?KiSSKrSSKrSSKrSSKJr SSKJr SSKJrJ r J r J r SSK r SSKJr SSKJrJr SSKJr SS KJrJrJr SS KJrJr SS KJrJrJrJ r J!r!J"r"J#r#J$r$J%r%J&r&J'r'J(r(J)r) SS K*J+r+ SS K,J-r- SSK.J/r/J0r0J1r1J2r2J3r3J4r4J5r5J6r6J7r7 \&"5(aSSK8r8\("5(aSSK9J:r; \)Rx"\=5r>Sr?\!"S\?5\-"SS9"SS\555r@\""\@R5\@lA\@RRb5\@RRRSSSS9\@RlBgg)N)deepcopy)partial)AnyCallableOptionalUnion)custom_object_save) BatchFeature get_size_dict)BaseImageProcessorFast)ChannelDimensionSizeDictvalidate_kwargs)Unpack VideosKwargs) IMAGE_PROCESSOR_NAMEPROCESSOR_NAMEVIDEO_PROCESSOR_NAME TensorTypeadd_start_docstrings copy_func download_urlis_offline_mode is_remote_urlis_torch_availableis_torchcodec_availableis_torchvision_v2_availablelogging) cached_file)requires) VideoInput VideoMetadatagroup_videos_by_shapeis_valid_video load_videomake_batched_metadatamake_batched_videosreorder_videosto_channel_dimension_format) functionala 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)backendsc2^\rSrSrSrSrSrSrSrSr Sr Sr Sr Sr SrSrSrSrSrSrSrSrSr\rS/rS\\SS4U4S jjrS\4S jrS S S\4S jrSAS\ S\!\"S\!\#\"\$44Sjjr%SAS\S\#\ \&4S\!\'S\!\(S\)S 4 Sjjr*SAS\S\!\#\+\,4S\!\+S\)S 4Sjjr-\."\/5S\S\\S\4Sj5r0SBS\)S S\'S\'S\1S\!SS \'S!\1S"\'S#\$S$\'S%\!\#\$\)\$4S&\!\#\$\)\$4S'\!\#\+\24S\4S(jjr3\4SCS)\#\+\5Rl4S*\!\#\+\5Rl4S+\'S,\'S-\!\#\+\'4S.\+4 S/jj5r7SDS0\#\+\5Rl4S1\'4S2jjr8\4S)\#\+\5Rl4S\9\&\+\:4\&\+\:444S3j5r;\4S4\&\+\:44S5j5rS8\#\+\5Rl44S9jr?S:r@\4S;\#\+\5Rl44S<j5rA\4SES=j5rBSBS>\#\+\)\+\)\)\+44S?jjrCS@rDU=rE$)FBaseVideoProcessorNTgp?Fpixel_values_videoskwargsreturnc >[TU]5 URSS5UlUR 5Hup#[ XU5 M URSUR5nUb#[XQRSUR5S9OSUl URSUR5nUb [USS 9OSUl [URRR!55UlUR"HDnUR%U5b[ XX5 M%[ X['[)XS555 MF g![ a%n[RSUSUSU35 UeSnAff=f) Nprocessor_classz Can't set z with value z for sizedefault_to_square)r7r8 crop_size) param_name)super__init__pop_processor_classitemssetattrAttributeErrorloggererrorr7r r8r9list valid_kwargs__annotations__keysmodel_valid_processing_keysgetrgetattr)selfr3keyvalueerrr7r9 __class__s e/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/transformers/video_processing_utils.pyr<BaseVideoProcessor.__init__sP  & ++X>*1'%%en5 ''1 $? $F-N%% q **&)T**#'"3"3F";";KKV LVE!7V LRST";$$o$%%*.):):6):)g&%%!Ms=D3 D.7 D3.D3input_data_formatdevicec"/nUHn[U[R5(a?[U[R U5n[ R"U5R5nUbURU5nURU5 M U$)z* Prepare the input videos for processing. ) r{npndarrayr*rFIRSTrl from_numpy contiguoustorz)rKrUrrprocessed_videosrYs rP_prepare_input_videos(BaseVideoProcessor._prepare_input_videosFs~E%,,3E;K;Q;QSde((/::<!(  # #E * rXc [UR5[URRR55S/-S9 URRHnUR U[ XS55 M! URS5nURS5nURS5nURS5nU(a[UR40UD6OSnURUUUUS9upURXUS9nUR"S 0UD6nUR"S 0UD6 URS 5 URS 5n UR"S S U0UD6n U (aXzS'U $) Nreturn_tensors)captured_kwargsvalid_processor_keysrrrrrq)rqrrrs)rUrr data_formatreturn_metadatarU)rrGrDrErF setdefaultrJr=rrorr_further_process_kwargs_validate_preprocess_kwargs _preprocess) rKrUr3 kwarg_namerrrrrqrsrpreprocessed_videoss rPrTBaseVideoProcessor.preprocess]sm "KKM!