+ js^RIt^RIt^RIt^RIHt^RIHt^RIHt^RI t ^RI H t ^RI Ht^RIHt^RIHt^R IHt^R IHtHtHtHt^R IHtHt^R IHtHtH t H!t!H"t"H#t#H$t$H%t%H&t&H't'H(t(^R I)H*t*H+t+^RI,H-t-^RI.H/t/H0t0H1t1H2t2H3t3H4t4H5t5H6t6H7t7H8t8H9t9]$!4'd^RI:t:]&!4'd ^RI;Ht?]!4'd^RIH@t@]'P!]B4tC]-!RR7!RR]44tD]#!]DP4]DnE]DPPe:]DPPPRRRR7]DPnFR#R#)N)Callable)partial)Any)is_offline_mode)validate_typed_dict)custom_object_save)TorchvisionBackend BatchFeature)ChannelDimensionSizeDictis_vision_availablevalidate_kwargsUnpack VideosKwargs) IMAGE_PROCESSOR_NAMEPROCESSOR_NAMEVIDEO_PROCESSOR_NAME TensorTypeauto_docstring copy_funcis_torch_availableis_torchcodec_availableis_torchvision_v2_availableloggingsafe_load_json_file) cached_filehf_api)requires) TORCHVISION_VIDEO_DECODING_ERROR VideoInput VideoMetadatagroup_videos_by_shapeinfer_channel_dimension_format'is_torchvision_video_decoding_availableis_valid_video load_videomake_batched_metadatamake_batched_videosreorder_videos)PILImageResampling)backendsc"aa]tRt^OtoRtRtRtRtRtRt Rt Rt Rt Rt RtR*tRtRtRtRtRtRtRt]tR.tV3RlV3RlltV3RlRltV3R lR ltR+V3R lR lltR+V3R lRlltR+V3RlRllt] V3RlRl4t!R,V3RlRllt"]#R-V3RlRll4t$R.V3RlRllt%]#V3RlRl4t&]#V3RlRl4t'V3RlV3Rllt(V3RlR lt)V3R!lR"lt*R#t+]#V3R$lR%l4t,]#R/R&l4t-R,V3R'lR(llt.R)t/Vt0V;t1#)0BaseVideoProcessorNTFpixel_values_videosc4<V^8dQhRS[S[,RR/#)kwargsreturnNr)format __classdict__s"d/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/video_processing_utils.py __annotate__BaseVideoProcessor.__annotate__hs ## !5#$#c (<\SV`!R/VBR#)N)super__init__)selfr3 __class__s&,r7r>BaseVideoProcessor.__init__hs "6"r:c <V^8dQhRS[/#r2r4r )r5r6s"r7r8r9ks11L1r:c (VP!V3/VB#N) preprocess)r?videosr3s&&,r7__call__BaseVideoProcessor.__call__ksv000r:c$<V^8dQhRRRS[/#)r2video torch.Tensorr4)r")r5r6s"r7r8r9ns r:c :\P!V4pVPR,^8Xg!VR,^8P4'g VR,#VR,R, p^VR,, ^,VR,VR,,,pV#)z Converts a video to RGB format. Args: video (`"torch.Tensor"`): The video to convert. Returns: `torch.Tensor`: The converted video. go@).NNNrP).:NrONrPrP).NrPrP)tvFgrayscale_to_rgbshapeany)r?rKalphas&& r7convert_to_rgb!BaseVideoProcessor.convert_to_rgbns$$U+ ;;r?a l(;c(A'F'F'H'H' 'l#e+U?++s2U?5KeTaNb5bb r:cX<V^8dQhRS[RS[R,RS[S[,R,/#)r2metadata num_framesNfps)r#intfloat)r5r6s"r7r8r9s6333$J35[4  3r:c VeVe \R4hVeTM VPpVeTM VPpVPpVfDVe@VeVPf \R4h\ WQP, V,4pW%8d\RV RV R24hVe/\ P !^WUV, 4P 4pV#\ P !^V4P 4pV#)a Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames. If `fps` is passed along with metadata, `fps` frames per second are sampled uniformly. Arguments `num_frames` and `fps` are mutually exclusive. Args: metadata (`VideoMetadata`): Metadata of the video containing information about total duration, fps and total number of frames. num_frames (`int`, *optional*): Maximum number of frames to sample. Defaults to `self.num_frames`. fps (`int` or `float`, *optional*): Target frames to sample per second. Defaults to `self.fps`. Returns: np.ndarray: Indices to sample video frames. zc`num_frames`, `fps`, and `sample_indices_fn` are mutually exclusive arguments, please use only one!zAsked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Please pass in `VideoMetadata` object or use a fixed `num_frames` per input videoz(Video can't be sampled. The `num_frames=z` exceeds `total_num_frames=z`. ) ValueErrorrZr[total_num_framesr\torcharange)r?rYrZr[r3r`indicess&&&&, r7 sample_frames BaseVideoProcessor.sample_framess 0 ?z5u $.