tjH, 6ddlZddlmZddlmZddlZddlmZddlm Z ddl m Z m Z ddl mZmZmZmZdd lmZdd lmZmZmZd d lmZerdd lmZddlmZdddeeezde dedef dZ!GddedZ"eGddeZ#dgZ$dS)N)Iterable) TYPE_CHECKING)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDPILImageResamplingSizeDict) ImagesKwargs) TensorTypeauto_docstringrequires_backends)BeitImageProcessor)DepthEstimatorOutput) functional input_image torch.Tensor output_sizekeep_aspect_ratiomultiplereturncdd}|jdd\}}|\}}||z } ||z } |r+td| z td| z kr| } n| } || |z|} || |z|} t| | S)Nrct||z |z}| ||krtj||z |z}||krtj||z |z}|SN)roundmathfloorceil)valrmin_valmax_valxs a/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/dpt/modular_dpt.pyconstrain_to_multiple_ofz>get_resize_output_image_size..constrain_to_multiple_of0sd #. ! !H ,  1w;; 3>**X5A w;; #.))H4A)rheightwidth)rN)shapeabsr ) rrrrr( input_height input_width output_height output_width scale_height scale_width new_height new_widths r'get_resize_output_image_sizer9*s    !, 1"## 6L+"-M<!</L,K' q;  #a,&6"7"7 7 7&LL'K)),*EPXYYYJ(({)BXVVVI :Y 7 7 77r)c<eZdZUdZeed<eed<eed<eed<dS)DPTImageProcessorKwargsa= ensure_multiple_of (`int`, *optional*, defaults to 1): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overridden by `ensure_multiple_of` in `preprocess`. keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can be overridden by `keep_aspect_ratio` in `preprocess`. do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. ensure_multiple_of size_divisorrdo_reduce_labelsN)__name__ __module__ __qualname____doc__int__annotations__boolr)r'r;r;QsN  r)r;F)totalc$eZdZejZeZeZ dddZ dZ dZ dZ dZdZdZdZdZdZdZeZ d%dd d ed d d ededzdedd fdZ d&dd dedd fdZded deded ed d dedededededeeezdzdeeezdzdededzdededzdedzdef$d Z d'd!d"d#e ee!eefzdzdzdee"e#e ffd$Z$dS)(DPTImageProcessorir,TFgp?r+Nimagersizeresamplez7PILImageResampling | tvF.InterpolationMode | int | None antialiasr<rrc|jr|js$td|t ||j|jf||}t j|||||S)a< Resize an image to `(size["height"], size["width"])`. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`. antialias (`bool`, *optional*, defaults to `True`): Whether to use antialiasing when resizing the image ensure_multiple_of (`int`, *optional*): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, and `do_resize` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Returns: `torch.Tensor`: The resized image. zDThe size dictionary must contain the keys 'height' and 'width'. Got )rrr)rLrM)r-r. ValueErrorkeysr9rresize)selfrJrKrLrMr<rrs r'rQzDPTImageProcessor.resizezs:{ s$* sqdhdmdmdodoqqrr r2 dj1/'    "(ukH`ijjjjr)r=c|jdd\}}d}|||\}}|||\}} ||| |f} tj|| S)a Center pad a batch of images to be a multiple of `size_divisor`. Args: image (`torch.Tensor`): Image to pad. Can be a batch of images of dimensions (N, C, H, W) or a single image of dimensions (C, H, W). size_divisor (`int`): The width and height of the image will be padded to a multiple of this number. r*Nc\tj||z |z}||z }|dz}||z }||fS)Nr)r r")rKr=new_sizepad_size pad_size_leftpad_size_rights r'_get_padz-DPTImageProcessor.pad_image.._get_padsBy !455 DH$H$MM% 5N .0 0r))r/tvFpad) rRrJr=r-r.rYpad_top pad_bottompad_left pad_rightpaddings r' pad_imagezDPTImageProcessor.pad_imagesu BCC(  1 1 1'hv|<<&hul;;)Wi<wug&&&r)imagesr> do_resizedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stddo_paddisable_groupingc |r||}t||\}}i}|D]&\}}|r|||||| }|||<'t ||}t||\}}i}|D]T\}}|r|||}|||| | | | }|r|||}|||<Ut ||}|S)N)rl)rJrKrLr<r) reduce_labelritemsrQr center_croprescale_and_normalizera)rRrbr>rcrKrLrdrerfrgrhrirjrr<rkr=rlkwargsgrouped_imagesgrouped_images_indexresized_images_groupedr/stacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess r' _preprocesszDPTImageProcessor._preprocesssx*  /&&v..F0EV^n/o/o/o,,!#%3%9%9%;%; ; ; !E> !%(%'9&7 "-""-; "5 ) )'(>@TUU0E^fv/w/w/w,,#% %3%9%9%;%; = = !E> M!%!1!1.)!L!L!77 NL*V_N N!% !M!M.< $U + +)*BDXYYr)outputsr target_sizesct|d|j}|/t|t|krtdg}|dgt|zn|}t ||D]~\}}|`t jj| d d|dd }| d |i|S) a Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. Only supports PyTorch. Args: outputs ([`DepthEstimatorOutput`]): Raw outputs of the model. target_sizes (`TensorType` or `List[Tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized. Returns: `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions. torchNz]Make sure that you pass in as many target sizes as the batch dimension of the predicted depthrr+bicubicF)rKmode align_cornerspredicted_depth) rrlenrOzipr~nnr interpolate unsqueezesqueezeappend)rRr{r|rresultsdepth target_sizes r'post_process_depth_estimationz/DPTImageProcessor.post_process_depth_estimations ( $(((!1  $3+?+?3|CTCT+T+To 8D8LvO 4 444R^ "%o|"D"D 7 7 E;&+77OOA&&0033+Iej8')) NN-u5 6 6 6 6r))Tr+F)r+r)%r?r@rAr BICUBICrLr rir rjrKrcrfrhrkrgr<rrerdr>r; valid_kwargsr rErCrQralistfloatrrzrtupledictstrrrFr)r'rIrIes~!)H'J%IC ( (DIJL FNIN*L)*"'&k&k&k&kL &k  &k  $J &k &k &k&k&k&kV''''  ''''88 ^$8 8  8  8 L 8 8 8 8 8 8 DK'$.8 4;&-8  8  $J8 !8 "Dj#8 $+%8 ( )8 8 8 8 zJN''''!4c3h#884?$F' d3 ?# $ ''''''r)rI)%r collections.abcrtypingrr~image_processing_backendsrimage_processing_baserimage_transformsrr image_utilsr r r r processing_utilsrutilsrrrbeit.image_processing_beitrmodeling_outputsrtorchvision.transforms.v2rrZrCrEr9r;rI__all__rFr)r'rs  $$$$$$ ;;;;;;111111EEEEEEEE -,,,,,BBBBBBBBBB;;;;;;9888888777777$8$8x}$$8$8 $8  $8$8$8$8Nl%(yyyyy*yyyx  r)