`i)BdZddlmZmZddlZddlmZmZm Z ddl m Z m Z m Z ddlmZmZmZmZmZmZmZmZmZddlmZmZmZmZejeZerddl Z Gd d eZ!d gZ"dS) 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#peZdZdZdgZddejjddddddddf dede e e e fde d ed e e e e fd ed ee efd ede eeeefde eeeefdeddffd Ze jddfdejde e e fde de ee efde ee efdejf dZeddddddddddddejdfdede ede e e e fde e d e ed e e d e ed e ed e ede eeeefde eeeefde ede ee efde ede ee efdejjf 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  Ttjd i| ||nddi}t|d}||nddd}t|dd}||_||_||_||_||_||_||_ ||_ | | ngd |_ | | ngd |_ | |_ dS) 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$r%r&kwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/chameleon/image_processing_chameleon.pyr1z ChameleonImageProcessor.__init__Ts ""6"""'ttos-CTU;;;!*!6IIsUX**OOO&/&;,image data_formatinput_data_formatc d}d|vr |d}d}n(d|vrd|vr|d|df}ntdt||||}t|f||||d|S) aZ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Tr)Fr+r,zASize must contain either 'shortest_edge' or 'height' and 'width'.)rr*r9)rrr8r9) ValueErrorr r ) r2r7rrr8r9r3r* output_sizes r5r zChameleonImageProcessor.resizevs2! d " "(D %     'T//NDM2DD`aa a2 //      #/       r6imagesreturn_tensorsc ||nj}||nj}t|dd}||nj}||nj}||nj}t|dd}||nj}||nj}| | nj} | | nj } | | nj } | | nj } |}t|}t|stdt!||| | | ||||| | rfd |D}d |D}|r/t#|d rt$d t)|d g}|D]}|r||| }|r||}|r||}| r|| | }||fd|D}d|i}t5|| S)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. NrF)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$r%rr rrrc:g|]}|Sr/) blend_rgba).0r7r2s r5 z6ChameleonImageProcessor.preprocess..s%AAAdooe,,AAAr6c,g|]}t|Sr/)r)rBr7s r5rCz6ChameleonImageProcessor.preprocess..s <<.1s9    ({N_ ` ` `   r6r)data tensor_type)rrrrrr r!r"r#r$r%r& fetch_imagesrrr;rrlogger warning_oncerr center_croprescale normalizeappendr)r2r=rrrrr r!r"r#r$r%r&r>r8r9 all_imagesr7rJs` `` r5 preprocessz"ChameleonImageProcessor.preprocesssL"+!6IIDN 'ttTYTfNNN'388+9+E4K^!*!6IIDN !) W[\\\ #-#9ZZt +9+E4K^'3'?||TEV #-#9ZZt !*!6IIDN +9+E4K^""6**)&11F## :  &!)%!)   BAAAA&AAAF=vay I I   % %E t %dXar ss k((u9Xi(jj m 5Zk ll jiSd'   e $ $ $ $     #    '>BBBBr6c@t|tjjs|S|jdkr|St j|d}|dddddfdks|dS|dddddfdz }d|ddddtjfz dz|ddddtjf|ddddddfzz}tj | ddS) 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 r5rAz"ChameleonImageProcessor.blend_rgba9s6%11 L Z5 L8EMM&1122AAAq!C',,.. (=='' 'AAAq!E)uQQQ2:-..#5aaaBJ>N8ORZ[\[\[\^_^_^_acbcac[cRd8ddy""7>>'#:#:EBBBr6)__name__ __module__ __qualname____doc__model_input_namesr\r]LANCZOSboolrdictstrintrrfloatlistr1BICUBICr_ndarrayr r rFIRSTr rrTrA __classcell__)r4s@r5rr+s$$L(()-'*y'8#.2,2!:>9=#---tCH~&-% -  - DcN+ --c5j)--U5$u+#567-E%e"456-- ------L(:'A>BDH / / z/ 38n/ % / eC)9$9:; / $E#/?*?$@A /  / / / / b%$&&%))-15)-#'%)*.'+:>9=)-;?2B2HDH!OCOCOCD>OCtCH~& OC -. OC ! OCC=OCTNOC!OCtnOCU5$u+#567OCE%e"456OC!OC!sJ!78OC./OC $E#/?*?$@A!OC" #OCOCOC'&OCbC CzCCCCCCCCr6r)#rltypingrrnumpyr_image_processing_utilsrrrimage_transformsr r r image_utilsr r rrrrrrrutilsrrrr get_loggerrirMr\r__all__r/r6r5rsz+*""""""""UUUUUUUUUUaaaaaaaaaa                      _^^^^^^^^^^^  H % %JJJgCgCgCgCgC0gCgCgCT % %r6