#9Zt _c$((#44  #/8<<#7 h-  TpTpW3#\V^,4'g\ V^,\4'dnVPV4U U u.uF9p \P!V U u.uFqPV 4NK up ^R7NK; pp p V'd \R4hW3#VPWR7wrW3#uup iuup p i)z2 Decode input videos and sample frames if needed. )rg)rY)dimzUSampling frames from a list of images is not supported! Set `do_sample_frames=False`.ri)r*r)r'zipframes_indicesappend isinstancerl fetch_imagesrastack process_imager_ fetch_videos) r?rGrgrhrisampled_videossampled_metadatarKrYrcimagesimages &&&&& r7_decode_and_sample_videos,BaseVideoProcessor._decode_and_sample_videossQ%V,.vU &) $ $)9N! #&v#>+X>*1'%%en5 ''1 $? $F-N%% q **&)T**#'"3"3F";";KK Ou!3!3E!: OUVW";$$o$%%*.):):6):)g&%%!PsE 0E E E c l<V^8dQhRS[RS[S[,R,RS[R,RS[R,/#)r2rGinput_data_formatNdevicer4rL)r"strr rl)r5r6s"r7r8r9sG   !11D8 d  n   r:c |.pVFp\V\P4'd%\P!V4P 4pVf \ V4pV\P8Xd#VP^^^^4P 4pVeVPV4pVPV4K V#)z* Prepare the input videos for processing. ) rsnpndarrayra from_numpy contiguousr%r LASTpermutetorr)r?rGrrprocessed_videosrKs&&&& r7_prepare_input_videos(BaseVideoProcessor._prepare_input_videossE%,,((/::<!($B5$I! $4$9$99 aAq1<<>!(  # #E *!" r:c<<V^8dQhRS[RS[S[,RS[/#)r2rGr3r4)r"rrr )r5r6s"r7r8r9s.*#*#*#&*#  *#r:c (\VP4\VPPP44R.,R7\ VPV4VPPFpVP V\WR44K! VPR4pVPR4pVPR4pVPR4pV'd\VP3/VBMRpVPVVVVR7wrVPWVR7pVP!R /VBpVP!R /VBVPR 4VPR 4p VP!R R V/VBp V 'dWzR&V #) return_tensors)captured_kwargsvalid_processor_keysNrrhrrg)rgrhri)rGrr data_formatreturn_metadatarGr<)rkeysrl valid_kwargs__annotations__r setdefaultgetattrpoprrdr|r_standardize_kwargs_validate_preprocess_kwargs _preprocess) r?rGr3 kwarg_namerrhrrgrirpreprocessed_videoss &&, r7rFBaseVideoProcessor.preprocesss "KKM!%d&7&7&G&G&L&L&N!OScRd!d D--v6++;;J   j'$D*I J<#JJ':;!::&89H%$45EUGD$6$6A&A[_!%!?!? )-/ "@"  ++6gm+n))3F3 ((262  =! **%67"..GfGG 4B 0 1""r:c<V^8dQhRS[R,RS[RS[RS[RRRS[R S[R S[R S[R S[R S[S[S[,,R,RS[S[S[,,R,RS[S[,R,RS[/#)r2rGrLdo_convert_rgb do_resizesizeresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonedo_center_crop crop_size do_rescalerescale_factor do_normalize image_meanN image_stdrr4)rlrkr r]rrr )r5r6s"r7r8r9.s+h+h^$+h+h +h  +h L +h+h+h+h+h+hDK'$.+h4;&-+hj(4/+h !+hr:c \V4wpp/pVP4F?wppV'dVPV4pV'dVPVWER7pVVV&KA \ VV4p\V4wpp/pVP4F9wppV'dVP VV4pVP VWWV 4pVVV&K; \ VV4p\RV/V R7#))rrr0)data tensor_type)r$itemsrVresizer+ center_croprescale_and_normalizer )r?rGrrrrrrrrrrrrr3grouped_videosgrouped_videos_indexresized_videos_groupedrSstacked_videosresized_videosprocessed_videos_groupedrs&&&&&&&&&&&&&&, r7rBaseVideoProcessor._preprocess.s $0EV/L,,!#%3%9%9%; !E>!%!4!4^!D!%^$!Z,: "5 ) &< ((>@TU0E^/T,,#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N/= $U +&<**BDXY"79I!JXfggr:c <V^8dQhRS[S[P,RS[S[P,R,RS[RS[RS[S[,R,RS[/#)r2pretrained_model_name_or_path cache_dirNforce_downloadlocal_files_onlytokenrevisionrosPathLikerk)r5r6s"r7r8r9\so`=`='*R[['8`=$t+`= `=  `= TzD `=`=r:c W'R&W7R&WGR&WgR&VeWWR&VP!V3/VBwrVP!V3/VB#)aC Instantiate a type of [`~video_processing_utils.VideoProcessorBase`] from an video processor. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained video hosted inside a model repo on huggingface.co. - a path to a *directory* containing a video processor file saved using the [`~video_processing_utils.VideoProcessorBase.save_pretrained`] method, e.g., `./my_model_directory/`. - a path to a saved video processor JSON *file*, e.g., `./my_model_directory/video_preprocessor_config.json`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model video processor should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the video processor files and override the cached versions if they exist. