o Zh|8@sdZddlmZmZmZmZddlZddlm Z m Z m Z ddl m Z mZmZddlmZmZmZmZmZmZmZmZmZddlmZmZmZmZdd lm Z erUddl!Z!e"e#Z$d d Z%d d Z&e ddGddde Z'dgZ(dS)z#Image processor class for ImageGPT.)DictListOptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)rescaleresizeto_channel_dimension_format) ChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availablelogging)requirescCsf|j}tjt|dd}tjt|dd}t||}|dddfd||dddf}|S)NZaxisr)TnpsumZsquarematmul)abZa2b2abdr'e/var/www/auris/lib/python3.10/site-packages/transformers/models/imagegpt/image_processing_imagegpt.pysquared_euclidean_distance-s  (r)cCs$|dd}t||}tj|ddS)Nrrr)reshaper)rZargmin)xclustersr&r'r'r(color_quantize6s  r.)Zvision)backendscseZdZdZdgZdddejddfdeee e e e j fde deeee fded e d e d dffd d Zejddfde j deee fdedeeeefdeeeefd e j f ddZ  dde j deeeefdeeeefd e j fddZedddddddejdf dedee deeee fded ee d ee deee e e e j fdeeeefdeeeefdeeeefd ejjfddZZS)ImageGPTImageProcessora Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values" (color clusters). Args: clusters (`np.ndarray` or `List[List[int]]`, *optional*): The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overridden by `clusters` in `preprocess`. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by `do_resize` in `preprocess`. size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): Size of the image after resizing. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in `preprocess`. do_color_quantize (`bool`, *optional*, defaults to `True`): Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`. Z pixel_valuesNTr- do_resizesizeresample do_normalizedo_color_quantizereturnc sjtjdi||dur|nddd}t|}|dur!t|nd|_||_||_||_||_ ||_ dS)N)heightwidthr') super__init__r rarrayr-r1r2r3r4r5)selfr-r1r2r3r4r5kwargs __class__r'r(r;Ws  zImageGPTImageProcessor.__init__image data_formatinput_data_formatcKsTt|}d|vs d|vrtd||d|df}t|f||||d|S)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.BILINEAR`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. 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. r8r9zFThe `size` dictionary must contain the keys `height` and `width`. Got )r2r3rBrC)r ValueErrorkeysr )r=rAr2r3rBrCr>Z output_sizer'r'r(r ms#zImageGPTImageProcessor.resizecCst|d||d}|d}|S)a Normalizes an images' pixel values to between [-1, 1]. Args: image (`np.ndarray`): Image to normalize. 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?)rAscalerBrCr)r )r=rArBrCr'r'r( normalizesz ImageGPTImageProcessor.normalizeimagesreturn_tensorsc s|dur|nj}durnjtdurnj|dur%|nj}|dur.|nj}|dur7|nj}t|}t |}t |sKt dt |d|r\|dur\t ddd|D}|rpt |drptddurzt|d|rfd d|D}|rfd d|D}|rfd d|D}t|}t|||jdd }|jd} || d }t|}n fd d|D}d|i} t| |dS)aX 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_normalize=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_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`): Whether to color quantize the image. clusters (`np.ndarray` or `List[List[int]]`, *optional*, defaults to `self.clusters`): Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if `do_color_quantize` 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: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. Only has an effect if `do_color_quantize` is set to `False`. 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. NzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r1r2r3z8Clusters must be specified if do_color_quantize is True.cSsg|]}t|qSr')r.0rAr'r'r( sz5ImageGPTImageProcessor.preprocess..rzIt looks like you are trying to rescale already rescaled images. If you wish to do this, make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].csg|] }j|dqS))rAr2r3rC)r rJ)rCr3r=r2r'r(rLscsg|] }j|dqS))rArC)rGrJ)rCr=r'r(rLcsg|] }t|tjqSr')r r ZLASTrJ)rCr'r(rLrMr*csg|] }t|dqS))Zinput_channel_dim)r rJ)rBrCr'r(rL's Z input_ids)dataZ tensor_type)r1r2r r3r4r5r-rr<rrrDrrloggerZ warning_oncerr.r+shapelistr) r=rHr1r2r3r4r5r-rIrBrCZ batch_sizerNr')rBrCr3r=r2r( preprocesssZ6         z!ImageGPTImageProcessor.preprocess)NN)__name__ __module__ __qualname____doc__Zmodel_input_namesrZBILINEARrrrintrZndarrayboolrstrr;r r rGrZFIRSTrrPILZImagerR __classcell__r'r'r?r(r0<s   3     r0))rVtypingrrrrnumpyrZimage_processing_utilsrrr Zimage_transformsr r r Z image_utilsr rrrrrrrrutilsrrrrZutils.import_utilsrrZZ get_loggerrSrOr)r.r0__all__r'r'r'r(s",    t