%d&7&7&G&G&L&L&N!OScRd!d ++;;J   j'$D*I J<#JJ':;!::&89H%$45EUGD$6$6A&A[_!%!?!? )-/ "@"  ++6gm+n--77 ((262  =! **%67"..GfGG 4B 0 1""rXdo_convert_rgb do_resizer7 interpolationzF.InterpolationModedo_center_cropr9 do_rescalerescale_factor do_normalize image_mean image_stdrc [U5unn0nUR5H:unnU(aURU5nU(aURUXES9nUUU'M< [ UU5n[U5unn0nUR5H8unnU(aUR UU5nUR UXXU 5nUUU'M: [ UU5nU (a[R"USS9OUn[SU0U S9$)N)r7rrrur2)data tensor_type) r$r?rcresizer) center_croprescale_and_normalizerlr}r )rKrUrrr7rrr9rrrrrrr3grouped_videosgrouped_videos_indexresized_videos_groupedr`stacked_videosresized_videosprocessed_videos_groupedrs rPrBaseVideoProcessor._preprocesss $0EV/L,,!#%3%9%9%; !E>!%!4!4^!D!%^$!d,: "5 ) &< ((>@TU0E^/T,,#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N/= $U +&<**BDXYCQ5;;'7Q?Wg"79I!JXfggrXpretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionc X'S'X7S'XGS'XgS'URSS5nUb+[R"S[5 Ub [ S5eUnUbXWS 'UR "U40UD6upUR "U 40UD6$) a 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 or url 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. resume_download: Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers. 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} ```rrrruse_auth_tokenNrThe `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.V`token` and `use_auth_token` are both specified. Please set only the argument `token`.r)r=warningswarn FutureWarningriget_video_processor_dict from_dict) clsrrrrrrr3rvideo_processor_dicts rPfrom_pretrained"BaseVideoProcessor.from_pretrainedst({#1 %5!"%z$4d;  % MME   l#E  #7O'*'C'CDa'lek'l$}}1>. ) ) ?>2BBe!fg g NT2 #ZZ(8$?NjjN,@,@,Mb,QRG'':6:G#99.I     ' tD A')ggll>CW&X# 56 /0K/LMN   ' ' -jj) ( ,,,rXc URSS5nURSS5nURSS5nURSS5nURSS5nURSS5nURS S5n URS S5n URS S 5n URS S5n URSS5n Ub+[R"S[5 Ub [ S5eUnSU S.nU bXS'[ 5(aU (d[ RS5 Sn [U5n[RRU5n[RRU5(aUnSnOf[U5(aUn[U5nOH[n[[ ["4Vs/sH n[%UUUUUUU UUU U SS9 =ncMUPM" nnUSn[+USSS9nUR-5nSSS5 [.R0"W5nUR3SU5nU(a[ RS"U35 UU4$[ RS"WS#U35 UU4$s snf![&a e[(a ['SUSUS[S35ef=f!,(df  N=f![.R4a ['S US!35ef=f)$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. rNrFresume_downloadproxiesrrrr subfolder_from_pipeline _from_autorrvideo processor) file_typefrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueT) filenamerrrrrr 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 filerutf-8encodingvideo_processorz"It looks like the config file at 'z' is not a valid JSON file.zloading configuration file z from cache at )r=rrrrirrBrstrrrisdirrrrrrrr OSError ExceptionopenreadjsonloadsrIJSONDecodeError)rrr3rrrrrrrrr from_pipelinerris_localresolved_video_processor_filevideo_processor_filer resolved_fileresolved_video_processor_filesreadertextrs rPr+BaseVideoProcessor.get_video_processor_dictes?