|-jZ?e5dGdde Z@e5dGdde ZAe@ZBdS))Iterable) lru_cache)AnyOptionalUnionN) BatchFeature)BaseImageProcessor) center_crop)convert_to_rgbdivide_to_patchesget_resize_output_image_sizeget_size_with_aspect_ratiogroup_images_by_shapereorder_images) normalize)rescale)resize) ChannelDimension ImageInput ImageTypeSizeDictget_image_size#get_image_size_for_max_height_widthget_image_typeget_max_height_widthinfer_channel_dimension_formatis_valid_imageload_image_as_tensor) ImagesKwargsUnpack) TensorTypeis_torch_availableis_torchvision_availableis_vision_availablelogging)is_rocm_platformis_torchdynamo_compilingrequires)PILImageResampling) functional)pil_torch_interpolation_mappingtorch_pil_interpolation_mapping)torch torchvision)backendsc eZdZdZdeeffd ZedefdZ ede fdZ de e e ze e e zfdZ d=d ed ed zd e ezd zd eddeeddf dZd edefdZ d>de ddeded zde d zdeded zded zdeeddffdZ d?d dded d!d"eddf d#Ze d?d dd$eeefd%ed&d"eddf d'Zd dd(eddfd)Zd dd*eeezd+eeezddfd,Zed-. d@d/ed zd0ee ezd zd1ee ezd zd2ed zd3ed zd eddefd4Z ddd2ed3ed/ed0ee ezd1ee ezddfd5Z!d ddeddfd6Z"de dd7eded d!d8ed9ed2ed3ed/ed0ee ezd zd1ee ezd zd:ed zded zded zd;e e#zd zde$f d<Z%xZ&S)ATorchvisionBackendzATorchvision backend for GPU-accelerated batched image processing.kwargsc Ttjdi||jdi|dSNsuper__init___set_attributesselfr3 __class__s f/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/transformers/image_processing_backends.pyr9zTorchvisionBackend.__init__Y?""6"""&&v&&&&&returnc:tddS) `bool`: Whether or not this image processor is using the fast (Torchvision) backend. The `is_fast` property is deprecated and will be removed in v5.3 of Transformers. Use the `backend` attribute instead (e.g., `processor.backend == "torchvision"`). The `is_fast` property is deprecated and will be removed in v5.3 of Transformers. Use the `backend` attribute instead (e.g., `processor.backend == 'torchvision'`).Tlogger warning_oncer<s r>is_fastzTorchvisionBackend.is_fast]s)  `   tr@cdS)B `str`: The backend used by this image processor. r/r6rHs r>backendzTorchvisionBackend.backendjs }r@image_url_or_urlsct|ttfrfd|DSt|trt |St |r|St dt|)z Convert a single or a list of URLs / paths into `torch.Tensor` objects. Already-valid image objects (tensors, numpy arrays, PIL Images) are passed through unchanged so that callers who pre-load images are unaffected. c:g|]}|Sr6) fetch_images).0xr<s r> z3TorchvisionBackend.fetch_images..ys'DDDQD%%a((DDDr@z=only a single or a list of entries is supported but got type=) isinstancelisttuplestrrr TypeErrortype)r<rMs` r>rPzTorchvisionBackend.fetch_imagesqs '$ 7 7 wDDDD2CDDD D )3 / / w'(9:: : - . . w$ $u\`ar\s\suuvv vr@Nimagedo_convert_rgbinput_data_formatdevicez torch.device torch.Tensorc xt|}|tjtjtjfvrt d||r||}|tjkrtj|}n6|tjkr&tj | }|j dkr| d}|t|}|tjkr)|ddd }|||}|S)z/Process a single image for torchvision backend.Unsupported input image type rNr)rrPILTORCHNUMPY ValueErrorr tvF pil_to_tensorr. from_numpy contiguousndim unsqueezerrLASTpermuteto)r<rZr[r\r]r3 image_types r> process_imagez TorchvisionBackend.process_images$E** imY_ioN N NIZIIJJ J  /''..E  & &%e,,EE 9? * *$U++6688E :??OOA&&E  $ >u E E   0 5 5 5MM!Q**5577E  HHV$$E r@c t|SzConvert an image to RGB format.r r<rZs r>r z!TorchvisionBackend.convert_to_rgbe$$$r@rconstantFimagespad_size fill_value padding_mode return_maskdisable_grouping is_nested)r^r^c |0|jr|jstd|d|j|jf}nt|}t |||\} } i} i} | D]\} }|jdd}|d|dz }|d|dz }|dks|dkrtd|d |d||krdd||f}tj|||| }|| | <|rKtj |tj d dddddf}d|d d|dd|df<|| | <t| | | }|rt| | | }||fS|S)z5Pad images using Torchvision with batched operations.NCPad size must contain 'height' and 'width' keys only. Got pad_size=.)r|r}rrzrPadding dimensions are negative. Please make sure that the `pad_size` is larger than the image size. Got pad_size=z , image_size=)fillrzdtype.)r}) heightwidthrerritemsshaperfpadr. zeros_likeint64r)r<rwrxryrzr{r|r}r3grouped_imagesgrouped_images_indexprocessed_images_groupedprocessed_masks_groupedrstacked_images image_sizepadding_height padding_widthpadding stacked_masksprocessed_imagesprocessed_maskss r>rzTorchvisionBackend.pads  O t t !rgo!r!r!rsss 8HH+F33H/D %50 0 0 ,,$& "$%3%9%9%;%; ? ? !E>'-bcc2J%a[:a=8N$QK*Q-7M!!]Q%6%6 U08UUGQUUUX%%a?!$z`l!m!m!m.< $U + ? % 0u{ S S STWYZ\]\]\]_`_`_`T` a GH c?Z]?OjmOCD1>'.)*BDXdmnnn  5,-DFZfopppO#_4 4r@Tsizeresamplez7PILImageResampling | tvF.InterpolationMode | int | None antialiasc ^|-t|ttfrt|}n|}ntjj}|tjjkr+t dtjj }|j r=|j r6t|dd|j |j }n|j r#t||j dt j}nt|jr=|jr6t)|dd|j|j}n0|jr|jr|j|jf}nt/d|dt1r&t3r|||||St j||||S) z"Resize an image using Torchvision.Na You have used a torchvision backend image processor with LANCZOS resample which not yet supported for torch.Tensor. BICUBIC resample will be used as an alternative. Please fall back to a pil backend image processor if you want full consistency with the original model.rFrdefault_to_squarer\jSize must contain 'height' and 'width' keys, or 'max_height' and 'max_width', or 'shortest_edge' key. Got r interpolationr)rTr*intr,rfInterpolationModeBILINEARLANCZOSrFrGBICUBIC shortest_edge longest_edgerrrrFIRST max_height max_widthrrrrer(r'_compile_friendly_resizer)r<rZrrrr3rnew_sizes r>rzTorchvisionBackend.resizes  (%7$=>> ) ? I ( 1:M C19 9 9   A     19M   $"3 1 RSS!"