sgMdZddlZddlZddlmZmZmZddlZddl m Z ddl m Z m Z ddlmZmZmZmZddlmZmZmZmZmZmZmZdd lmZmZmZmZdd l m!Z!erddl"Z"dd l#m$Z$m%Z%m&Z&erddl'Z'ejPe)Z*d Z+d Z, d de-de.de-de-de.de.de.de.dee/dee-de$jHfdZ0 d!dejbde-deee-e2ffdZ3Gdde Z4y)"z%Image processor class for Pix2Struct.N)DictOptionalUnion)hf_hub_download)BaseImageProcessor BatchFeature)convert_to_rgb normalizeto_channel_dimension_format to_pil_image)ChannelDimension ImageInputget_image_sizeinfer_channel_dimension_formatmake_list_of_imagesto_numpy_array valid_images) TensorTypeis_torch_availableis_vision_availablelogging)requires_backends)Image ImageDraw ImageFontzybelkada/fontscttdg|jd}tjj j |||f||f}|j|jd|jd||d}|jdddddj|jd|z|jd|z|jd|z|z}|jdS) a Utiliy function to extract patches from a given image tensor. Returns a tensor of shape (1, `patch_height`, `patch_width`, `num_channels`x `patch_height` x `patch_width`) Args: image_tensor (torch.Tensor): The image tensor to extract patches from. patch_height (int): The height of the patches to extract. patch_width (int): The width of the patches to extract. torchr)strider) rtorch_extract_patches unsqueezernn functionalunfoldreshapesizepermute) image_tensor patch_height patch_widthpatchess m/var/www/html/venv/lib/python3.12/site-packages/transformers/models/pix2struct/image_processing_pix2struct.pyr$r$4s+gY7))!,Lhh!!(( k7R\hju[v(wGool//2L4E4Ea4H,XceghGooaAq!,44! ,! +!|+k9G   Q text text_size text_colorbackground_color left_padding right_padding top_paddingbottom_padding font_bytes font_pathreturnc Tttdtjd} | j |} dj | } || t j|} n| | } nttd} tj| d|} tjtjd d |}|j!d | | \}}}}||z|z}||z|z}tjd ||f|}tj|}|j#||f| || |S) a Render text. This script is entirely adapted from the original script that can be found here: https://github.com/google-research/pix2struct/blob/main/pix2struct/preprocessing/preprocessing_utils.py Args: text (`str`, *optional*, defaults to ): Text to render. text_size (`int`, *optional*, defaults to 36): Size of the text. text_color (`str`, *optional*, defaults to `"black"`): Color of the text. background_color (`str`, *optional*, defaults to `"white"`): Color of the background. left_padding (`int`, *optional*, defaults to 5): Padding on the left. right_padding (`int`, *optional*, defaults to 5): Padding on the right. top_padding (`int`, *optional*, defaults to 5): Padding on the top. bottom_padding (`int`, *optional*, defaults to 5): Padding on the bottom. font_bytes (`bytes`, *optional*): Bytes of the font to use. If `None`, the default font will be used. font_path (`str`, *optional*): Path to the font to use. If `None`, the default font will be used. visionP)width)r2 z Arial.TTFzUTF-8)encodingr*RGB)r r rr)xyr2fillfont)r render_texttextwrap TextWrapperwrapjoinioBytesIOrDEFAULT_FONT_PATHrtruetyperDrawrnewtextbboxr2)r2r3r4r5r6r7r8r9r:r;wrapperlines wrapped_textrG temp_draw_ text_width text_height image_width image_heightimagedraws r0rHrHOs$Lk8,"",G LLdL #E99U#L)"3zz*%  0+>   dW9 EDuyy8HIJI$-$6$6v|T$R!Aq*k|+m;K,~=L IIek<8:J KE >>% DII, ,)r`rCwhiterDr)r render_headerr rHmaxr@intheightrrRpasteresizerrrLASTr ) r]r_r`kwargs header_image new_width new_heightnew_header_height new_images r0rcrcs$mX. 2C DEv00LL&& 4IU\\Y%<=>JL//9|?Q?Q3QRS %)Z:K-K!LgVI OOL''4E(FGP OOELL)Z!89A?P;QRy)I%i04D4I4II/ ;K;P;PQ r1ceZdZdZdgZ ddededeeefdeded df fd Z dd e jdede d e eeefd e jf d Z dd e jde eeefd e eeefd e jfdZddddddej$dfdede edede ede ede eeefde eeefded e eeefd efdZxZS)Pix2StructImageProcessoraf Constructs a Pix2Struct image processor. Args: do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. According to Pix2Struct paper and code, the image is normalized with its own mean and standard deviation. patch_size (`Dict[str, int]`, *optional*, defaults to `{"height": 16, "width": 16}`): The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16. max_patches (`int`, *optional*, defaults to 2048): The maximum number of patches to extract from the image as per the [Pix2Struct paper](https://arxiv.org/pdf/2210.03347.pdf). is_vqa (`bool`, *optional*, defaults to `False`): Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is rendered onto the input images. flattened_patchesNdo_convert_rgb do_normalize patch_size max_patchesis_vqar<c xt|di|||nddd|_||_||_||_||_y)N)rfr@)super__init__rurtrsrvrw)selfrsrtrurvrwrj __class__s r0r|z!Pix2StructImageProcessor.