cCiDSrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJr SS K J!r! \"5(aSSK"r"\RF"\$5r%\!"S S 9"S S \55r&S /r'g)z$Image processor class for Perceiver.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict) center_cropresizeto_channel_dimension_format) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availablelogging)requires)vision)backendsc!>^\rSrSrSrS/rSSSS\RSSSSS4 S\S\ \ \ \ 4S \S \ \ \ \ 4S \S \S \ \ \4S\S\ \ \\\4S\ \ \\\4SS4U4SjjjrSS\R$S\ \ \ 4S \ \ S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\RSS4S\R$S \ \ \ 4S \S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\"5SSSSSSSSSSS\R.S4 S\S\ \S\ \ \ \ 4S \ \S \ \ \ \ 4S \ \S \ \S \ \S\ \S\ \ \\\4S\ \ \\\4S\ \ \ \4S\S\ \ \ \4S\R6R64Sjj5rSrU=r$)PerceiverImageProcessor0a Constructs a Perceiver image processor. Args: do_center_crop (`bool`, `optional`, defaults to `True`): Whether or not to center crop the image. If the input size if smaller than `crop_size` along any edge, the image will be padded with zeros 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": 256, "width": 256}`): Desired output size when applying center-cropping. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image to `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` 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. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize: 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_center_crop crop_size do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stdreturnc  ">[T U]"S0U D6 UbUOSSS.n[USS9nUbUOSSS.n[U5nXlX lX0lX@lXPlX`lXpl Xl U bU O[Ul U bXlg[Ulg)N)heightwidthr$ param_name)super__init__rr#r$r%r&r'r(r)r*r r+r r,) selfr#r$r%r&r'r(r)r*r+r,kwargs __class__s r/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/perceiver/image_processing_perceiver.pyr7 PerceiverImageProcessor.__init__Xs "6"!*!6IsUX*DY&/&;AUimage data_formatinput_data_formatc Uc UROUn[U5n[USS9n[XS9upx[Xx5n USUS- U -n USUS- U -n [ U4X4UUS.UD6$)a Center crop an image to `(size["height"] / crop_size["height"] * min_dim, size["width"] / crop_size["width"] * min_dim)`. Where `min_dim = min(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Args: image (`np.ndarray`): Image to center crop. crop_size (`dict[str, int]`): Desired output size after applying the center crop. size (`dict[str, int]`, *optional*): Size of the image after resizing. If not provided, the self.size attribute will be used. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. r$r2) channel_dimr0r1)r&r?r@)r&rrminr ) r8r>r$r&r?r@r9r0r1min_dimcropped_height cropped_widths r;r #PerceiverImageProcessor.center_cropws8!LtyydT"!) D &uL f$x.9X+>>'Ig7);;wF    0#/     r=c [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)a Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Returns: `np.ndarray`: The resized image. r0r1zFThe `size` dictionary must contain the keys `height` and `width`. Got )r&r'r?r@)r ValueErrorkeysr )r8r>r&r'r?r@r9 output_sizes r;r PerceiverImageProcessor.resizesyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r=imagesreturn_tensorsc hUbUO URnUbUO URn[USS9nUbUO URnUbUO URn[U5nUbUO UR nUbUO UR nUbUO URnU bU O URn U bU O URn U bU O URn [U5n[U5(d [S5e[UUU U U UUUUUS9 UVs/sHn[U5PM nnU(a([!US5(a["R%S5 Uc['US5nU(aUVs/sHoR)XX^S9PM nnU(a UVs/sHnUR+XXnS9PM nnU(a UVs/sHnUR-XUS 9PM nnU (a UVs/sHnUR/XXS 9PM nnUVs/sH n[1XUS 9PM nnS U0n[3UU S 9$s snfs snfs snfs snfs snfs snf)aq 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_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image to `crop_size`. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Desired output size after applying the center crop. 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_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. 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: - `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$r2zkInvalid 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$r%r&r'rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r&r@)r>r&r'r@)r>scaler@)r>meanstdr@)input_channel_dimr")data tensor_type)r#r$rr%r&r'r(r)r*r+r,rrrIrrrlogger warning_oncerr r rescale normalizer r)r8rMr#r$r%r&r'r(r)r*r+r,rNr?r@r>rTs r; preprocess"PerceiverImageProcessor.preprocesss@,:+E4K^K^!*!6IDNN !) D !*!6IDNN 'tTYYT"'38#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN )&1F##:  &!)%!) 6<vay I  pvpvgl   bpv  $#E %X k#  $#E 5Rc d#  $#EUp#  ou ntej 'N_ `nt  '>BBQ=     s$<HHH +H%H*2H/) r$r#r*r(r%r+r,r'r)r&)NNN)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBICUBICboolrdictstrintrfloatlistr7npndarrayrr r rFIRSTrrPILImagerZ__static_attributes__ __classcell__)r:s@r;r r 0s"H(( $.2)-'9'A'A,3!:>9=VVDcN+V V tCH~& V % VVc5j)VVU5$u+#567VE%e"456V VVF#>BDH * zz* S>* sm * eC)9$9:; * $E#/?*?$@A *  * b(:'A'A>BDH . zz. 38n. % . eC)9$9:; . $E#/?*?$@A .  . `%&*..2$()-15%)*.'+:>9=;?(8(>(>DHICIC!ICDcN+ IC D> IC tCH~& IC-.ICTNIC!ICtnICU5$u+#567ICE%e"456IC!sJ!78IC&IC$E#/?*?$@AIC !IC'ICr=r )(r`typingrrnumpyriimage_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrrrrutilsrrrrutils.import_utilsrrl get_loggerr\rVr __all__r5r=r;rys+"UUPP    _^*   H % ;mC0mC mC` % %r=