bCiA^:SrSSKJrJr SSKrSSKJrJrJ r J r SSK J r J r SSKJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJrJrJ r SS K!J"r" \"5(aSSK#r#\"5(aSSK$r$\ RJ"\&5r'\""S S 9"S S \55r(S /r)g)zImage processor class for Beit.)OptionalUnionN)INIT_SERVICE_KWARGSBaseImageProcessor 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_torch_availableis_torch_tensoris_vision_availablelogging)requires)vision)backendsc%^\rSrSrSrS/r\"\S9SS\RSSSSSSSS4 S \ S \ \ \ \4S \S \ S \ \ \ \4S\\\4S\ S\ S\ \\\\4S\ \\\\4S\ SS4U4Sjjj5r\RSS4S\R(S \ \ \4S \S\ \\ \4S\ \\ \4S\R(4 SjjrS\S\R(4SjrS(S\S\ \ S \ \ S \ \ \ \4S \ \S \ \ S \ \ \ \4S\ \ S\ \S\ \ S\ \\\\4S\ \\\\4S\ \\ \44SjjrS(S\S \ \ S \ \ \ \4S \ \S \ \ S \ \ \ \4S\ \ S\ \S\ \ S\ \\\\4S\ \\\\4S\ \\ \4S\ \\ \4S\R(4SjjrS)S\S \ \ S \ \ \ \4S \ \S \ \ S \ \ \ \4S\ \ S\ \\ \44SjjrS*U4S jjr\"5SSSSSSSSSSSSS\R:S4S!\S"\ \S \ \ S \ \ \ \4S \ \S \ \ S \ \ \ \4S\ \ S\ \S\ \ S\ \\\\4S\ \\\\4S\ \ S#\ \\ \4S\S\ \\ \4S\R@R@4"S$jj5r!S*S%\ \\"4S&jjr#S'r$U=r%$)+BeitImageProcessor9a Constructs a BEiT 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 the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): Size of the output image after resizing. 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 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 the `do_center_crop` parameter in the `preprocess` method. crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`. Can be overridden by the `crop_size` 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_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`): The mean to use if normalizing the image. This is a float or list of floats of length of the number of channels of 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`): The standard deviation to use if normalizing the image. This is a float or list of floats of length of the number of channels of the image. Can be overridden by the `image_std` parameter in the `preprocess` method. do_reduce_labels (`bool`, *optional*, defaults to `False`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the `preprocess` method. pixel_values)extraTNgp?F do_resizesizeresampledo_center_crop crop_sizerescale_factor do_rescale do_normalize image_mean image_stddo_reduce_labelsreturnc  $>[T U]"S0U D6 UbUOSSS.n[U5nUbUOSSS.n[USS9nXlX lX0lX@lXPlXplX`l Xl U bU O[Ul U bU O[UlXlg)N)heightwidthr)) param_name)super__init__r r%r&r'r(r)r+r*r,r r-r r.r/)selfr%r&r'r(r)r*r+r,r-r.r/kwargs __class__s h/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/beit/image_processing_beit.pyr9BeitImageProcessor.__init__gs "6"'tc-JT"!*!6IsUX*DZ&/&;AV 0image data_formatinput_data_formatc [USSS9nSU;dSU;a[SUR535e[U4USUS4UUUS.UD6$)a Resize an image to (size["height"], size["width"]). Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PIL.Image.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 (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Tr&default_to_squarer6r3r4z@The `size` argument must contain `height` and `width` keys. Got )r&r'rArB)r ValueErrorkeysr )r:r@r&r'rArBr;s r=r BeitImageProcessor.resizesw0TTfM 4 7$#6_`d`i`i`k_lmn n  x.$w-0#/     r?labelcB[U5nSXS:H'US- nSXS:H'U$)Nr)r)r:rIs r= reduce_labelBeitImageProcessor.reduce_labels2u%qj !sl r?cU(aURU5nU(aURXX]S9nU(aURXU S9nU(aURXU S9nU (aUR XXS9nU$)N)r@r&r'rB)r@r&rB)r@scalerB)r@meanstdrB)rNr center_croprescale normalize)r:r@r/r%r&r'r(r)r+r*r,r-r.rBs r= _preprocessBeitImageProcessor._preprocesssu %%e,E KKeKoE $$5Te$fE LLuVgLhE NNYNtE r?c[U5nU(a%[U5(a[RS5 U c [ U5n UR USUUUUUUUU U U U S9 nU b [ XU S9nU$)zPreprocesses a single image.zIt 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.F) r/r%r&r'r(r)r+r*r,r-r.rB)input_channel_dim)rrlogger warning_oncerrWr )r:r@r%r&r'r(r)r+r*r,r-r.rArBs r=_preprocess_image$BeitImageProcessor._preprocess_images$u% /%00   s   $ >u E   ")!)