!HH   3'"'"2"8 HH _  :5::<<;Ldo_c_mnnHH [ TZ  TZ0HH  $ % % \*:*<*< \00-QZ[[ [z%R[\\\\r@rrztvF.InterpolationModec|jtjkr|dz }t j||||}|dz}tj|dkd|}tj|dkd|}|tj}nt j||||}|S)zOA wrapper around tvF.resize for torch.compile compatibility with uint8 tensors.rr) rr.uint8floatrfrwhereroundrn)rZrrrs r>rz+TorchvisionBackend._compile_friendly_resizes ;%+ % %KKMMC'EJuhmW`aaaECKEK S%88EK 1e44EKKMM$$U[11EEJuhmW`aaaE r@scalec ||zS)z5Rescale an image by a scale factor using Torchvision.r6r<rZrr3s r>rzTorchvisionBackend.rescale"su}r@meanstdc .tj|||S)z%Normalize an image using Torchvision.)rfrr<rZrrr3s r>rzTorchvisionBackend.normalize+s}UD#...r@ )maxsize do_normalize image_mean image_std do_rescalerescale_factorc|r<|r:tj||d|z z}tj||d|z z}d}|||fS)N)r]g?F)r.tensor)r<rrrrrr]s r>!_fuse_mean_std_and_rescale_factorz4TorchvisionBackend._fuse_mean_std_and_rescale_factor5sa  , j@@@C.DXYJ Yv>>>#BVWIJ9j00r@c|||||||j\}}}|r6||tj||}n|r|||}|S)zFRescale and normalize images using Torchvision (fused for efficiency).)rrrrrr]r)rr]rrnr.float32r)r<rwrrrrrs r>rescale_and_normalizez(TorchvisionBackend.rescale_and_normalizeFs-1,R,R%!!)= -S- - ) Iz  :^^FIIEMI$B$BJPYZZFF  :\\&.99F r@c 8|j|j$td||jdd\}}|j|j}}||ks||kr{||kr||z dznd||kr||z dznd||kr ||z dzdznd||kr ||z dzdzndg}t j||d}|jdd\}}||kr||kr|St||z dz } t||z dz } t j|| | ||S) z'Center crop an image using Torchvision.N=The size dictionary must have keys 'height' and 'width'. Got rrarr)rg@) rrrekeysrrfrrcrop) r<rZrr3 image_height image_width crop_height crop_width padding_ltrbcrop_top crop_lefts r>r zTorchvisionBackend.center_crop_s ; $*"4j]a]f]f]h]hjjkk k$)K$4! k"&+tzZ  # #{\'A'A3= 3K3Kk)a//QR5@<5O5O|+11UV7AK7O7Ok)A-!33UV9D|9S9S|+a/A55YZ L GE _preprocesszTorchvisionBackend._preprocess{sf*0EV^n/o/o/o,,!#%3%9%9%;%; ; ; !E> a!%>W_!`!`,: "5 ) )'(>@TUU0E^fv/w/w/w,,#% %3%9%9%;%; = = !E> M!%!1!1.)!L!L!77 NL*V_N/= $U + +)*BDXYY  p#xx(88^nxoo .2B!CQ_````r@)NNN)NrrvFFF)NT)NNNNNN)'__name__ __module__ __qualname____doc__r!r r9propertyboolrIrWrLrUrPrrrrpr rrrrVrr staticmethodrrrrrrrrr r"r r __classcell__r=s@r>r2r2UsKK' !5''''''    X X wcDIoT#Y.Owwww&'+;?+/ !!!t !!11D8 ! ( ! & ! !!!!F%J%:%%%%"!"#-!(-!&0 0 ^$0 0 $J 0 Dj 0  0 +0 $;0  u34nD E0 0 0 0 lOS 3]3]3]3]L 3]  3] 3]3]3]3]j<@ S/ 78   \$  //huo%/Xe_ $ /  ////Yr%)1504"&'++/11Tk1DK'$.14;&- 1 4K 1  1(1 1111     DK' 4;& 2MMM  MMMM8-a^$-a-a -a L -a  -a-a-a-a-aDK'$.-a4;&--at -aT/-a+-a j(4/!-a$ %-a-a-a-a-a-a-a-ar@r2)visionceZdZdZdeeffd ZedefdZ ede fdZ d-de d edzd e e zdzdeedejf d Zde de fd Z d.deejdededzde dzdedeeejeejfeejzf dZ d-dejdedddedzdejf dZdejdedejfdZdejdeeezdeeezdejfdZdejdedejfd Zdeejd!ededdd"ed#ed$ed%ed&ed'eeezdzd(eeezdzd)edzdedzd*e ezdzdefd+Zde e e!fffd, Z"xZ#S)/ PilBackendz9PIL/NumPy backend for portable CPU-only image processing.r3c Ttjdi||jdi|dSr5r7r;s r>r9zPilBackend.