rhNAdZddlmZddlmZmZddlZddlm Z m Z m Z ddl m Z mZmZddlmZmZmZmZmZmZmZmZmZmZmZdd lmZmZmZdd l m!Z!ejDe#Z$e!d Gd de Z%dgZ&y)z Image processor class for LeViT.)Iterable)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)get_resize_output_image_sizeresizeto_channel_dimension_format) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)requires)vision)backendsc!leZdZdZdgZddej dddddeef de de e e e fded e d e e e e fd e d ee efd e de eeeefde eeeefddffd Zej ddfdej&de e e fdede ee efde ee efdej&f dZedddddddddddej.df dede e de e e e fded e e d e e e e fd e e d e ed e e de eeeefde eeeefde edede ee efdefdZxZS)LevitImageProcessora Constructs a LeViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width, size["shortest_egde"])`. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether or not to center crop the input to `(crop_size["height"], crop_size["width"])`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired image size after `center_crop`. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Controls 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`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`list[int]`, *optional*, defaults to `[0.485, 0.456, 0.406]`): 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 (`list[int]`, *optional*, defaults to `[0.229, 0.224, 0.225]`): 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_resizesizeresampledo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stdreturnc  2t |di| ||nddi}t|d}||nddd}t|d}||_||_||_||_||_||_||_ ||_ | | nt|_ | | |_yt|_y) N shortest_edgeFdefault_to_squareheightwidthr% param_name)super__init__r r!r"r#r$r%r&r'r(r r)rr*) selfr!r"r#r$r%r&r'r(r)r*kwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/levit/image_processing_levit.pyr8zLevitImageProcessor.__init__[s "6"'tos-CTU;!*!6IsUX*DY&/&;AUimage data_formatinput_data_formatc t|d}d|vr+td|dz}t||d|} | d| dd}d |vsd |vrtd |j t |f|d |d f|||d |S) a- Resize an image. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width, size["shortest_egde"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Fr/r-g$I$I?)r"r0r@rr1r2r3zFSize dict must have keys 'height' and 'width' or 'shortest_edge'. Got )r"r#r?r@)r intr ValueErrorkeysr ) r9r>r"r#r?r@r: size_dictr- output_sizes r<r zLevitImageProcessor.resizezsD"$%@ d "d?.C CDM6MUVgK$/q>KNKI 9 $y(@XYbYgYgYiXjk   H%y'9:#/     r=imagesreturn_tensorsc ,||n |j}||n |j}||n |j}||n |j}||n |j}| | n |j } | | n |j } | | n |j} ||n |j}t|d}||n |j}t|d}t|}t|s tdt||| | | ||||| |Dcgc] }t|}}|r#t!|drt"j%d|t'|d}|r"|Dcgc]}|j)|||| }}|r!|Dcgc]}|j+||| }}|r!|Dcgc]}|j-||| }}| r"|Dcgc]}|j/|| | | }}|Dcgc]}t1|| | }}d |i}t3|| Scc}wcc}wcc}wcc}wcc}wcc}w) a Preprocess an image or batch of images to be used as input to a LeViT model. Args: images (`ImageInput`): Image or batch of images 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 output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. 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 output image after center cropping. Crops images to (crop_size["height"], crop_size["width"]). do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Factor to rescale the image pixel values by. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image pixel values by `image_mean` and `image_std`. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Mean to normalize the image pixel values by. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Standard deviation to normalize the image pixel values by. 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 (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`): 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. 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. Fr/r%r4zkInvalid 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@)input_channel_dimr )data tensor_type)r!r#r$r&r'r(r)r*r"r r%rrrDrrrlogger warning_oncerr center_croprescale normalizer r)r9rHr!r"r#r$r%r&r'r(r)r*rIr?r@r>rLs r< preprocesszLevitImageProcessor.preprocesssnH"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYTU;!*!6IDNN !) D $V,F#:  &!)%!) 6<vay I  kqrbgdkk%xK\k]rFr kqrbgd&&uiK\&]rFr lrschdll5.L]l^sFs oufkuj)O`aF ou ej '{N_ `  '>BB?=sst s$5G8G=(H H.H H)__name__ __module__ __qualname____doc__model_input_namesrBICUBICr rboolrdictstrrCrfloatrr8npndarrayrr rFIRSTrrrrS __classcell__)r;s@r<rr0s%N(()-'9'A'A#.2,3!>S=QVVtCH~&V% V  V DcN+ VVc5j)VVU5(5/#9:;VE%%"89:V VF(:'A'A>BDH 5 zz5 38n5 % 5 eC)9$9:; 5 $E#/?*?$@A 5  5 n%&%))-'+)-.2%)*.'+>B=A/3(8(>(>DHCCCCD>CCtCH~& CC % CC ! CCDcN+CCTNCC!CCtnCCU5(5/#9:;CCE%%"89:CC!,CC&CC$E#/?*?$@ACC !CC'CCr=r)'rWcollections.abcrtypingrrnumpyr^image_processing_utilsrrr image_transformsr r r image_utilsr rrrrrrrrrrutilsrrrutils.import_utilsr get_loggerrTrNr__all__r6r=r<rls'$"UU    JI*   H % ;DC,DC DCN ! !r=