pyiSSKrSSKrSSKrSSKJr SSKJr SSKJr SSK r SSK J r J r SSKJr SSKJr SS KJr SS KJr SS KJrJrJrJr SS KJrJr SS KJrJ r J!r!J"r"J#r#J$r$J%r%J&r&J'r'J(r(J)r) SSK*J+r+ SSK,J-r- SSK.J/r/J0r0J1r1J2r2J3r3J4r4J5r5J6r6J7r7 \%"5(aSSK8r8\'"5(a SSK9J:s J;s Jr> \(R~"\@5rASrB\#"S\B5\-"SS9"SS\555rC\$"\CR5\ClD\CRRb5\CRRRSSSS9\CRlEgg)N)Callable)partial)Any) create_repois_offline_mode)validate_typed_dict)custom_object_save)TorchvisionBackend) BatchFeature)ChannelDimensionSizeDictis_vision_availablevalidate_kwargs)Unpack VideosKwargs) IMAGE_PROCESSOR_NAMEPROCESSOR_NAMEVIDEO_PROCESSOR_NAME TensorTypeadd_start_docstrings copy_funcis_torch_availableis_torchcodec_availableis_torchvision_v2_availableloggingsafe_load_json_file) cached_file)requires) VideoInput VideoMetadatagroup_videos_by_shapeinfer_channel_dimension_formatis_valid_video load_videomake_batched_metadatamake_batched_videosreorder_videos)PILImageResamplinga Args: do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the video's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict`, *optional*, defaults to `self.size`): Size of the output video after resizing. Can be overridden by the `size` parameter in the `preprocess` method. size_divisor (`int`, *optional*, defaults to `self.size_divisor`): The size by which to make sure both the height and width can be divided. default_to_square (`bool`, *optional*, defaults to `self.default_to_square`): Whether to default to a square video when resizing, if size is an int. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the video to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method. crop_size (`dict[str, int]` *optional*, defaults to `self.crop_size`): Size of the output video after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the video by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`): Scale factor to use if rescaling the video. Only has an effect if `do_rescale` is set to `True`. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the video. Can be overridden by the `do_normalize` parameter in the `preprocess` method. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Mean to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Standard deviation to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `self.image_std`): Whether to convert the video to RGB. video_metadata (`VideoMetadata`, *optional*): Metadata of the video containing information about total duration, fps and total number of frames. do_sample_frames (`int`, *optional*, defaults to `self.do_sample_frames`): Whether to sample frames from the video before processing or to process the whole video. num_frames (`int`, *optional*, defaults to `self.num_frames`): Maximum number of frames to sample when `do_sample_frames=True`. fps (`int` or `float`, *optional*, defaults to `self.fps`): Target frames to sample per second when `do_sample_frames=True`. return_tensors (`str` or `TensorType`, *optional*): Returns stacked tensors if set to `pt, otherwise returns a list of tensors. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output video. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input video. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input video. If unset, the channel dimension format is inferred from the input video. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: video in (height, width) format. device (`torch.device`, *optional*): The device to process the videos on. If unset, the device is inferred from the input videos. return_metadata (`bool`, *optional*): Whether to return video metadata or not. z!Constructs a base VideoProcessor.)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+\,"\-5S\S\\S\4Sj5r.SBS\'S S\%S\%S\/SSS \%S!\/S"\%S#\"S$\%S%\"\'\"-S-S&\"\'\"-S-S'\)\0-S-S\4S(jjr1\2SCS)\)\3Rh-S*\)\3Rh-S-S+\%S,\%S-\)\%-S-S.\)4 S/jj5r5SDS0\)\3Rh-S1\%4S2jjr6\2S)\)\3Rh-S\7\$\)\84\$\)\8444S3j5r9\2S4\$\)\844S5j5r:S\$\)\844U4S6jjr;S\)4S7jr\2S;\)\3Rh-4S<j5r?\2SES=j5r@SBS>\)\'\)-\'\'\)-4S?jjrAS@rBU=rC$)FBaseVideoProcessorNTgp?Fpixel_values_videoskwargsreturnc &>[TU]"S0UD6 g)N)super__init__)selfr1 __class__s `/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/video_processing_utils.pyr6BaseVideoProcessor.__init__s "6"c (UR"U40UD6$N) preprocess)r7videosr1s r9__call__BaseVideoProcessor.__call__sv000r;videoz torch.Tensorc$[R"U5nURSS:Xd#USSSS2SS24S:R5(dU$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)r7rBalphas r9convert_to_rgb!BaseVideoProcessor.convert_to_rgbs$$U+ ;;r?a c1al(;c(A'F'F'H'HLc1al#e+U3a?++s2U3a?5KeTWY[Z[Y[]^`aTaNb5bb r;metadata num_framesfpsc UbUb [S5eUbUO URnUbUO URnURnUc8Ub5Ub URc [S5e[ XQR- U-5nX%:a[SUSUS35eUb*[ R "SXUU- 5R 5nU$[ R "SU5R 5nU$)a Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames. If `fps` is passed along with metadata, `fps` frames per second are sampled uniformty. Arguments `num_frames` and `fps` are mutually exclusive. Args: metadata (`VideoMetadata`): Metadata of the video containing information about total duration, fps and total number of frames. num_frames (`int`, *optional*): Maximum number of frames to sample. Defaults to `self.num_frames`. fps (`int` or `float`, *optional*): Target frames to sample per second. Defaults to `self.fps`. Returns: np.ndarray: Indices to sample video frames. zc`num_frames`, `fps`, and `sample_indices_fn` are mutually exclusive arguments, please use only one!zAsked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Please pass in `VideoMetadata` object or use a fixed `num_frames` per input videoz(Video can't be sampled. The `num_frames=z` exceeds `total_num_frames=z`. r) ValueErrorrOrPtotal_num_framesinttorcharange)r7rNrOrPr1rSindicess r9 sample_frames BaseVideoProcessor.sample_framess 0 ?z5u $.#9Zt _c$((#44  #/8<<#7 h- +X>*1'%%en5 ''1 $? $F-N%% q **&)T**#'"3"3F";";KK Ou!3!3E!: OUVW";$$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$)z* Prepare the input videos for processing. rrEr ) rcnpndarrayrU from_numpy contiguousr#r LASTpermutetorb)r7r?rorpprocessed_videosrBs r9_prepare_input_videos(BaseVideoProcessor._prepare_input_videos*sE%,,((/::<!($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_keysror[rprZ)rZr[r\)r?rorp data_formatreturn_metadatar?r4)rkeysrd valid_kwargs__annotations__r setdefaultgetattrpoprrXrmr{_standardize_kwargs_validate_preprocess_kwargs _preprocess) r7r?r1 kwarg_nameror[rprZr\rpreprocessed_videoss r9r>BaseVideoProcessor.preprocessGs "KKM!%d&7&7&G&G&L&L&N!OScRd!d D--v6++;;J   j'$D*I J<#JJ':;!::&89H%$45EUGD$6$6A&A[_!%!?!? )-/ "@"  ++6gm+n))3F3 ((262  =! **%67"..GfGG 4B 0 1""r;do_convert_rgb do_resizesizeresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stdr~c [U5unn0nUR5H:unnU(aURU5nU(aURUXES9nUUU'M< [ UU5n[U5unn0nUR5H8unnU(aUR UU5nUR UXXU 5nUUU'M: [ UU5n[SU0U S9$)N)rrr0)data tensor_type)r"itemsrLresizer( center_croprescale_and_normalizer )r7r?rrrrrrrrrrrr~r1grouped_videosgrouped_videos_indexresized_videos_groupedrIstacked_videosresized_videosprocessed_videos_groupedrzs r9rBaseVideoProcessor._preprocessvs $0EV/L,,!