%!/!   "/VghE r?segmentation_mapc L[U5nURS:XaUSnSn [RnOSn Uc [ USS9nUR UUUUUUUSS[RS9 nU (a[ R"USS 9nUR[ R5nU$) z'Preprocesses a single segmentation map.)N.TFrL) num_channels) r@r/r%r'r&r(r)r,r+rBr)axis) rndimrFIRSTrrWnpsqueezeastypeint64) r:r_r%r&r'r(r)r/rBadded_dimensions r=_preprocess_segmentation_map/BeitImageProcessor._preprocess_segmentation_maps**:;  A %/ : "O 0 6 6 #O ($BCSbc$d!++"-).44,   !zz*:C +22288<r?c *>[TU]"U4SU0UD6$)Nsegmentation_maps)r8__call__)r:imagesrnr;r<s r=roBeitImageProcessor.__call__+s wV:KVvVVr?rprnreturn_tensorscJUbUO URnUbUO URn[USSS9nUbUO URnUbUO URnUbUO UR n[USSS9nUbUO UR nU bU O URn U bU O URn U bU O URn U bU O URn U bU O URn [U5nUb [USS9nUb[U5(d [S5e[U5(d [S5e[UU U U U UUUUUS 9 UVs/sHnUR!UUUUU UUU UU U UUS 9 PM nnS U0nUb*UVs/sHnUR#UU UUUUUS 9PM nnUUS '[%UUS9$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`. segmentation_maps (`ImageInput`, *optional*) Segmentation maps 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 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_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. do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. 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. Tr&rDr)ra)expected_ndimszwInvalid segmentation_maps type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.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)r%r&r') r@r%r(r+r,r'r&r*r)r-r.rArBr#)r_r/r%r'r&r(r)labels)data tensor_type)r%r&r r'r(r)r+r*r,r-r.r/rrrFrr]rkr)r:rprnr%r&r'r(r)r+r*r,r-r.r/rrrArBimgrvr_s r= preprocessBeitImageProcessor.preprocess0sDV"+!6IDNN 'tTYYTTfM'38+9+E4K^K^!*!6IDNN !)tP[\ #-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN /?/K+QUQfQf)&1  ( 89J[\ ]   (>O1P1P: F##:  &!)%!) :!   " "#-%)!-#%#'"3 # !  &'  (): !):$11%5%5'%#1'2):  !/DN>BBI , !s 9%F+F  target_sizesc URnUb[U5[U5:wa [S5e[U5(aUR 5n/n[ [U55Han[ RRRX5RSS9X%SSS9nUSRSS9nURU5 Mc U$URSS9n[ URS5Vs/sHoUPM nnU$s snf)a Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`BeitForSemanticSegmentation`]): Raw outputs of the model. target_sizes (`list[Tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. Returns: semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id. zTMake sure that you pass in as many target sizes as the batch dimension of the logitsr)dimbilinearF)r&mode align_cornersrL)logitslenrFrnumpyrangetorchnn functional interpolate unsqueezeargmaxappendshape) r:outputsr{rsemantic_segmentationidxresized_logits semantic_mapis r="post_process_semantic_segmentation5BeitImageProcessor.post_process_semantic_segmentations""  #6{c,// j|,,+113 $& !S[)!&!4!4!@!@K))a)0|7Hzin"A" .a077A7> %,,\: *%$%+MMaM$8 !GLMbMhMhijMkGl$mGl!1%=Gl !$m$$%ns9D ) r)r(r,r/r+r%r-r.r'r*r&) NNNNNNNNNNNN)NNNNNNN)N)&__name__ __module__ __qualname____firstlineno____doc__model_input_namesrrrBICUBICboolrdictstrintrfloatlistr9rfndarrayrr rrNrWr]rkrorerPILImagerytupler__static_attributes__ __classcell__)r<s@r=r!r!9sj(T(($+>?)-'9'A'A#.2,3!:>9=!&11tCH~&1% 1  1 DcN+ 1c5j)111U5$u+#5671E%e"45611 1@1H(:'A'A>BDH " zz" 38n" % " eC)9$9:; " $E#/?*?$@A "  " H*,0$()-15)-.2%)*.'+:>9=DH#4.D>  tCH~&  -. !DcN+TN!tnU5$u+#567E%e"456$E#/?*?$@AH%))-15)-.2%)*.'+:>9=>BDH++D>+tCH~& + -. + ! +DcN++TN+!+tn+U5$u+#567+E%e"456+eC)9$9:;+$E#/?*?$@A+ +`%))-15)-.2+/DH' $' D>' tCH~& ' -. ' ! ' DcN+' #4.' $E#/?*?$@A' RW %&37$()-15)-.2%)*.'+:>9=+/;?(8(>(>DH#YCYC$J/YCD> YC tCH~& YC -. YC!YCDcN+YCTNYC!YCtnYCU5$u+#567YCE%e"456YC#4.YC!sJ!78YC &!YC"$E#/?*?$@A#YC$ %YC'YCv)%QUV[Q\H])%)%r?r!)*rtypingrrrrfimage_processing_utilsrrrr image_transformsr r image_utilsr r rrrrrrrrrutilsrrrrrrutils.import_utilsrrr get_loggerrr[r!__all__r7r?r=rs&"jjC    +   H % ;{%+{% {%| r?