o wi@sddlZddlZddlZddlZddlmZmZmZddlZ ddl m Z ddl m Z mZddlmZddlmZmZmZddlmZmZdd lmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%dd l&m'Z'dd l(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/e$rdd lm0Z0e!rddl1Z1e"rdd lm2Z2e#rddl3m4Z5nddl6m4Z5e%7e8Z9dZ:ede:e'ddGdddeZ;ee;j<e;_dddde;j<_=dSdS)N)AnyOptionalUnion)custom_object_save) BatchFeature get_size_dict)BaseImageProcessorFast)ChannelDimensionSizeDictvalidate_kwargs)Unpack VideosKwargs) VIDEO_PROCESSOR_NAME TensorTypeadd_start_docstrings cached_file copy_func download_urlis_offline_mode is_remote_urlis_torch_availableis_torchvision_availableis_torchvision_v2_availableis_vision_availablelogging)requires) VideoInput VideoMetadatagroup_videos_by_shape load_videomake_batched_videosreorder_videosto_channel_dimension_format)PILImageResampling)pil_torch_interpolation_mapping) 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. 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. 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`, *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.z!Constructs a base VideoProcessor.)vision torchvision)backendsc+s8eZdZdZdZdZdZdZdZdZ dZ dZ dZ dZ dZdZdZdZdZdZdZdZeZdgZdeeddffdd Zdefd d Zd d defd dZ   dSd d dee e!e"fdee#dee#fddZ$  dTdede!dee e%e&fde'd fddZ(e)e*dedeedefddZ+     dUde'd de e'e!e'e"fde,de,de-dee#ded d!e,d"e-d#e,d$e,d%e.d&e,d'ee e.e'e.fd(ee e.e'e.fd)ee,dee#dee#d*ee e%e/fd+ed def*d,d-Z0e1  . .  /dVd0e e%e2j3fd1ee e%e2j3fd2e,d3e,d4ee e%e,fd5e%f d6d7Z4dWd8e e%e2j3fd9e,fd:d;Z5e1d0e e%e2j3fde6e"e%e7fe"e%e7fffde"e%e7ffd?d@Z9de"e%e7ffdAdBZ:de%fdCdDZ;dEe e%e2j3ffdFdGZe1dXdNdOZ?dPe e%e'e%ffdQdRZ@ZAS)YBaseVideoProcessorNTgp?pixel_values_videoskwargsreturnc s"t|dd|_|D](\}}zt|||Wqty8}ztd|d|d||d}~ww|d|j }|durOt ||d|j dnd|_ |d|j }|durct |dd nd|_ t |jj|_|jD]}||durt||||qrt||t||dqrdS) Nprocessor_classz Can't set z with value z for sizedefault_to_square)r/r0 crop_size) param_name)super__init__pop_processor_classitemssetattrAttributeErrorloggererrorr/rr0r1list valid_kwargs__annotations__keysmodel_valid_processing_keysgetgetattr)selfr,keyvalueerrr/r1 __class__`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/transformers/video_processing_utils.pyr4s.  zBaseVideoProcessor.__init__cKs|j|fi|S)N) preprocess)rCvideosr,rIrIrJ__call__szBaseVideoProcessor.__call__videoz torch.TensorcCst|}|jddks|ddddddfdks|S|ddddddfd}d|ddddddfd|ddddddf|dddddddf}|S)z Converts a video to RGB format. Args: video (`"torch.Tensor"`): The video to convert. Returns: `torch.Tensor`: The converted video. .Ngo@r)Fgrayscale_to_rgbshapeany)rCrNalpharIrIrJconvert_to_rgbs .Tz!BaseVideoProcessor.convert_to_rgbmetadata num_framesfpscCs|dur |dur td|dur|n|j}|dur|n|j}|jd}|dur=|dur=|dur3tdt||d|}||krLtd|d|d|dur\td|||}ntd|}||}|S) aq 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: video (`torch.Tensor`): Video that need to be sampled. metadata (`VideoMetadata`, *optional*): 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`, *optional*): Target frames to sample per second. Defaults to `self.fps`. Returns: torch.Tensor: Sampled video frames. Nzc`num_frames`, `fps`, and `sample_indices_fn` are mutually exclusive arguments, please use only one!rzAsked 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 videorZz(Video can't be sampled. The `num_frames=z` exceeds `total_num_frames=z`. ) ValueErrorrYrZrTinttorcharange contiguous)rCrNrXrYrZtotal_num_framesindicesrIrIrJ sample_framess,  z BaseVideoProcessor.sample_framesrLvideo_metadatainput_data_formatcCsvt|}|durdd|D}ndgt|}g}|D]}t|tjr1t|tj|}t | }| |q||fS)z: Prepare the input videos for processing. NcSsg|] }|D]}|qqSrIrI).0 batch_listrXrIrIrJ /sz.) r!len isinstancenpndarrayr#r FIRSTr] from_numpyr_append)rCrLrcrdbatch_metadataprocessed_videosrNrIrIrJ_prepare_input_videos$s   z(BaseVideoProcessor._prepare_input_videoscKst|t|jjdgd|jjD] }||t||dq|d}|d}|j|||d\}}|j d i|}|j d i||d}t |t t frVt|n||d<|d|d |jd ||d |S) Nreturn_tensors)captured_kwargsvalid_processor_keysrdrc)rLrcrdresample interpolationr0 data_format)rLrcrI)r r?r<r=r> setdefaultrBr5rq_further_process_kwargs_validate_preprocess_kwargsrir$r\r% _preprocess)rCrLr, kwarg_namerdrcrurIrIrJrK>s&       