__init__sH "6"(2(>*r\^D_(,& r1r]r`c t|jdt|tj|}t j |}|d|d}}t|tj\}} tj|||z z|| z z} tttj| |z|z |d} tttj| | z|z |d} t| |zd} t| |zd}t jjj|j!d| |fddd j#d}t%|||}|j&}|d}|d }|d }|j)||z|g}t j*|j)|dgj-d|j)||zdg}t j*|j)d|gj-|dj)||zdg}|dz }|dz }|j/t j0}|j/t j0}t j2|||gd }t jjj5|ddd|||zz gj7}t9|}|S) a Extract flattened patches from an image. Args: image (`np.ndarray`): Image to extract flattened patches from. max_patches (`int`): Maximum number of patches to extract. patch_size (`dict`): Dictionary containing the patch height and width. Returns: result (`np.ndarray`): A sequence of `max_patches` flattened patches. rrfr@r rbilinearFT)r*mode align_corners antialiasr#rr!)rextract_flattened_patchesr rFIRSTr from_numpyrmathsqrtrdminfloorr&r' interpolater%squeezer$shaper)arangerepeattofloat32catpadfloatr)r}r]rvrur`rjr-r.r\r[scalenum_feasible_rowsnum_feasible_colsresized_height resized_widthr/ patches_shaperowscolumnsdepthrow_idscol_idsresults r0rz2Pix2StructImageProcessor.extract_flattened_patchess. $88'B,E3C3I3IK\]  '$.x$8*W:Mk $25:J:P:P$Q! k + )DEWbIbcdDJJu|/Cl/R$SU` acdeDJJu{/B[/P$QS^ _abc.=qA- ;Q? ##// OOA  -0 0 '!* (|[I Q"a //4'>5"9:,,t$,,dAY7>>q'JRRTX[bTbdeSfg,,w'//G =DDT1MUUW[^eWeghVij 1 1 **U]]+**U]]+GWg6;$$((!Q;$QX.;Y1Z[aac' r1 data_formatc n|jtjk(r|jtj}tj |}tj |}t|dtjtj|jz }t|f||||d|S)a Normalize an image. image = (image - image_mean) / image_std. The image std is to mimic the tensorflow implementation of the `per_image_standardization`: https://www.tensorflow.org/api_docs/python/tf/image/per_image_standardization Args: image (`np.ndarray`): Image to normalize. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. g?)meanstdrr`) dtypenpuint8astyperrrrdrrprodrr )r}r]rr`rjrradjusted_stddevs r0r z"Pix2StructImageProcessor.normalize5s, ;;"(( "LL,Ewwu~ffUmc32775;;3G)H#HI  #/     r1images header_textreturn_tensorsc ||n |j}||n |j}||n |j}||n |j}|j} | j dd t dt|}t|s t d|r|D cgc] } t| }} |D cgc] } t| }} | t|d} | r}| t d| jdd} | jdd}t|tr|gt|z}t!|D cgc]\}} t#| ||| | }}} |r |D cgc]} |j%| | }} |D cgc]} |j'| ||| }} |D cgc]4} | j)d dk7j+t,j.6}} t1||d|}|Scc} wcc} wcc} }wcc} wcc} wcc} w)a Preprocess an image or batch of images. The processor first computes the maximum possible number of aspect-ratio preserving patches of size `patch_size` that can be extracted from the image. It then pads the image with zeros to make the image respect the constraint of `max_patches`. Before extracting the patches the images are standardized following the tensorflow implementation of `per_image_standardization` (https://www.tensorflow.org/api_docs/python/tf/image/per_image_standardization). Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images. header_text (`Union[List[str], str]`, *optional*): Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. max_patches (`int`, *optional*, defaults to `self.max_patches`): Maximum number of patches to extract. patch_size (`dict`, *optional*, defaults to `self.patch_size`): Dictionary containing the patch height and width. 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. Nrz8data_format is not an accepted input as the outputs are zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.rz.A header text must be provided for VQA models.r:r;)r:r;)r]r`)r]rvrur`r!)axis)rrattention_mask)data tensor_type)rtrsrurvrwget ValueErrorrrr rrpop isinstancestrlen enumeratercr rsumrrrr )r}rrrsrtrvrurrr`rjrwr]r:r;iattention_masksencoded_outputss r0 preprocessz#Pix2StructImageProcessor.preprocess\s?j(4'?|TEVEV +9+E4K^K^#-#9Zt %0%vay I  " !QRRL$7J ;5I+s+*mc&k9 !*& 1Aue[^ V_`F djk[`dnn5DUnVkFk    * *_p +   V\\EEII2I.!3;;BJJG\\&'-Q_m SA= l ]s$G!)G&-G+G12G69G;)TTNiFN)NN)__name__ __module__ __qualname____doc__model_input_namesboolrrrer|rndarraydictrrrrr rrrr __classcell__)r~s@r0rqrqs(-- $!%) cN      *EI OzzOO O $E#/?*?$@A O Oh?CDH % zz% eC)9$9:;% $E#/?*?$@A %  % T&*#'+%)/3;?(8(>(>DHqqc]q q tn q c] qT#s(^,q!sJ!78q&q$E#/?*?$@Aq qr1rq) $blackrbrrrNNr)5rrMrtypingrrrnumpyrhuggingface_hubrimage_processing_utilsrr image_transformsr r r r image_utilsrrrrrrrutilsrrrrutils.import_utilsrrIPILrrrr get_loggerrloggerrOr$rrebytesrHrChildProcessErrorrcrqrzr1r0rsd, ((+FddRQ3//   H %$ :#"&#@ @@@ @  @  @@@@}@ [[@Jbf' ::'"'7?cK\F\@]7^'TP1Pr1