$JJ{D1 $4e< **%6=**Y- 7D)$4d;!::&8%@::j$/JJ{B/  #3T:  **\59  % MME   l#E#4Y  $+8' (   %5 KKE F# (+,I(J%77==!>? 77>>7 8 8,I )H 8 9 9#@ ,89V,W )#7 $  &:;OQ_$`2$`)49%-&/+9$+,;-="''1%-&/BG *   ! "$`/2*1Oq0Q- 3S7Kv{{}L#'::d#3 #7#;#; >+1::f+=  ( & [4H%H06 ;0G  -5 45  ,,.JC,,e4  %)C JJsD !  &&789 "* *" "rXc[UR5nURSS5 URSS5 URRUS'U$)z Serializes this instance to a Python dictionary. Returns: `dict[str, Any]`: Dictionary of all the attributes that make up this video processor instance. rHN_valid_kwargs_namesvideo_processor_type)r__dict__r=rO__name__)rKoutputs rPto_dictBaseVideoProcessor.to_dictsJ$--( 0$7 ($/)-)@)@%& rXcUR5nUR5H8up#[U[R5(dM&UR 5X'M: UR SS5nUbXAS'[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. r>Nr6T)indent sort_keys ) rr?r{rrtolistr=rdumps)rK dictionaryrLrMr>s rPto_json_string!BaseVideoProcessor.to_json_strings}\\^ $**,JC%,,"',,. - &>>* This API is experimental and may have some slight breaking changes in the next releases. Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoVideoProcessor "`): The auto class to register this new video processor with. rNz is not a valid auto class.) r{rrtransformers.models.automodelsautor rir)r auto_class auto_modules rPregister_for_auto_class*BaseVideoProcessor.register_for_auto_classLsE *c**#,,J66{// |+FGH H$rXvideo_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`. Note that `torchvision` decoding is deprecated and will be removed in future versions. r-rw)backendrs)rrrr{rDrxrr&)rKr6rsr9xs rPrBaseVideoProcessor.fetch_videosfs}&(( MMI $G ' . .arsar\]///Warstu u/Tef fts A7 )r>r9rHr7)NNrS)NFFNmain)F)AutoVideoProcessor)Fr __module__ __qualname____firstlineno__rresamplerrr7 size_divisorr8r9rrrrrrrrrgrfrqrrrEmodel_input_namesrr<r rVr"rcr#rrkrfloatrodictboolrrDrrrrrBASE_VIDEO_PROCESSOR_DOCSTRINGrTrrr classmethodrPathLikerrtuplerrrrrrr(r,r4r__static_attributes__ __classcell__)rOs@rPr0r0s KHJI DLIINJNLN CJNOL./G !5G$G>1L1 8%)+/ 33SM3eCJ' ( 3r,004 &&&&mT12&&#4. && $H- && n  &&VEI $   $E#/?*?$@A   n   .&&#&#&&#  &#&#l<@,h^$,h,h ,h  ,h  56 ,h,h,h,h,h,hU5$u+#567,hE%e"456,h!sJ!78,h !,h\8<$!&,0o=',S"++-='>o=E#r{{"234o= o=  o= c4i() o=o=o=b;-eC4D.E;-TX;-zs,,1#r{{2B,Cs, tCH~tCH~- .s,s,j*#T#s(^*#*#X c3h GG* 05bkk1A+B 0D+uS"++-='>++$%%2geCcDcO4S.TggrXr0rr=zvideo processor file)object object_class object_files)Drrrr r functoolsrtypingrrrrnumpyrdynamic_module_utilsr image_processing_utilsr r image_processing_utils_fastr image_utilsrrrprocessing_utilsrrutilsrrrrrrrrrrrrr utils.hubr utils.import_utilsr! video_utilsr"r#r$r%r&r'r(r)r*rltorchvision.transforms.v2r+r^ get_loggerrrBrGr0r__doc__formatrrXrPr`s>  114@ 3#(     9   H %A" H'" ,-b g/b g.  b gJ"++=+I+I!J!!))5-?-K-K-S-S-Z-Z /CRh.[.""*6rX