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `hf auth login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`. return_unused_kwargs (`bool`, *optional*, defaults to `False`): If `False`, then this function returns just the final video processor object. If `True`, then this functions returns a `Tuple(video_processor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not video processor attributes: i.e., the part of `kwargs` which has not been used to update `video_processor` and is otherwise ignored. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. kwargs (`dict[str, Any]`, *optional*): The values in kwargs of any keys which are video processor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* video processor attributes is controlled by the `return_unused_kwargs` keyword parameter. Returns: A video processor of type [`~video_processing_utils.ImagVideoProcessorBase`]. Examples: ```python # We can't instantiate directly the base class *VideoProcessorBase* so let's show the examples on a # derived class: *LlavaOnevisionVideoProcessor* video_processor = LlavaOnevisionVideoProcessor.from_pretrained( "llava-hf/llava-onevision-qwen2-0.5b-ov-hf" ) # Download video_processing_config from huggingface.co and cache. video_processor = LlavaOnevisionVideoProcessor.from_pretrained( "./test/saved_model/" ) # E.g. video processor (or model) was saved using *save_pretrained('./test/saved_model/')* video_processor = LlavaOnevisionVideoProcessor.from_pretrained("./test/saved_model/video_preprocessor_config.json") video_processor = LlavaOnevisionVideoProcessor.from_pretrained( "llava-hf/llava-onevision-qwen2-0.5b-ov-hf", do_normalize=False, foo=False ) assert video_processor.do_normalize is False video_processor, unused_kwargs = LlavaOnevisionVideoProcessor.from_pretrained( "llava-hf/llava-onevision-qwen2-0.5b-ov-hf", do_normalize=False, foo=False, return_unused_kwargs=True ) assert video_processor.do_normalize is False assert unused_kwargs == {"foo": False} ```rrrrr)get_video_processor_dict from_dict) clsrrrrrrr3video_processor_dicts &&&&&&&, r7from_pretrained"BaseVideoProcessor.from_pretrained[sdn({#1 %5!"%z  #7O'*'C'CDa'lek'l$}}1>. ) ) ?>2BBe!fg g NT2 #ZZ(8$?NjjN,@,@,Mb,QRGh**7LTLVLTTG#99.I     ' tD A')ggll>CW&X# 56 /0K/LMN   ' ' -jj) ( ,,,r:c<V^8dQhRS[S[P,RS[S[S[S[3,S[S[S[3,3,/#)r2rr4)rrrtuplerjr)r5r6s"r7r8r9sD{,{,,/"++,={, tCH~tCH~- .{,r:c VPRR4pVPRR4pVPRR4pVPRR4pVPRR4pVPRR4pVPR R 4p VPR R4p VPR R4p R RRV /p V eWR&\4'd V'g\PR4Rp\ V4p\ P PV4p \ P PV4'dTpRpRp Mi\p\V\VVVVVV VV RR7 pV\3Uu.uF#p\VVVVVVVV VV RR7 ;pfK!VNK% ppV'd V^,MRpRpVe\V4pRV9d VR,pVeVf \V4pVf\RV RV RX R24hV 'd\PRV 24VV3#\PRX RV 24VV3#uupi \dh\d\RT RT RT R24hi;i)a From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a video processor of type [`~video_processing_utils.VideoProcessorBase`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. Returns: `tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the video processor object. rNrFproxiesrrr subfolder_from_pipeline _from_auto file_typevideo processorfrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueT) filenamerrrrr user_agentrr%_raise_exceptions_for_missing_entriesz Can't load video processor for 'z'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'z2' is the correct path to a directory containing a z filevideo_processorzloading configuration file z from cache at )rrrrrrrisdirrrrrrOSError Exceptionr)rrr3rrrrrrr from_pipelinerris_localresolved_video_processor_fileresolved_processor_filevideo_processor_filer resolved_fileresolved_video_processor_filesrprocessor_dicts&&, r7r+BaseVideoProcessor.get_video_processor_dicts$JJ{D1 $4e<**Y- 7D)!::&8%@::j$/JJ{B/  #3T:  **\59!#46GY  $+8' (   %5 KKE F# (+,I(J%77==!>? 