eThKSSKrSSKrSSKrSSKrSSKJrJrJrJrJ r J r SSK r SSK Jr SSKJrJr SSKJr SSKJrJrJr SSKJrJr SS KJrJrJrJrJ r J!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.J/r/J0r0J1r1J2r2J3r3 \)"5(aSS KJ4r4 \&"5(aSSK5r5\'"5(aSS KJ6r6 \("5(aSSK7J8r9 OSSK:J8r9 \*Rv"\<5r=Sr>\ "S\>5\,"SS9"SS\555r?\""\?R5\?l@\?RRb5\?RRRSSSS9\?RlAgg)N)AnyDictListOptionalTupleUnion)custom_object_save) BatchFeature get_size_dict)BaseImageProcessorFast)ChannelDimensionSizeDictvalidate_kwargs)Unpack VideosKwargs)VIDEO_PROCESSOR_NAME TensorTypeadd_model_info_to_auto_map"add_model_info_to_custom_pipelinesadd_start_docstrings cached_file copy_func download_urlis_offline_mode is_remote_urlis_torch_availableis_torchvision_availableis_torchvision_v2_availableis_vision_availablelogging)requires) VideoInputgroup_videos_by_shape load_videomake_batched_videosreorder_videosto_channel_dimension_format)PILImageResampling)pil_torch_interpolation_mapping) functionalac 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. do_pad (`bool`, *optional*): Whether to pad the video to the `(max_height, max_width)` of the videos in the batch. 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. 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.z!Constructs a base VideoProcessor.)vision torchvision)backendsc"^\rSrSrSrSrSrSrSrSr Sr Sr Sr Sr SrSrSrSrSr\rS/rS\\SS4U4SjjrS\4S jrS S S\4S jrS;S \S\\\\4S\SS\ S 4Sjjr!\""\#5S \S\\S\4Sj5r$SS*\\\,RZ4S+\%4S,jjr/\+S#\\\,RZ4S\0\1\\24\1\\2444S-j5r3\+S.\1\\244S/j5r4S\1\\244S0jr5S\4S1jr6S2\\\,RZ44S3jr7S4r8\+S5\\\,RZ44S6j5r9\+S?S7j5r:S8\\\ \44S9jr;S:r[TU]5 URSS5UlUR 5Hup#[ XU5 M URSUR5nUb#[XQRSUR5S9OSUl URSUR5nUb [USS 9OSUl [URRR!55UlUR"H;nUR%U5b[ XX5 M%[ X['XS55 M= 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_keysgetgetattr)selfr3keyvalueerrr7r9 __class__s [/var/www/auris/envauris/lib/python3.13/site-packages/transformers/video_processing_utils.pyr<BaseVideoProcessor.__init__sK  & +!%!4!4^!D!%"Xe"-"-; "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/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 `huggingface-cli 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/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 FutureWarning ValueErrorget_video_processor_dict from_dict) clsrrrrrrr3rvideo_processor_dicts rPfrom_pretrained"BaseVideoProcessor.from_pretrainedIst({#1 %5!"%z$4d;  % MME   l#E  #7O'*'C'CDa'lek'l$}}1>. ) ) ?>2BBe!fg g NT2 #ZZ(8$?NjjN,@,@,Mb,QRG'':6:G#99.I     ' tD A')ggll>CW&X# 56 /0K/LMN   ' ' -jj) ( ,,,rXc URSS5nURSS5nURSS5nURSS5nURSS5nURSS5nURS S5n URS S5n URS S 5n URS S5n URSS5n Ub+[R"S[5 Ub [ S5eUnSU S.nU bXS'[ 5(aU (d[ RS5 Sn [U5n[RRU5n[RRU5(aUnSnO8[U5(aUn[U5nO[n[!UUUUUUU UUU U S9 n[)USSS9nUR+5nSSS5 [,R."W5nU(a[ RS"U35 O[ RS"WS#U35 U(d0S$U;a[3US$U5US$'S%U;a[5US%U5US%'UU4$!["a- Sn[!UUUUUUU UUU U S9 n[ R%S5 N["a e[&a [#SUSUS[S35ef=f!,(df  GN=f![,R0a [#S US!35ef=f)&a From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a video processor of type [`~video_processing_utils.VideoProcessorBase`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. Returns: `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the video processor object. rNrFresume_downloadproxiesrrrr subfolder_from_pipeline _from_autorrvideo processor) file_typefrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueT) rrrrrr user_agentrrzpreprocessor_config.jsonaAYou have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.z Can't load video processor for 'z'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'z2' is the correct path to a directory containing a z filerutf-8encodingz"It looks like the config file at 'z' is not a valid JSON file.zloading