qi( ddlZddlmZddlmZmZddlZddlmZddl m Z ddl m Z m Z ddlmZmZmZmZdd lmZmZmZd d lmZd d lmZerddlmZddlmcmcm Z!ddde"ee"zde#de"def dZ$eGddeZ%dgZ&y)N)Iterable) TYPE_CHECKINGOptional) BatchFeature)BaseImageProcessorFast)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDPILImageResamplingSizeDict) TensorTypeauto_docstringrequires_backends)BeitImageProcessorFast)DPTImageProcessorKwargs)DepthEstimatorOutput input_image torch.Tensor output_sizekeep_aspect_ratiomultiplereturncdd}|jdd\}}|\}}||z } ||z } |r"td| z td| z kr| } n| } || |z|} || |z|} t| | S)Nct||z |z}| ||kDrtj||z |z}||krtj||z |z}|SN)roundmathfloorceil)valrmin_valmax_valxs U/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/dpt/modular_dpt.pyconstrain_to_multiple_ofz>get_resize_output_image_size..constrain_to_multiple_of4s[ #. !H ,  1w; 3>*X5A w; #.)H4Ar)rheightwidth)rN)shapeabsr) rrrrr) input_height input_width output_height output_width scale_height scale_width new_height new_widths r(get_resize_output_image_sizer9.s !, 1 1"# 6L+"-M<!</L,K q; #a,&6"7 7&L'K),*EPXYJ({)BXVI :Y 77r*c&eZdZejZeZeZ dddZ dZ dZ dZ dZdZdZdZdZdZdZeZ d&dd d ed ed d ededzdedd fdZ d'dd dedd fdZded deded ed ed dedededededeeezdzdeeezdzdededzdededzdedzd ee zdzde!f&d!Z" d(d"d#d$e ee#eefzdzdzdee$ee ffd%Z%y))DPTImageProcessorFastir,TFgp?rNimagersize interpolationztvF.InterpolationMode antialiasensure_multiple_ofrrc|jr |jstd|jt ||j|jf||}t j |||||S)a< Resize an image to `(size["height"], size["width"])`. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`. antialias (`bool`, *optional*, defaults to `True`): Whether to use antialiasing when resizing the image ensure_multiple_of (`int`, *optional*): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, and `do_resize` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Returns: `torch.Tensor`: The resized image. zDThe size dictionary must contain the keys 'height' and 'width'. Got )rrr)r>r?)r-r. ValueErrorkeysr9rresize)selfr<r=r>r?r@rrs r(rDzDPTImageProcessorFast.resizehsr:{{$**cdhdmdmdocpqr r2 djj1/'  &,, %MY  r* size_divisorc|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#)r=rFnew_sizepad_size pad_size_leftpad_size_rights r(_get_padz1DPTImageProcessorFast.pad_image.._get_padsAyy !45 DH$H$MM% 5N .0 0r*)r/tvFpad) rEr<rFr-r.rMpad_top pad_bottompad_left pad_rightpaddings r( pad_imagezDPTImageProcessorFast.pad_images` BC(  1'v|<&ul;)Wi<wwug&&r*imagesdo_reduce_labels do_resizedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stddo_paddisable_groupingreturn_tensorsc |r|j|}t||\}}i}|jD]"\}}|r|j||||| }|||<$t ||}t||\}}i}|jD]H\}}|r|j ||}|r|j ||}|j||| | | | }|||<Jt ||}td|i|S)N)ra)r<r=r>r@r pixel_values)data tensor_type) reduce_labelr itemsrDr center_croprUrescale_and_normalizer)rErVrWrXr=r>rYrZr[r\r]r^r_rr@r`rFrarbkwargsgrouped_imagesgrouped_images_indexresized_images_groupedr/stacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess r( _preprocessz!DPTImageProcessorFast._preprocesssC, &&v.F0EV^n/o,,!#%3%9%9%; ; !E>!%("/'9&7 "-"-; "5 ) ;((>@TU0E^fv/w,,#% %3%9%9%; = !E>!%!1!1.)!L!% !M!77 NL*V_N/= $U + =**BDXY.2B!CQ_``r*outputsr target_sizesct|d|j}|"t|t|k7r tdg}|dgt|zn|}t ||D]s\}}|Yt j jj|jdjd|ddj}|jd |iu|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 depthrrbicubicF)r=mode align_cornerspredicted_depth) rr{lenrBziprwnn functional interpolate unsqueezesqueezeappend)rErtrur{resultsdepth target_sizes r(post_process_depth_estimationz3DPTImageProcessorFast.post_process_depth_estimations( $(!11  $3+?3|CT+To 8D8LvO 44R^ "%o|"D 7 E;&++77OOA&003+Iej8') NN-u5 6  7r*)NTrF)rr)&__name__ __module__ __qualname__r BICUBICresampler r^r r_r=rXr[r]r`r\r@rrZrYrWr valid_kwargsrrboolintrDrUlistfloatstrrrrstupledictrr*r(r;r;UsI!))H'J%IC (DIJL FNIN*L <@)*"'( ( (  78 (  (  $J (  (  ( Z'''  '88a^$8a8a 8a  8a  78 8a8a8a8a8a8aDK'$.8a4;&-8a 8a $J8a !8a"Dj#8a$+%8a&j(4/'8a* +8azJN'''!4c3h#884?$F' d3 ?# $ 'r*r;)'r!collections.abcrtypingrrrwimage_processing_baserimage_processing_utils_fastrimage_transformsr r image_utilsr r r rutilsrrrbeit.image_processing_beit_fastrimage_processing_dptrmodeling_outputsr$torchvision.transforms.v2.functional transformsv2rrNrrr9r;__all__rr*r(rs  $* 1AE  E9822$8$8x}$$8$8 $8  $8Ny2yyx # #r*