77>>7 8 8,I )&* #H#7 2 +61+'#1#%5)%':? +'&:;O$P2$P)49%-&/+9$+-="''1%-&/BG *    "M$P/2*:X215]a.& $ " .01HIN N2'56G'H$ ( 49M9U#67T#U  '23P2QR55R4ST++?*@G   KK56S5TU V $V++ KK-.B-C?SpRqr $V++2.  67T6UV99V8WX//C.DEK s*=$H4!H/H/ H4/H44I  I c6<V^8dQhRS[S[S[3,/#)r2rrjrr)r5r6s"r7r8r9ms&#&#T#s(^&#r:c VP4pVPRR4pTPVP4UUu/uF#wrEW@PP 9gK!WEbK% upp4V!R/VBp.p\ \VP444F_p\Wh4'gKWPP 9gK2\WhVPVR44VPV4Ka V'd'\PRVP RV R24\PRV 24V'dWb3#V#uuppi) a Instantiates a type of [`~video_processing_utils.VideoProcessorBase`] from a Python dictionary of parameters. Args: video_processor_dict (`dict[str, Any]`): Dictionary that will be used to instantiate the video processor object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~video_processing_utils.VideoProcessorBase.to_dict`] method. kwargs (`dict[str, Any]`): Additional parameters from which to initialize the video processor object. Returns: [`~video_processing_utils.VideoProcessorBase`]: The video processor object instantiated from those parameters. return_unused_kwargsFNzImage processor z : kwargs z were applied for backward compatibility. To avoid this warning, add them to valid_kwargs: create a custom TypedDict extending ImagesKwargs with these keys and set it as the `valid_kwargs` class attribute.zVideo processor r<)copyrupdaterrrreversedrlrhasattrsetattrrrr warning_once__name__r) rrr3rkvr extra_keyskeys &&, r7rBaseVideoProcessor.from_dictls-" 488:%zz*@%H##flln$nndaM]M]MmMmHmTQTn$no5 45 D/0C,, This API is experimental and may have some slight breaking changes in the next releases. Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoVideoProcessor "`): The auto class to register this new video processor with. Nz is not a valid auto class.) rsrr transformers.models.automodelsautor r_r)r auto_class auto_modules&& r7register_for_auto_class*BaseVideoProcessor.register_for_auto_classsE *c**#,,J66{// |+FGH H$r:cp<V^8dQhRS[S[S[,,S[S[S[,,,/#)r2video_url_or_urls)rrl)r5r6s"r7r8r9s,ggcDIoT#Y.Ogr:c  LRp\4'g;\4'g\R\ 24h\P !R4Rp\ V\4'd2\\VUu.uFq@PWBR7NK up!4#\WVR7#uupi)z Convert a single or a list of urls into the corresponding `np.array` objects. If a single url is passed, the return value will be a single object. If a list is passed a list of objects is returned. torchcodeczQ`torchcodec` is not installed and cannot be used to decode the video by default. z`torchcodec` is not installed and cannot be used to decode the video by default. Falling back to `torchvision`. Note that `torchvision` decoding is deprecated and will be removed in future versions. torchvisionro)backendri) rr& ImportErrorr!warningswarnrsrlrprwr()r?rIrirMxs&&& r7rwBaseVideoProcessor.fetch_videoss&((:<<!g78: MMI $G ' . .arsar\]///Warstu u/Tef fts1B! r<gp?)NNrE)NFFNmain)F)AutoVideoProcessor)2r  __module__ __qualname____firstlineno__rrrrr size_divisordefault_to_squarerrrrrrrrhr[rZrgrrrmodel_input_namesr>rHrVrdr|rrrFr classmethodrrrrrr$rr4r>rFrw__static_attributes____classdictcell__ __classcell__)r@r6s@@r7r/r/OsKHJI DLIINJNLN CJNOL./##11233j&&&&P  :*#*#X+h+hZ`=`=`=D.-.-`{,{,z&#&#P   G G 0 0D++$%%2gggr:r/rrTzvideo processor file)object object_class object_files)visionrL)Hr rrOcollections.abcr functoolsrtypingrnumpyrhuggingface_hubrhuggingface_hub.dataclassesrdynamic_module_utilsrimage_processing_backendsr image_processing_utilsr image_utilsr r rrprocessing_utilsrrutilsrrrrrrrrrrr utils.hubrrutils.import_utilsr video_utilsr!r"r#r$r%r&r'r(r)r*r+ra$torchvision.transforms.v2.functional transformsv2 functionalrQr, get_loggerr rr/r__doc__r5r<r:r7rxs1 $+;490 3    +(      66/   H % ,-t g+t g.t gn"++=+I+I!J!!))5-?-K-K-S-S-Z-Z /CRh.[.""*6r: