jsSSKrSSKrSSKrSSKJr SSKJr SSKJr SSK r SSK J r SSK Jr SSKJr SS KJr SS KJr SS KJrJrJrJr SS KJrJr SS KJrJrJ r J!r!J"r"J#r#J$r$J%r%J&r&J'r'J(r( SSK)J*r*J+r+ SSK,J-r- SSK.J/r/J0r0J1r1J2r2J3r3J4r4J5r5J6r6J7r7J8r8J9r9 \$"5(aSSK:r:\&"5(a SSK;Jr? \"5(aSSKJ@r@ \'R"\B5rC\-"SS9"SS\55rD\#"\DR5\DlE\DRRb5\DRRRSSSS9\DRlFgg)N)Callable)partial)Any)is_offline_mode)validate_typed_dict)custom_object_save)TorchvisionBackend) BatchFeature)ChannelDimensionSizeDictis_vision_availablevalidate_kwargs)Unpack 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)vision torchvision)backendsc^\rSrSrSrSrSrSrSrSr Sr Sr Sr Sr SrSrSrSrSrSrSrSrSr\rS/rS\\SS4U4S jjrS\4S jrS S S\4S jrSAS\ S\!S-S\!\"-S-4Sjjr#SAS\S\ \$-S\%S-S\&S-S\'S 4 Sjjr(SAS\S\)\*-S-S\)S-S\'S 4Sjjr+\,S\S\\S\4Sj5r-SBS\'S S\%S\%S\.SSS \%S!\.S"\%S#\"S$\%S%\"\'\"-S-S&\"\'\"-S-S'\)\/-S-S\4S(jjr0\1SCS)\)\2Rf-S*\)\2Rf-S-S+\%S,\%S-\)\%-S-S.\)4 S/jj5r4SDS0\)\2Rf-S1\%4S2jjr5\1S)\)\2Rf-S\6\$\)\74\$\)\7444S3j5r8\1S4\$\)\744S5j5r9S\$\)\744U4S6jjr:S\)4S7jr;S8\)\2Rf-4S9jr\1SES=j5r?SBS>\)\'\)-\'\'\)-4S?jjr@S@rAU=rB$)FBaseVideoProcessorONTgp?Fpixel_values_videoskwargsreturnc &>[TU]"S0UD6 g)N)super__init__)selfr3 __class__s ڱC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/video_processing_utils.pyr8BaseVideoProcessor.__init__hs "6"c (UR"U40UD6$N) preprocess)r9videosr3s r;__call__BaseVideoProcessor.__call__ksv000r=videoz torch.Tensorc>[R"U5nURSS:Xd#USSSS2SS24S:R5(dUSSS2SS2SS24$USSSS2SS24S- nSUSSSS2SS24- S-USSSS2SS24USSS2SS2SS24--nU$)z Converts a video to RGB format. Args: video (`"torch.Tensor"`): The video to convert. Returns: `torch.Tensor`: The converted video. .Ngo@r)tvFgrayscale_to_rgbshapeany)r9rDalphas r;convert_to_rgb!BaseVideoProcessor.convert_to_rgbns$$U+ ;;r?a c1al(;c(A'F'F'H'Hbqb!Q' 'c1al#e+U3a?++s2U3a?5KeTWY[Z[Y[]^`aTaNb5bb r=metadata num_framesfpsc UbUb [S5eUbUO URnUbUO URnURnUc8Ub5Ub URc [S5e[ XQR- U-5nX%:a[SUSUS35eUb*[ R "SXUU- 5R 5nU$[ R "SU5R 5nU$)a Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames. If `fps` is passed along with metadata, `fps` frames per second are sampled uniformly. Arguments `num_frames` and `fps` are mutually exclusive. Args: metadata (`VideoMetadata`): Metadata of the video containing information about total duration, fps and total number of frames. num_frames (`int`, *optional*): Maximum number of frames to sample. Defaults to `self.num_frames`. fps (`int` or `float`, *optional*): Target frames to sample per second. Defaults to `self.fps`. Returns: np.ndarray: Indices to sample video frames. zc`num_frames`, `fps`, and `sample_indices_fn` are mutually exclusive arguments, please use only one!zAsked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Please pass in `VideoMetadata` object or use a fixed `num_frames` per input videoz(Video can't be sampled. The `num_frames=z` exceeds `total_num_frames=z`. r) ValueErrorrQrRtotal_num_framesinttorcharange)r9rPrQrRr3rUindicess r; sample_frames BaseVideoProcessor.sample_framess 0 ?z5u $.#9Zt _c$((#44  #/8<<#7 h- +X>*1'%%en5 ''1 $? $F-N%% q **&)T**#'"3"3F";";KK Ou!3!3E!: OUVW";$$o$%%*.):):6):)g&%%!Ps=D-D(1 D-(D-input_data_formatdevicect/nUHn[U[R5(a$[R"U5R 5nUc [ U5nU[R:Xa"URSSSS5R 5nUbURU5nURU5 M U$)z* Prepare the input videos for processing. rrGr) renpndarrayrW from_numpy contiguousr$r LASTpermutetord)r9rArqrrprocessed_videosrDs r;_prepare_input_videos(BaseVideoProcessor._prepare_input_videossE%,,((/::<!