__init__r?r@rAc:tddS)rCrDFrErHs r>rIzPilBackend.is_fasts)  `   ur@cdS)rKpilr6rHs r>rLzPilBackend.backends ur@NrZr[r\c jt|}|tjtjtjfvrt d||r||}|tjkr0tj|}|j dkr| tj n|}n$|tjkr| }|j dkrtj |d}|t|}|tj kr/t|tjrtj|d}|S)z'Process a single image for PIL backend.r`Nrar)axis)rarr)rrrbrcrdrer nparrayrjrrlnumpy expand_dimsrrTndarray transpose)r<rZr[r\r3ros r>rpzPilBackend.process_images$E** imY_ioN N NIZIIJJ J  /''..E  & &HUOOEzQ=N=V$4$9$9\m! 9? * *KKMME :??N5q111E  $ >u E E   0 5 5 5%,, 7 UI66 r@c t|Srrrsrts r>r zPilBackend.convert_to_rgbrur@rrvFrwrxryrzr{c |0|jr|jstd|d|j|j}}nt|\}}g} g} |D]} t | t j\} } || z }|| z }|dks|dkrtd|d|d| d| d | |ks| |kr?d d|fd|ff}|d krtj| |d | } ntj| || } | | |rBtj ||ftj }d|d| d| f<| ||r| | fS| S)z)Pad images to specified size using NumPy.Nrr channel_dimrzsPadding dimensions are negative. Please make sure that the `pad_size` is larger than the image size. Got pad_size=(z, z), image_size=(z).)rrrv)modeconstant_values)rrr) rrrerrrrrrappendzerosr)r<rwrxryrzr{r3 target_height target_widthrrrZrrrr pad_widthmasks r>rzPilBackend.pads  O t t !rgo!r!r!rsss*2/8>v*F*F 'M< - -E*5>N>TUUUMFE*V3N(50M!!]Q%6%6 s1>ssBNss_essinsss &&%<*?*?$a%81m:LM :--F5)*V`aaaEEF5),GGGE  # #E * * * -x =RXNNN()WfWfuf_%&&t,,,  5#_4 4r@rrzPILImageResampling | None reducing_gapc |Ft|ttfs*t|tvrt|}n tj}||n tj}|jrC|jrrzPilBackend.resize"s   8>PRU=V(W(W .:xKj?j?j:8D-6'3889K9T   $"3 *5>N>TUUUMFE1"!HH   3'"'"2"8 HH _  *5>N>TUUUMFE:FE?DO]a]kllHH [ TZ  TZ0HH   %(..4     r@rc Pt||tjtjS)z/Rescale an image by a scale factor using NumPy.)rrr\) np_rescalerrrs r>rzPilBackend.rescaleUs- (..4     r@rrc Rt|||tjtjS)zNormalize an image using NumPy.)rrrr\) np_normalizerrrs r>rzPilBackend.normalizecs0 (..4     r@c |j|j$td|t ||j|jft jt jS)z!Center crop an image using NumPy.Nr)rrr\)rrrernp_center_croprr)r<rZrr3s r>r zPilBackend.center_cropssi ; $*"4j]a]f]f]h]hjjkk k +tz*(..4     r@rrrrrrrrrrc Xg}|D]z}|r||||}|r|||}|r|||}| r||| | }||{| r||| }t d|i|S)z2Preprocess using PIL backend (portable, CPU-only).r)rxrr)rr rrrrr )r<rwrrrrrrrrrrrrxrr3rrZs r>rzPilBackend._preprocesss& + +E O %dX NN ;(( :: < UN;; Euj)DD  # #E * * * *  M#xx(88xLL .2B!CQ_````r@ct}|dddr|ddd|d<|S)Nimage_processor_typePil)r8to_dictgetendswith)r<processor_dictr=s r>rzPilBackend.to_dictsa**   4b 9 9 B B5 I I a5CDZ5[\_]_\_5`N1 2r@)NN)NrrvF)$rrrrr!r r9rrrIrWrLrrrrrpr rUrrrVrrrrrrr r"r rdictrrrrs@r>rrsCC' !5''''''    X X'+;? """t "!11D8 " & "  """"H%J%:%%%%"!"#-! 1 1 RZ 1 1 $J 1 Dj 1  1  tBJbj!11 2T"*5E E1 1 1 1 n15#' 1 1 z1 1 . 1 Dj 1  1 1 1 1 f  z          z huo% Xe_ $        z       ""aRZ "a"a "a . "a  "a"a"a"a"aDK'$."a4;&-"at "aT/"aj(4/"a" #"a"a"a"aHc3hr@r)Ccollections.abcr functoolsrtypingrrrrrimage_processing_baser image_processing_utilsr image_transformsr rr r rrrrrr rr rr  image_utilsrrrrrrrrrrrprocessing_utilsr r!utilsr"r#r$r%r&utils.import_utilsr'r(r)r*r.torchvision.transforms.v2r+rfr,r- get_loggerrrFr2rBaseImageProcessorFastr6r@r>r(s%$$$$$''''''''''//////666666                          32222222UTTTTTTTTT0//////LLL+;;;;;;]]]]]]]]]&*#&*#  H % % +,,,RaRaRaRaRa+RaRa-,Raj  ;AAAAA#AA AJ,r@