#%3%9%9%; !E>!%!4!4^!D!%^$!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) clsrrrrrrr1video_processor_dicts r9from_pretrained"BaseVideoProcessor.from_pretrainedsdn({#1 %5!"%z  #7O'*'C'CDa'lek'l$}}1>. ) ) ?>2BBe!fg g NT2 #ZZ(8$?NjjN,@,@,Mb,QRG!'CDCFCKKG#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)rrr1rrrrrrr from_pipelinerris_localresolved_video_processor_fileresolved_processor_filevideo_processor_filer resolved_fileresolved_video_processor_filesrprocessor_dicts r9r+BaseVideoProcessor.get_video_processor_dict6s$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 r4)copyrupdaterrrreversedrdrhasattrsetattrrbr warning_once__name__r) rrr1rkvr extra_keyskeys r9rBaseVideoProcessor.from_dicts-" 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)r5to_dictrr8r)r7 filtered_dictr8s r9rBaseVideoProcessor.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. rrT)indent sort_keys )rrrcrsrttolistjsondumps)r7 dictionaryrvalues r9to_json_string!BaseVideoProcessor.to_json_strings\\\^ $**,JC%,,"',,. -zz*Q$?$FFr;json_file_pathc[USSS9nURUR55 SSS5 g!,(df  g=f)z Save this instance to a JSON file. Args: json_file_path (`str` or `os.PathLike`): Path to the JSON file in which this image_processor instance's parameters will be saved. wutf-8encodingN)openwriter)r7r writers r9rBaseVideoProcessor.to_json_files3.# 8F LL,,. /9 8 8s 5 AcTURRSUR53$)N )r8rr)r7s r9__repr__BaseVideoProcessor.__repr__s(..))*!D,?,?,A+BCCr; json_filec[USSS9nUR5nSSS5 [R"W5nU"S0UD6$!,(df  N,=f)a Instantiates a video processor of type [`~video_processing_utils.VideoProcessorBase`] from the path to a JSON file of parameters. Args: json_file (`str` or `os.PathLike`): Path to the JSON file containing the parameters. Returns: A video processor of type [`~video_processing_utils.VideoProcessorBase`]: The video_processor object instantiated from that JSON file. rr rNr4)rreadrloads)rrreadertextrs r9from_json_file!BaseVideoProcessor.from_json_filesI)S7 3v;;=D4#zz$/*)**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.) rcrrtransformers.models.automodelsautorrRr)r auto_class auto_modules r9register_for_auto_class*BaseVideoProcessor.register_for_auto_classsE *c**#,,J66{// |+FGH H$r;video_url_or_urlsc Sn[5(d[R"S5 Sn[U[5(a.[ [ UVs/sHo@R XBS9PM sn65$[XUS9$s snf)z Convert a single or a list of urls into the corresponding `np.array` objects. If a single url is passed, the return value will be a single object. If a list is passed a list of objects is returned. torchcodecz`torchcodec` is not installed and cannot be used to decode the video by default. Falling back to `torchvision`. 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KHJI DLIINJNLN CJNOL./# !5#$#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;r.rr2zvideo processor file)object object_class object_files)Grrr-collections.abcr functoolsrtypingrnumpyrshuggingface_hubrrhuggingface_hub.dataclassesrdynamic_module_utilsr image_processing_backendsr image_processing_utilsr image_utilsr rrrprocessing_utilsrrutilsrrrrrrrrrrr utils.hubrutils.import_utilsr video_utilsr r!r"r#r$r%r&r'r(rU$torchvision.transforms.v2.functional transformsv2 functionalrGr) get_loggerrrr<r.r__doc__formatr4r;r9r[sK $8;490 3    #(     66/   H %A" H'" ,-p g+p g.  p gf"++=+I+I!J!!))5-?-K-K-S-S-Z-Z /CRh.[.""*6r;