($B5$I! $4$9$99 aAq1<<>!(  # #E *!" r=c [UR5[URRR55S/-S9 [ URU5 URRHnUR U[XS55 M! URS5nURS5nURS5nURS5nU(a[UR40UD6OSnURUUUUS9upURXUS9nUR"S 0UD6nUR"S 0UD6 URS 5 URS 5n UR"S S U0UD6n U (aXzS'U $) Nreturn_tensors)captured_kwargsvalid_processor_keysrqr]rrr\)r\r]r^)rArqrr data_formatreturn_metadatarAr6)rkeysrf valid_kwargs__annotations__r setdefaultgetattrpoprrZror}_standardize_kwargs_validate_preprocess_kwargs _preprocess) r9rAr3 kwarg_namerqr]rrr\r^rpreprocessed_videoss r;r@BaseVideoProcessor.preprocesss "KKM!%d&7&7&G&G&L&L&N!OScRd!d D--v6++;;J   j'$D*I J<#JJ':;!::&89H%$45EUGD$6$6A&A[_!%!?!? )-/ "@"  ++6gm+n))3F3 ((262  =! **%67"..GfGG 4B 0 1""r=do_convert_rgb do_resizesizeresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stdrc [U5unn0nUR5H:unnU(aURU5nU(aURUXES9nUUU'M< [ UU5n[U5unn0nUR5H8unnU(aUR UU5nUR UXXU 5nUUU'M: [ UU5n[SU0U S9$)N)rrr2)data tensor_type)r#itemsrNresizer* center_croprescale_and_normalizer )r9rArrrrrrrrrrrrr3grouped_videosgrouped_videos_indexresized_videos_groupedrKstacked_videosresized_videosprocessed_videos_groupedr|s r;rBaseVideoProcessor._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/= $U +&<**BDXY"79I!JXfggr=pretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionc X'S'X7S'XGS'XgS'UbXWS'UR"U40UD6upUR"U40UD6$)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 r;from_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 URSS5nURSS5nURSS5nURSS5nURSS5nURSS5nURS S 5n URS S5n URS S5n S U S.n U bXS'[5(aU(d[RS5 Sn[ U5n[ R RU5n [ R RU5(aUnSnSn O^[n[U[UUUUUU UU SS9 nU[4Vs/sHn[UUUUUUUU UU SS9 =ncMUPM! nnU(aUSOSnSnUb[U5nSU;aUSnUbUc [U5nUc[SUSUSWS35eU (a[RSU35 UU4$[RSWSU35 UU4$s snf![a e[a [SUSUSUS35ef=f)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_autovideo processor) file_typefrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueT) filenamerrrrr user_agentrr%_raise_exceptions_for_missing_entriesrz 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 )rrrrstrrrisdirrrrrrOSError Exceptionr)rrr3rrrrrrr from_pipelinerris_localresolved_video_processor_fileresolved_processor_filevideo_processor_filer resolved_fileresolved_video_processor_filesrprocessor_dicts r;r+BaseVideoProcessor.get_video_processor_dicts$JJ{D1 $4e<**Y- 7D)!::&8%@::j$/JJ{B/  #3T:  **\59#4Y  $+8' (   %5 KKE F# (+,I(J%77==!>? 77>>7 8 8,I )&* #H#7 2 +61+'#1#%5)%':? +'&:;O$P2$P)49%-&/+9$+-="''1%-&/BG *    "$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$8!HH9H?HH*Irc UR5nURSS5nURUR5VVs0sH"upEX@RR ;dM XE_M$ snn5 U"S0UD6n/n[ [UR555H]n[Xh5(dMXRR ;dM0[XhURUS55 URU5 M_ U(a&[RSURSUS35 [RSU35 U(aXb4$U$s snnf) 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 r6)copyrupdaterrrreversedrfrhasattrsetattrrdr warning_once__name__r) rrr3rkvr extra_keyskeys r;rBaseVideoProcessor.from_dictls-" 488:%zz*@%H##flln$nndaM]M]MmMmHmTQTn$no5 45 D/0C,,[TU]5nURSS5 URRUS'U$)z Serializes this instance to a Python dictionary. Returns: `dict[str, Any]`: Dictionary of all the attributes that make up this video processor instance. image_processor_typeNvideo_processor_type)r7to_dictrr:r)r9 filtered_dictr:s r;rBaseVideoProcessor.to_dicts=) 0$7040G0G ,-r=cUR5nUR5H8up#[U[R5(dM&UR 5X'M: [ R"USSS9S-$)z Serializes this instance to a JSON string. Returns: `str`: String containing all the attributes that make up this feature_extractor instance in JSON format. rtT)indent sort_keys )rrrerurvtolistjsondumps)r9 dictionaryrvalues r;to_json_string!BaseVideoProcessor.to_json_strings\\\^ $**,JC%,,"',,. -zz*Q$?