biݟddlZddlZddlZddlmZddlmZddlmZddl m Z m Z ddl Z ddlmZmZddlmZdd lmZdd lmZmZdd lmZdd lmZmZmZdd lmZm Z ddl!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:e(rddl;Z;e*r ddlcm?Z@e+jAeBZCdZDe&deDe0dGddeZEe'eEjFeE_FeEjFjG.eEjFjGHdddeEjF_GdSdS)N)Callable)deepcopy)partial)AnyOptional) create_repois_offline_mode)validate_typed_dict)custom_object_save) BatchFeature get_size_dict)BaseImageProcessorFast)ChannelDimensionSizeDictvalidate_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_videosa 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)backendsceZdZdZdZdZdZdZdZdZ dZ dZ dZ dZ dZdZdZdZdZdZdZdZeZdgZdeeddffd Zdefd Zd d defd Z dAd ede dzde e!zdzfdZ" dAdedee#zde$dzde%dzde&d f dZ' dAdede(e)zdzde(dzde&d fdZ*e+e,dedeedefdZ- dBde&d de$de$de.de/dde$d e.d!e$d"e!d#e$d$e!e&e!zdzd%e!e&e!zdzd&e(e0zdzdefd'Z1e2 dCd)e(e3j4zd*e(e3j4zdzd+e$d,e$d-e(e$zdzd.e(f d/Z5dDd0e(e3j4zd1e$fd2Z6e2d)e(e3j4zde7e#e(e8fe#e(e8fffd3Z9e2d4e#e(e8ffd5Z:de#e(e8ffd6Z;de(fd7Ze2d;e(e3j4zfd<Z?e2dEd>Z@dBd?e(e&e(ze&e&e(zfd@ZAxZBS)FBaseVideoProcessorNTgp?Fpixel_values_videoskwargsreturnc ht|dd|D]N\}} t |||#t $r*}t d|d|d||d}~wwxYw|d|j}|*t||d|j nd|_|d|j }|t|d nd|_ t|j j|_|jD]\}||t ||||/t ||t%t'||d]dS) Nprocessor_classz Can't set z with value z for sizedefault_to_square)r6r7 crop_size) param_name)super__init__popitemssetattrAttributeErrorloggererrorr6rr7r8list valid_kwargs__annotations__keysmodel_valid_processing_keysgetrgetattr)selfr2keyvalueerrr6r8 __class__s ^/root/projects/butler/venv/lib/python3.11/site-packages/transformers/video_processing_utils.pyr;zBaseVideoProcessor.__init__s  $d+++!,,..  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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. Nzc`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) ValueErrorrarbtotal_num_framesinttorcharange)rIr`rarbr2reindicess rN sample_framesz BaseVideoProcessor.sample_framess"0 ?z5u $.#9ZZt _cc$(#4  #/8<#7 h- zKBaseVideoProcessor._decode_and_sample_videos...:s# N N Ne!25!9!9 N N NrTr)dim)rgstack)rsimagess rNruz@BaseVideoProcessor._decode_and_sample_videos..9sGK N Nv N N NTUVVVrTzUSampling frames from a list of images is not supported! Set `do_sample_frames=False`.rm) r*r)r'zipframes_indicesappend isinstancerB fetch_imagesrd fetch_videos) rIrRrkrlrmsampled_videossampled_metadatarUr`ris rN_decode_and_sample_videosz,BaseVideoProcessor._decode_and_sample_videossh%V,,.vnUUU &) $ $ h)9 hN! #&v~#>#> 2 2x++X>>>*1'%%eGn555 ''1111#F-NNq ** h&)T** h"&"3"3F";";$$o *.):):6Uf):)g)g&~%%rTinput_data_formatdevicecg}|D]}t|tjr&tj|}|t |}|tjkr*| dddd}|| |}| ||S)z: Prepare the input videos for processing. NrrXr ) r}npndarrayrg from_numpy contiguousr&rLASTpermutetor|)rIrRrrprocessed_videosrUs rN_prepare_input_videosz(BaseVideoProcessor._prepare_input_videosFs + +E%,, =(//::<<!($B5$I$I! $4$999 aAq11<<>>!((  # #E * * * *rTc (t|t|jjdgzt |j||jjD]'}||t||d(|d}|d}|d}|d}|rt|j fi|nd}| ||||\}}| |||}|j d i|}|jd i||d |d } |jd d |i|} | r|| d<| S) Nreturn_tensors)captured_kwargsvalid_processor_keysrrlrrk)rkrlrm)rRrr data_formatreturn_metadatarRrq)rrErBrCrDr setdefaultrHr<rrjrr_further_process_kwargs_validate_preprocess_kwargs _preprocess) rIrRr2 kwarg_namerrlrrkrmrpreprocessed_videoss rNrQzBaseVideoProcessor.preprocesscs "KKMM!%d&7&G&L&L&N&N!O!OScRd!d    D-v666+; K KJ   j'$ D*I*I J J J J"JJ':;;!::&899H%%$455EU_GD$6AA&AAA[_!%!?!? )-/ "@" "  ++6M^gm+nn--7777((226222  =!!! **%677.d.GGfGGG  C4B  0 1""rTdo_convert_rgb do_resizer6 interpolationztvF.InterpolationModedo_center_cropr8 do_rescalerescale_factor do_normalize image_mean image_stdrc t|\}}i}|D];\}}|r||}|r||||}|||< E!%!4!4^!D!D e!%^$Vc!d!d,: "5 ) )'(>@TUU0E^/T/T,,#% %3%9%9%;%; = = !E> M!%!1!1.)!L!L!77 NL*V_N/= $U + +)*BDXYY"79I!JXfggggrTmainpretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionc v||d<||d<||d<||d<|||d<|j|fi|\}}|j|fi|S)aR 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. 