o ZhN@sdZddlZddlZddlmZmZmZmZddlZ ddl m Z m Z m Z ddlmZmZmZddlmZmZmZmZmZmZmZmZmZmZmZmZddlm Z m!Z!m"Z"m#Z#e#$e%Z&e"rbddl'Z'Gd d d e Z(d gZ)dS) z!Image processor class for Gemma3.N)DictListOptionalUnion)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc&s@eZdZdZddgZddejddddddddddf dedee e e fd ed ed e e e fd ed ee e ee fdee e ee fdeedeedee dee dee ddffdd Z  d"dejde de de dee e efdee e eff ddZ  d"deejdede de de dee e efdee e effddZedddddddddejddddddfdedeedee e e fd ed eed ee d eed ee e ee fdee e ee fdee e efdeedee e efdeedeedee dee dee dejjf$d d!ZZS)#Gemma3ImageProcessoraI Constructs a SigLIP 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 `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` 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 `1/255`): 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 by the specified mean and standard deviation. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): 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 `[0.5, 0.5, 0.5]`): 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. do_pan_and_scan (`bool`, *optional*): Whether to apply `pan_and_scan` to images. pan_and_scan_min_crop_size (`int`, *optional*): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*): Minimum aspect ratio to activate pan and scan. pixel_values num_cropsTNgp? do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbdo_pan_and_scanpan_and_scan_min_crop_sizepan_and_scan_max_num_crops"pan_and_scan_min_ratio_to_activatereturnc stjdi||dur|nddd}t|dd}|dur |nt}|dur(|nt}||_||_||_||_||_ ||_ ||_ ||_ | |_ | |_| |_| |_| |_dS)N)heightwidthT)default_to_square)super__init__r r rr r!r"r#r$r%r&r'r(r)r*r+r,)selfr r!r"r#r$r%r&r'r(r)r*r+r,kwargs __class__r2a/var/www/auris/lib/python3.10/site-packages/transformers/models/gemma3/image_processing_gemma3.pyr4_s$  zGemma3ImageProcessor.__init__image data_formatinput_data_formatcsrt\}}||kr6|||krgStt||d} ttt||| } td| } t|| } d} n+|||kr>gStt||d} ttt||| } td| } t|| } d} tt|| tt|| t|kr|gSfddt| D} fddt| D} |tj krfddt | | D} | Sfddt | | D} | S) a Pan and Scan and image, by cropping into smaller images when the aspect ratio exceeds minimum allowed ratio. Args: image (`np.ndarray`): Image to resize. pan_and_scan_min_crop_size (`int`, *optional*): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*): Minimum aspect ratio to activate pan and scan. 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?cg|]}|qSr2r2.0i) crop_size_wr2r9 z5Gemma3ImageProcessor.pan_and_scan..cr?r2r2r@) crop_size_hr2r9rDrEcs,g|]\}}||||fqSr2r2rAZpos_hZpos_wrFrCr:r2r9rDscs2g|]\}}dd||||fqS)Nr2rGrHr2r9rDs ) rintmathfloorminmaxceilrangerZLAST itertoolsproduct)r5r:r*r+r,r;r<r/r0Z num_crops_wZ num_crops_hZcrop_positions_wZcrop_positions_hZ image_cropsr2rHr9 pan_and_scans>           z!Gemma3ImageProcessor.pan_and_scanimagesc CsNg}g} |D]} |j| |||||d} || g| | t| q|| fS)N)r:r*r+r,r;r<)rRextendappendlen) r5rSr)r*r+r,r;r<Zpas_images_listrr:Z pas_imagesr2r2r9 _process_images_for_pan_and_scans z5Gemma3ImageProcessor._process_images_for_pan_and_scanreturn_tensorsc Cs8|dur|n|j}|dur|n|j}t|ddd}|dur|n|j}|dur(|n|j}|dur1|n|j}|dur:|n|j}|durC|n|j}| durL| n|j} | durU| n|j } |dur^|n|j }|durg|n|j }|durp|n|j }|dury|n|j }t|}t|stdt||||| |||d| rdd|D}d d|D}|rt|d rtd | durt|d } |r|j|||||| | d \}}nd d|D}g}|D]9}|r|d|d}}t|||f|| d}|r|j||| d}|r|j||| | d}t|| | d}||q||d}t|| dS)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. 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 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`. 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. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. do_pan_and_scan (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to apply `pan_and_scan` to images. pan_and_scan_min_crop_size (`int`, *optional*, defaults to `self.pan_and_scan_min_crop_size`): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*, defaults to `self.pan_and_scan_max_num_crops`): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*, defaults to `self.pan_and_scan_min_ratio_to_activate`): Minimum aspect ratio to activate pan and scan. Nr!F) param_namer1zkInvalid 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"cSg|]}t|qSr2)r rAr:r2r2r9rDdrEz3Gemma3ImageProcessor.preprocess..cSrZr2)rr[r2r2r9rDgrErzIt looks like you are trying to rescale already rescaled images. 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