rhAdZddlmZmZddlZddlmZmZm Z ddl m Z m Z m Z ddlmZmZmZmZmZmZmZmZmZddlmZmZmZmZej:eZerddl Z Gd d eZ!d gZ"y) z$Image processor class for Chameleon.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)get_resize_output_image_sizeresizeto_channel_dimension_format) ChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc#eZdZdZdgZddej jddddddddf dede e e e fde d ed e e e e fd ed ee efd ede eeeefde eeeefdeddffd Ze j$ddfdej(de e e fde de ee efde ee efdej(f dZeddddddddddddej0dfdede ede e e e fde d e ed e e d e ed e ed e ede eeeefde eeeefde ede ee efde ede ee efdej j f dZdedefdZxZS)ChameleonImageProcessora Constructs a Chameleon 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 the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 512}`): Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to 1): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method. crop_size (`dict[str, int]` *optional*, defaults to {"height": 512, "width": 512}): Size of the output image after applying `center_crop`. Can be overridden by `crop_size` 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 0.0078): 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 `do_normalize` in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `[1.0, 1.0, 1.0]`): 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 `[1.0, 1.0, 1.0]`): 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. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. pixel_valuesTNgq? do_resizesizeresampledo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbreturnc  .t |d i| ||nddi}t|d}||nddd}t|dd}||_||_||_||_||_||_||_ ||_ | | ngd |_ | | ngd |_ | |_ y) N shortest_edgeiF)default_to_square)heightwidthTr )r* param_name)?r.r.)super__init__rrrrrr r!r"r#r$r%r&)selfrrrrr r!r"r#r$r%r&kwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/chameleon/image_processing_chameleon.pyr1z ChameleonImageProcessor.__init__Ts "6"'tos-CTU;!*!6IsUX*O&/&;,image data_formatinput_data_formatc d}d|vr|d}d}nd|vrd|vr |d|df}n tdt||||}t|f||||d|S) aZ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.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 (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Tr)Fr+r,zASize must contain either 'shortest_edge' or 'height' and 'width'.)rr*r9)rrr8r9) ValueErrorr r ) r2r7rrr8r9r3r* output_sizes r5r zChameleonImageProcessor.resizevs2! d "(D %   'T/NDM2D`a a2 //    #/     r6imagesreturn_tensorsc >||n |j}||n |j}t|dd}||n |j}||n |j}||n |j }t|dd}||n |j }||n |j}| | n |j} | | n |j} | | n |j} | | n |j} t|}t|s tdt||| | | ||||| | r|Dcgc]}|j!|}}|Dcgc] }t#|}}|r#t%|drt&j)d |t+|d}g}|D]m}|r|j-|||| }|r|j/||| }|r|j1||| }| r|j3|| | | }|j5|o|Dcgc]}t7|||}}d|i}t9|| Scc}wcc}wcc}w)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 resizing. Shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. 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 center crop. Only has an effect if `do_center_crop` 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 to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. 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. rF)r-r*r TzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.) r!r"r#r$r%rr rrrrzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r7rrr9)r7rr9)r7scaler9)r7meanstdr9)input_channel_dimr)data tensor_type)rrrrrr r!r"r#r$r%r&rrr;r blend_rgbarrlogger warning_oncerr center_croprescale normalizeappendr r)r2r=rrrrr r!r"r#r$r%r&r>r8r9r7 all_imagesrDs r5 preprocessz"ChameleonImageProcessor.preprocesss~L"+!6IDNN 'tTYYTfN'38+9+E4K^K^!*!6IDNN !) W[\ #-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN +9+E4K^K^)&1F#:  &!)%!)  :@Adooe,AFA6<vay I   %E %dXar s((u9Xi(j 5Zk ljiSd'   e $ %$$  ({N_ `  '>BBKB=8 s H'H(Hc$t|tjjs|S|jdk(r|St j |j d}|dddddfdkjs|j dS|dddddfdz }d|ddddtjfz dz|ddddtjf|ddddddfzz}tjj|jddS) a Convert image to RGB by blending the transparency layer if it's in RGBA format. If image is not `PIL.Image`, it si simply returned without modifications. Args: image (`ImageInput`): Image to convert. RGBRGBANrgo@uint8) isinstancePILImagemodenparrayconvertanynewaxis fromarrayastype)r2r7img_rgbaalphaimg_rgbs r5rFz"ChameleonImageProcessor.blend_rgba8s%1L ZZ5 L88EMM&12Aq!C',,.==' 'Aq!E)uQ2::-..#5aBJJ>N8ORZ[\^_acbcac[cRd8ddyy""7>>'#:EBBr6)__name__ __module__ __qualname____doc__model_input_namesrVrWLANCZOSboolrdictstrintrrfloatlistr1BICUBICrYndarrayr r rFIRSTr rrNrF __classcell__)r4s@r5rr+s$$L(()-'*yy'8'8#.2,2!:>9=#--tCH~&-% -  - DcN+ --c5j)--U5$u+#567-E%e"456-- -L(:'A'A>BDH / zz/ 38n/ % / eC)9$9:; / $E#/?*?$@A /  / b%&%))-'+)-#'%)*.'+:>9=)-;?2B2H2HDH!NCNCD>NCtCH~& NC % NC ! NCC=NCTNNC!NCtnNCU5$u+#567NCE%e"456NC!NC!sJ!78NC./NC $E#/?*?$@A!NC" #NC'NC`C CzCr6r)#rftypingrrnumpyrYimage_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrutilsrrrr get_loggerrcrGrVr__all__r/r6r5r{sm+"UUaa   _^   H %fC0fCR % %r6