$FFr=json_file_pathc[USSS9nURUR55 SSS5 g!,(df  g=f)z Save this instance to a JSON file. Args: json_file_path (`str` or `os.PathLike`): Path to the JSON file in which this image_processor instance's parameters will be saved. wutf-8encodingN)openwriter )r9r writers r;rBaseVideoProcessor.to_json_files3.# 8F LL,,. /9 8 8s 5 AcTURRSUR53$)N )r:rr )r9s r;__repr__BaseVideoProcessor.__repr__s(..))*!D,?,?,A+BCCr= json_filec[USSS9nUR5nSSS5 [R"W5nU"S0UD6$!,(df  N,=f)a Instantiates a video processor of type [`~video_processing_utils.VideoProcessorBase`] from the path to a JSON file of parameters. Args: json_file (`str` or `os.PathLike`): Path to the JSON file containing the parameters. Returns: A video processor of type [`~video_processing_utils.VideoProcessorBase`]: The video_processor object instantiated from that JSON file. rrrNr6)rreadrloads)rrreadertextrs r;from_json_file!BaseVideoProcessor.from_json_filesI)S7 3v;;=D4#zz$/*)**4 3s A Ac[U[5(d URnSSKJs Jn [ X!5(d[US35eXlg)a Register this class with a given auto class. This should only be used for custom video processors as the ones in the library are already mapped with `AutoVideoProcessor `. 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. rNz is not a valid auto class.) rerrtransformers.models.automodelsautorrTr)r auto_class auto_modules r;register_for_auto_class*BaseVideoProcessor.register_for_auto_classsE *c**#,,J66{// |+FGH H$r=video_url_or_urlsc :Sn[5(d9[5(d[S[35e[R "S5 Sn[ U[5(a.[[UVs/sHo@RXBS9PM sn65$[XUS9$s snf)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. r-ra)backendr^) rr% ImportErrorr warningswarnrerfrbrjr')r9r,r^r/xs r;rjBaseVideoProcessor.fetch_videoss&((:<<!g78: MMI $G ' . .arsar\]///Warstu u/Tef fts.B r6)NNr?)NFFNmain)F)AutoVideoProcessor)Cr __module__ __qualname____firstlineno__rrrrr size_divisordefault_to_squarerrrrrrrr]rRrQr\rrrmodel_input_namesrr8r rBr!rNr"rVfloatrZdictboolrrfrorr r}rr@r rr classmethodrPathLikerrtuplerrrrr rrr"r*rj__static_attributes__ __classcell__)r:s@r;r0r0Os5KHJI DLIINJNLN CJNOL./# !5#$#1L1 8"&"& 33$J35[4  3r)--1 &&&&&,&&+ && $d? && n  &&V<@!   !11D8 d  n   :*#*#&*#  *#*#t37+h^$+h+h +h  +h L +h+h+h+h+h+hDK'$.+h4;&-+hj(4/+h !+hZ/3$!&#'`='*R[['8`=$t+`= `=  `= TzD `=`=`=D.-cBKK.?.-d.-`{,,/"++,={, tCH~tCH~- .{,{,z&#T#s(^&#&#P c3h  G G 03+< 0D+sR[['8++$%%2gcDIoT#Y.Oggr=r0rr6zvideo processor file)object object_class object_files)Hrrr1collections.abcr functoolsrtypingrnumpyruhuggingface_hubrhuggingface_hub.dataclassesrdynamic_module_utilsr image_processing_backendsr image_processing_utilsr image_utilsr r rrprocessing_utilsrrutilsrrrrrrrrrrr utils.hubrrutils.import_utilsr video_utilsr r!r"r#r$r%r&r'r(r)r*rW$torchvision.transforms.v2.functional transformsv2 functionalrIr+ get_loggerrrr0r__doc__formatr6r=r;r^s1 $+;490 3    +(      66/   H % ,-t g+t g.t gn"++=+I+I!J!!))5-?-K-K-S-S-Z-Z /CRh.[.""*6r=