`iddlZddlZddlZddlmZddlmZddlmZm Z m Z m Z ddl Z ddlmZddlmZmZddlmZdd lmZmZmZdd lmZmZdd lmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)dd l*m+Z+dd l,m-Z-ddl.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7e&rddl8Z8e(rddl9m:Z;e)j<e=Z>dZ?e!de?e-dGddeZ@e"e@jAe@_Ae@jAjB.e@jAjBCddde@jA_BdSdS)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)backendsceZdZdZdZdZdZdZdZdZ dZ dZ dZ dZ dZdZdZdZdZdZdZdZeZdgZdeeddffd Zdefd Zd d defd Z dAd ede e!de e"e!e#ffdZ$ dAdede"ee%fde e&de e'de(d f dZ) dAdede e"e*e+fde e*de(d fdZ,e-e.dedeedefdZ/ dBde(d de&de&de0de dde&d e0d!e&d"e#d#e&d$e e"e#e(e#fd%e e"e#e(e#fd&e e"e*e1fdefd'Z2e3 dCd)e"e*e4j5fd*e e"e*e4j5fd+e&d,e&d-e e"e*e&fd.e*f d/Z6dDd0e"e*e4j5fd1e&fd2Z7e3d)e"e*e4j5fde8e%e*e9fe%e*e9fffd3Z:e3d4e%e*e9ffd5Z;de%e*e9ffd6Zd:Z?e3d;e"e*e4j5ffd<Z@e3dEd>ZAdBd?e"e*e(e*e(e(e*ffd@ZBxZCS)FBaseVideoProcessorNTgp?Fpixel_values_videoskwargsreturnc rt|dd|_|D]N\}} t |||#t $r*}td|d|d||d}~wwxYw|d|j }|*t||d|j nd|_ |d|j }|t|d nd|_ t|jj|_|jD]\}||t ||||/t ||t't)||d]dS) 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 w/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/video_processing_utils.pyr<zBaseVideoProcessor.__init__s  & +zKBaseVideoProcessor._decode_and_sample_videos...:s" L L LE!7!7 L L LrVrdim)ristack)ruimagess rPrwz@BaseVideoProcessor._decode_and_sample_videos..9sGK L LV L L LRSTTTrVzUSampling frames from a list of images is not supported! Set `do_sample_frames=False`.ro) r)r(r&zipframes_indicesappend isinstancerD fetch_imagesrf fetch_videos) rKrTrmrnrosampled_videossampled_metadatarWrbrks rP_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&~%%rVinput_data_formatdevicec"g}|D]}t|tjrAt|tj|}t j|}|| |}| ||S)z: Prepare the input videos for processing. ) rnpndarrayr+rFIRSTri from_numpy contiguoustor)rKrTrrprocessed_videosrWs rP_prepare_input_videosz(BaseVideoProcessor._prepare_input_videosFs + +E%,, =3E;K;QSdee(//::<<!((  # #E * * * *rVc t|t|jjdgz|jjD]'}||t ||d(|d}|d}|d}|d}|rt|j fi|nd}| ||||\}}| |||}|j d i|}|j d i||d |d } |jd d |i|} | r|| d<| S) Nreturn_tensors)captured_kwargsvalid_processor_keysrrnrrm)rmrnro)rTrr data_formatreturn_metadatarTrs)rrGrDrErF setdefaultrJr=rrlrr_further_process_kwargs_validate_preprocess_kwargs _preprocess) rKrTr3 kwarg_namerrnrrmrorpreprocessed_videoss rPrSzBaseVideoProcessor.preprocess]s "KKMM!%d&7&G&L&L&N&N!O!OScRd!d    +; 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""rVdo_convert_rgb do_resizer7 interpolationzF.InterpolationModedo_center_cropr9 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 + +)*BDXYYCQg5;'7Q????Wg"79I!JXfggggrVmainpretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionc ||d<||d<||d<||d<|dd}|-tjdt|t d|}|||d <|j|fi|\} }|j| fi|S) 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 FutureWarningrfget_video_processor_dict from_dict) clsrrrrrrr3rvideo_processor_dicts rPfrom_pretrainedz"BaseVideoProcessor.from_pretrainedst({#1 %5!"%z$4d;;  % ME     l#E  #F7O'Cs'CDa'l'lek'l'l$fs}1<>. ) ) h !f>!f!f!fgg g NT2222  J#ZZ(8$??NjjN,@,@,M,Mb,QRRG'd'::6::G#99.II    ' t^D A A A A')gll>CW&X&X# 5666 M0KMMNNN    ' ' -jj)) (   ,,,rVc : |dd |dd|dd|dd|dd|dd}|d d|d d|d d |d d}|dd}|-tjdtt d|d|d||d<t rst ddttj }tj r}d}ntr}t}nqt} f dtt t"fD} | d}n6#t$$rt&$rt%dddtdwxYw t)|dd5} | } dddn #1swxYwYt-j| } | d| } n&#t,j$rt%d |d!wxYw|rt d"|n t d"|d#|| |fS)$a From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a video processor of type [`~video_processing_utils.VideoProcessorBase`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. Returns: `tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the video processor object. rNrFresume_downloadproxiesrrrr subfolder_from_pipeline _from_autorrvideo processor) file_typefrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueTcP g|]"}t| d x #S)F) filenamerrrrrr user_agentrr%_raise_exceptions_for_missing_entriesr ) rurrrrrr resolved_filerrrrrs rPrwz?BaseVideoProcessor.get_video_processor_dict..sp222 )49%-&/+9$+,;-="''1%-&/BG * * *   ! "   rVrz Can't load video processor for 'z'. 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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_dictzBaseVideoProcessor.to_dictsR$-(( 0$777 ($///)-)@%& rVc.|}|D]6\}}t|tjr|||<7|dd}|||d<tj|dddzS)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?rrrtolistr=rdumps)rK dictionaryrLrMr>s rPto_json_stringz!BaseVideoProcessor.to_json_strings\\^^ $**,, 1 1JC%,, 1"',,.. 3&>>* This API is experimental and may have some slight breaking changes in the next releases. Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoVideoProcessor "`): The auto class to register this new video processor with. rNz is not a valid auto class.) rrr transformers.models.automodelsautorrfr)r auto_class auto_modules rPregister_for_auto_classz*BaseVideoProcessor.register_for_auto_classLsn *c** -#,J666666666{J// I GGGHH H$rVvideo_url_or_urlscd}tstjdd}t|tr#t t fd|DSt ||S)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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