`i. <ddlZddlmZddlmZmZmZddlZddlm Z ddl m Z m Z ddl mZmZddlmZmZmZmZdd lmZmZmZd d lmZerdd lmZdd lmZdddee ee fde!de def dZ"Gdde Z#eGddeZ$dgZ%dS)N)Iterable) TYPE_CHECKINGOptionalUnion) BatchFeature)BaseImageProcessorFastDefaultFastImageProcessorKwargs)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDPILImageResamplingSizeDict) TensorTypeauto_docstringrequires_backends)BeitImageProcessorFast)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 w/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/dpt/modular_dpt.pyconstrain_to_multiple_ofz>get_resize_output_image_size..constrain_to_multiple_of4sd #. ! !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_sizer;.s    !, 1"## 6L+"-M<!</L,K' q;  #a,&6"7"7 7 7&LL'K)),*EPXYYYJ(({)BXVVVI :Y 7 7 77r+cleZdZUdZeeed<eeed<eeed<eeed<dS)DPTFastImageProcessorKwargsa 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`. size_divisor (`int`, *optional*): If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the DINOv2 paper, which uses the model in combination with DPT. 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__rint__annotations__boolr+r)r=r=Us^ ! %%%3-~%%%tn$$$$$r+r=c'&eZdZejZeZeZ dddZ dZ dZ dZ dZdZdZdZdZdZdZdZeZ d&dd d ed ed d edeededd fdZ d'dd dedd fdZded deded ed ed dedededededeeeeefdeeeeefdedeededeedeed eee e!fde"f&d!Z# d(d"d#d$eee!ee$eefdfdee%e e!ffd%Z&dS))DPTImageProcessorFastir.TFgp?r-Nimagersize interpolationzF.InterpolationMode 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)rMrN)r/r0 ValueErrorkeysr;r resize)selfrKrLrMrNr>rrs r)rRzDPTImageProcessorFast.resizes:{ s$* sqdhdmdmdodoqqrr r2 dj1/'    &, %MY    r+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$)rLr?new_sizepad_size pad_size_leftpad_size_rights r)_get_padz1DPTImageProcessorFast.pad_image.._get_padsBy !455 DH$H$MM% 5N .0 0r+)r1Fpad) rSrKr?r/r0rZpad_top pad_bottompad_left pad_rightpaddings r) pad_imagezDPTImageProcessorFast.pad_imagesu BCC(  1 1 1'hv|<<&hul;;)Wi<uUG$$$r+imagesr@ do_resizedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stddo_paddisable_groupingreturn_tensorsc h|r||}t||\}}i}|D]&\}}|r|||||| }|||<'t ||}t||\}}i}|D]T\}}|r|||}|r|||}|||| | | | }|||<Ut ||}|rtj |dn|}td|iS)N)rm)rKrLrMr>rr)dim pixel_values)data) reduce_labelr itemsrRr center_croprbrescale_and_normalizetorchstackr)rSrcr@rdrLrMrerfrgrhrirjrkrr>rlr?rmrnkwargsgrouped_imagesgrouped_images_indexresized_images_groupedr1stacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess r) _preprocessz!DPTImageProcessorFast._preprocesss,  /&&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 N!% !M!M!77 NL*V_N/= $U + +)*BDXYYCQg5;'7Q????Wg.2B!CDDDDr+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. rwNz]Make sure that you pass in as many target sizes as the batch dimension of the predicted depthrr-bicubicF)rLmode align_cornerspredicted_depth) rrlenrPziprwnnr interpolate unsqueezesqueezeappend)rSrrrresultsdepth target_sizes r)post_process_depth_estimationz3DPTImageProcessorFast.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+)NTr-F)r-r )'rArBrCrBICUBICresampler rjrrkrLrdrgrirlrhr>rr@rfrer= valid_kwargsrrrGrErRrblistfloatrstrrrrtupledictrrHr+r)rJrJls!)H'J%IC ( (DIJL FNIN.L :>,-"'( ( ( (  56 (  ( %SM (  (  ( ( ( ( Z%%%%  %%%%89E^$9E9E 9E  9E  56 9E9E9E9E9E9EU5$u+#5679EE%e"4569E 9E%SM9E !9E"sm#9E$#4.%9E&!sJ!78'9E* +9E9E9E9E|RV''''uZeCHo1F%LMN' d3 ?# $ ''''''r+rJ)&r"collections.abcrtypingrrrrwimage_processing_baserimage_processing_utils_fastr r image_transformsr r image_utilsr rrrutilsrrrbeit.image_processing_beit_fastrmodeling_outputsrtorchvision.transforms.v2rr[rErGr;r=rJ__all__rHr+r)rs" $$$$$$1111111111 111111bbbbbbbbEEEEEEEE  EDDDDD9888888555555$8$8sHSM)*$8$8 $8  $8$8$8$8N%%%%%"A%%%.{{{{{2{{{| # #r+