2r:r;r<r=r>) selfr2r:r;r<r=r>kwargs __class__s r*rFImageGPTImageProcessor.__init__Wsb "6"'tc-JT".6.B* "  (!2r,image data_formatinput_data_formatc [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)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.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. rBrCzFThe `size` dictionary must contain the keys `height` and `width`. Got )r;r<rMrN)r ValueErrorkeysr )rHrLr;r<rMrNrI output_sizes r*r ImageGPTImageProcessor.resizemsyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r,c&[USX#S9nUS- nU$)a Normalizes an images' pixel values to between [-1, 1]. Args: image (`np.ndarray`): Image to normalize. 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. g?)rLscalerMrNr)r )rHrLrMrNs r* normalize ImageGPTImageProcessor.normalizes "e9+s  r,imagesreturn_tensorsc UbUO URnUbUO URn[U5nUbUO URnUbUO URnUbUO UR nUbUO UR n[R"U5n[U5n[U5(d [S5e[UUUS9 U(aUc [S5eUV s/sHn [U 5PM nn U(a([US5(a[R!S5 U c[#US5n U(a UV s/sHn UR%XXJS9PM nn U(aUV s/sHoR'XS9PM nn U(aUV s/sHn [)U [*R,U 5PM nn [R"U5n[/X5R1UR2SS 5nUR2Sn UR1U S 5n[5U5nS U0n O UV s/sHn [)XU 5PM nn S U0n [7XS 9$s sn fs sn fs sn fs sn fs sn f) 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_normalize=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 resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`): Whether to color quantize the image. clusters (`np.ndarray` or `list[list[int]]`, *optional*, defaults to `self.clusters`): Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if `do_color_quantize` 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: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. Only has an effect if `do_color_quantize` is set to `False`. 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. NzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r:r;r<z8Clusters must be specified if do_color_quantize is True.rzIt looks like you are trying to rescale already rescaled images. If you wish to do this, make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].)rLr;r<rN)rLrNr. input_idsr9)data tensor_type)r:r;rr<r=r>r2r rGrrrPrrrlogger warning_oncerr rVr r LASTr3r/shapelistr)rHrXr:r;r<r=r>r2rYrMrNrL batch_sizer\s r* preprocess!ImageGPTImageProcessor.preprocesssbl"+!6IDNN 'tTYYT"'38'3'?|TEVEV 1B1N-TXTjTj'3888H%$V,F##:  & !1WX X6<vay I  $#E %X k#  djkdj[`nn5nVdjFk pvwpvgl1%9I9N9NPabpvFwXXf%F#F5==fll3B>OPF aJ^^J3F&\F(Dflmfl]b1%FWXflFm"F+DBBK= lxnsH>4II%I Ic>[TU]5nURS5b8[US[R 5(aUSR 5US'/SQnUHnX1;dM SX'M U$)Nr2) image_mean image_stdrescale_factor do_rescale)rEto_dictget isinstancer ndarraytolist)rHoutput missing_keyskeyrJs r*rkImageGPTImageProcessor.to_dict,sp" ::j ! -*VJ=OQSQ[Q[2\2\!' !3!:!:!r=r:r<r;)NN)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARrrrbintr rnbooldictstrrFr r rVrFIRSTr rPILImagerdrk__static_attributes__ __classcell__)rJs@r*r7r7<s.(( BF)-'9'B'B!"&35d3i"**!<=>3 3 tCH~& 3 % 33 3 334(:'B'B>BDH . zz. 38n. % . eC)9$9:; . $E#/?*?$@A .  . f?CDH zzeC)9$9:;$E#/?*?$@A   *%&%))-15'+,0AE;?>N>T>TDHwCwCD>wCtCH~& wC -. wC tn wC$D>wC5d3i"**!<=>wC!sJ!78wCeC)9$9:;wC$E#/?*?$@AwC wC'wCr  r,r7)'rxtypingrrnumpyr image_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrutilsrrrrutils.import_utilsrr get_loggerrtr^r+r3r7__all__rDr,r*rs*"UULL   _^*   H %   ;z/z zz $ $r,