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} ```rrrrNr)get_video_processor_dict from_dict) clsrrrrrrr2video_processor_dicts rNfrom_pretrainedz"BaseVideoProcessor.from_pretraineds{n({#1 %5!"%z  #F7O'Cs'CDa'l'lek'l'l$fs}1<>. ) ) h !f>!f!f!fgg g NT2222  J#ZZ(8$??NjjN,@,@,M,Mb,QRRG!'CCDCFCCKG#99.II    ' t^D A A A A')gll>CW&X&X# 5666 M0KMMNNN    ' ' -jj)) (   ,,,rTc  |dd |dd |dd|dd|dd|dd|d d |d d}|d d}d |d||d<trstddt t j}t jr}d}d}nt} tt d } f d|tfD} | r| dnd}n1#t$rt$rtddd|dwxYwd} |t|} d| vr| d} || t|} | tddd|d|rtd|n td|d|| |fS)a From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a video processor of type [`~video_processing_utils.VideoProcessorBase`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. Returns: `tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the video processor object. rNrFproxiesrrr subfolder_from_pipeline _from_autovideo processor) file_typefrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueT filenamerrrrr user_agentrr%_raise_exceptions_for_missing_entriescN g|]!}t|  d x "S)Frr ) rsrrrrrr resolved_filerrrrs rNruz?BaseVideoProcessor.get_video_processor_dict..sm222 )49%-&/+9$+-="''1%-&/BG * * *    "   rTrz 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 )r<r r@rstrrrisdirrrr!rrOSError Exceptionr)rrr2 from_pipelineris_localresolved_video_processor_fileresolved_processor_filevideo_processor_fileresolved_video_processor_filesrprocessor_dictrrrrrrrrrs ` @@@@@@@@@rNrz+BaseVideoProcessor.get_video_processor_dictRs$JJ{D11 $4e<<**Y-- 7D))!::&8%@@::j$//JJ{B//  #3T::  **\599#4YY  $+8J' (    $%5 $ KKE F F F# (+,I(J(J%7==!>?? 7>>7 8 88 ,I )&* #HH#7 2 +61+'#1#%5)%':? + + +'2222222222222%9;O$P222.*:Xa2155]a.-      K7TKK9VKK0DKKK  $ " .01HIIN N22'56G'H$ ( 49M9U#67T#U#U  'G3PGG5RGG,@GGG    KKU6SUU V V V V KKr.BrrSprr   $V++s %A F//.Grc |}|dd}d|vrd|vr|d|d<d|vrd|vr|d|d<|di|}g}|D];\}}t||r&t |||||<|D]}||dt d||r||fS|S)a Instantiates a type of [`~video_processing_utils.VideoProcessorBase`] from a Python dictionary of parameters. Args: video_processor_dict (`dict[str, Any]`): Dictionary that will be used to instantiate the video processor object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~video_processing_utils.VideoProcessorBase.to_dict`] method. kwargs (`dict[str, Any]`): Additional parameters from which to initialize the video processor object. Returns: [`~video_processing_utils.VideoProcessorBase`]: The video processor object instantiated from those parameters. return_unused_kwargsFr6r8NzVideo processor rq)copyr<r=hasattrr>r|r@r)rrr2rr to_removerJrKs rNrzBaseVideoProcessor.from_dictsR" 488::%zz*@%HH V  *> > >+1::f+=+=  ( & [4H%H%H06 ;0G0G  -#55 455  ,,.. & &JC,, &e444  %%% " "C JJsD ! ! ! ! 888999  #"F* *" "rTcBt|j}i}|D]8\}}|,tt ||d}|dkr||||<3|||<9|dd|dd|jj|d<|S)z Serializes this instance to a Python dictionary. Returns: `dict[str, Any]`: Dictionary of all the attributes that make up this video processor instance. 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Args: json_file_path (`str` or `os.PathLike`): Path to the JSON file in which this image_processor instance's parameters will be saved. wutf-8encodingN)openwriter )rIr writers rNrzBaseVideoProcessor.to_json_file$s.# 8 8 8 0F LL,,.. / / / 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0s(AA A cH|jjd|S)N )rMrr )rIs rN__repr__zBaseVideoProcessor.__repr__/s'.)CCD,?,?,A,ACCCrT json_filect|dd5}|}dddn #1swxYwYtj|}|di|S)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. rrrNrq)rreadrloads)rrreadertextrs rNfrom_json_filez!BaseVideoProcessor.from_json_file2s)S7 3 3 3 !v;;==D ! ! ! ! ! ! ! ! ! ! ! ! ! ! !#z$//s**)***s 488AutoVideoProcessorct|ts|j}ddlmcm}t ||st|d||_dS)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. 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