configuration file z from cache at auto_mapcustom_pipelines)r=rrrrrrBrstrrrisdirrrrrrEnvironmentError warning_once ExceptionopenreadjsonloadsJSONDecodeErrorrr)rrr3rrrrrrrrr from_pipelinerris_localresolved_video_processor_filevideo_processor_filereadertextrs rPr+BaseVideoProcessor.get_video_processor_dictsr$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 )2 (<$0;1('#1#$3%5)%' 1-^ 3S7Kv{{}L#'::d#3   KK56S5TU V KK-.B-C?SpRqr 113M(46S4$Z0"%99;]();<>[<$%78$V++{$ 'A$0;1('#1#$3%5)%' 1-##P $  &67T6UV99V8WX//C.DEK LK## "45R4SSno  s6 I*: K"KK"*4K  -K  KK""$Lrc UR5nURSS5nSU;aSU;aURS5US'SU;aSU;aURS5US'U"S0UD6n/nUR5H4upg[XF5(dM[ XFU5 UR U5 M6 UHnURUS5 M [ RSU35 U(aXB4$U$)a Instantiates a type of [`~video_processing_utils.VideoProcessorBase`] from a Python dictionary of parameters. Args: video_processor_dict (`Dict[str, Any]`): Dictionary that will be used to instantiate the video processor object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~video_processing_utils.VideoProcessorBase.to_dict`] method. kwargs (`Dict[str, Any]`): Additional parameters from which to initialize the video processor object. Returns: [`~video_processing_utils.VideoProcessorBase`]: The video processor object instantiated from those parameters. return_unused_kwargsFr7r9NzVideo processor rz)copyr=r?hasattrr@rprBr)rrr3rvideo_processor to_removerLrMs rPrBaseVideoProcessor.from_dicts" 488:%zz*@%H V *> >+1::f+=  ( & [4H%H06 ;0G  -5 45  ,,.JC,,e4  %)C JJsD !  &&789 "* *" "rXcx[R"UR5nURRUS'U$)z Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this video processor instance. video_processor_type)rdeepcopy__dict__rO__name__)rKoutputs rPto_dictBaseVideoProcessor.to_dicts0t}}-)-)@)@%& 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?rhrirjtolistr=rdumps)rK dictionaryrLrMr>s rPto_json_string!BaseVideoProcessor.to_json_strings}\\^ $**,JC%,,"',,. - &>>* This API is experimental and may have some slight breaking changes in the next releases. Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoVideoProcessor "`): The auto class to register this new video processor with. rNz is not a valid auto class.) rhrrtransformers.models.automodelsautorrr)r auto_class auto_modules rPregister_for_auto_class*BaseVideoProcessor.register_for_auto_classsE *c**#,,J66{// |+FGH H$rXvideo_url_or_urlsc[U[5(a!UVs/sHo RU5PM sn$[U[5(a [ U5$[ S[ U535es 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. z=only a single or a list of entries is supported but got type=)rhrD fetch_videosrr% TypeErrortype)rKrxs rPrBaseVideoProcessor.fetch_videos sm ' . .2CD2CQ%%a(2CD D )3 / //0 0[\`ar\s[tuv v EsA.)r>r9rHr7)NNrS)NFFNmain)F)AutoVideoProcessor)>r __module__ __qualname____firstlineno__rrwrrr7rr8r9rrrrrrrrrEmodel_input_namesrr<r rVr#rcrrrrrrrrBASE_VIDEO_PROCESSOR_DOCSTRINGrTboolrr~floatrr classmethodrPathLikerrrrrrrrrrrrrr__static_attributes__ __classcell__)rOs@rPr0r0s KHJI DLIIN FJNLNL./= !5=$=>1L1 8EI+/   $E#/?*?$@A (  n   28999&9  9:9\<@!/h^$/h/h /h  /h sm /h 56/h/h/h/h/h/h/hU5$u+#567/hE%e"456/h !sJ!78!/h" #/hb8<$!&,0o=',S"++-='>o=E#r{{"234o= o=  o= c4i() o=o=o=b;-eC4D.E;-TX;-zI,,1#r{{2B,CI, tCH~tCH~- .I,I,V*#T#s(^*#*#X c3h GG* 05bkk1A+B 0D+uS"++-='>++$%%2 weCcN.C w wrXr0rr$zvideo processor file)object object_class object_files)Crrrrtypingrrrrrrnumpyridynamic_module_utilsr image_processing_utilsr r image_processing_utils_fastr image_utilsrrrprocessing_utilsrrutilsrrrrrrrrrrrrrr r!utils.import_utilsr" video_utilsr#r$r%r&r'r(r)rlr*torchvision.transforms.v2r+r^torchvision.transforms get_loggerrrBr)r0r__doc__formatrzrXrPrBsQ  ::4@ 3")/<"$$=:   H %8"lv'" ,-H w/H w.  H wV"++=+I+I!J!!))5-?-K-K-S-S-Z-Z /CRh.[.""*6rX