zBaseVideoProcessor.preprocessdo_convert_rgb do_resizer/ size_divisorrvzF.InterpolationModedo_center_cropr1 do_rescaledo_padrescale_factor do_normalize image_mean image_stddo_sample_framesrrdevicec s|rfddt||D}durfdd|D}t|\}}i}|D]\}}|r4|}|r?j||||d}|||<q)t||}t|\}}i}|D]\}}|ra|| }|| | | ||}|||<qUt||}|r~tj |ddn|}t d|i|dS) Ncs"g|] \}}j||dqS))rXrYrZ)rb)rerNrX)rZrYrCrIrJrg{sz2BaseVideoProcessor._preprocess..csg|]}|qSrI)to)rerN)rrIrJrg)r/rrvr)dimr+)data tensor_type) ziprr7rWresizer" center_croprescale_and_normalizer]stackr)rCrLrcr}r~r/rrvrr1rrrrrrrrZrYrrrgrouped_videosgrouped_videos_indexresized_videos_groupedrTstacked_videosresized_videosprocessed_videos_groupedrprI)rrZrYrCrJr{bs:         zBaseVideoProcessor._preprocessFmainpretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionc Ks||d<||d<||d<||d<|dd}|dur*tdt|dur(td|}|dur2||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/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)r5warningswarn FutureWarningr[get_video_processor_dict from_dict) clsrrrrrrr,rvideo_processor_dictrIrIrJfrom_pretraineds&Z z"BaseVideoProcessor.from_pretrainedsave_directory push_to_hubc Ks|dd}|dur tdt|dddurtd||d<tj|r.t d|dtj |dd |rX|d d}|d | tjj d }|j |fi|}||}|jdurdt|||d tj|t}||td||r|j|||||dd|gS)aq Save an video processor object to the directory `save_directory`, so that it can be re-loaded using the [`~video_processing_utils.VideoProcessorBase.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the video processor JSON file will be saved (will be created if it does not exist). push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs (`dict[str, Any]`, *optional*): Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. rNrrrzProvided path (z#) should be a directory, not a fileT)exist_okcommit_messagerepo_id)configzVideo processor saved in )rr)r5rrrrAr[ospathisfileAssertionErrormakedirssplitsep _create_repo_get_files_timestamps _auto_classrjoinr to_json_filer:info_upload_modified_files) rCrrr,rrrfiles_timestampsoutput_video_processor_filerIrIrJsave_pretrainedsB      z"BaseVideoProcessor.save_pretrainedcKs^|dd}|dd}|dd}|dd}|dd}|dd}|d d} |d d} |d d } |d d} |dd} |durVtdt|durTtd|}d| d}| durc| |d<tro| sotdd} t|}t j |}t j |r|}d}nYt |r|}t|}nNzt}t||||||| ||| | d }Wn:tyd}t||||||| ||| | d }tdYntytytd|d|dtdwz"t|ddd }|}Wdn1swYt|}Wntjytd |d!w|r td"|||fStd"|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=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 )r5rrrr[rr:rstrrrisdirrrrrrOSError warning_once ExceptionopenreadjsonloadsJSONDecodeError)rrr,rrrrrrrrr from_pipelinerris_localresolved_video_processor_filevideo_processor_filereadertextrrIrIrJrSs                        z+BaseVideoProcessor.get_video_processor_dictrcKs|}|dd}d|vrd|vr|d|d<d|vr(d|vr(|d|d<|di|}g}|D]\}}t||rIt|||||q5|D]}||dqLtd||rc||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_kwargsFr/r1NzVideo processor rI)copyr5r7hasattrr8rnr:r)rrr,rvideo_processor to_removerDrErIrIrJrs&    zBaseVideoProcessor.from_dictcCs4t|j}|dd|dd|jj|d<|S)z Serializes this instance to a Python dictionary. 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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. wrrN)rwriter)rCrwriterrIrIrJr%s"zBaseVideoProcessor.to_json_filecCs|jjd|S)N )rHrrrCrIrIrJ__repr__0szBaseVideoProcessor.__repr__ json_filecCsNt|ddd }|}Wdn1swYt|}|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. rrrNrI)rrrr)rrrrrrIrIrJfrom_json_file3s   z!BaseVideoProcessor.from_json_fileAutoVideoProcessorcCsDt|ts|j}ddlmm}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. 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.) rirrtransformers.models.automodelsautorr[r)r auto_class auto_modulerIrIrJregister_for_auto_classFs   z*BaseVideoProcessor.register_for_auto_classvideo_url_or_urlscs@t|trfdd|DSt|trt|Stdt|)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. csg|]}|qSrI) fetch_videos)rexrrIrJrghrz3BaseVideoProcessor.fetch_videos..z=only a single or a list of entries is supported but got type=)rir<rr TypeErrortype)rCrrIrrJr`s  zBaseVideoProcessor.fetch_videos)NNN)NN)NNNNN)NFFNr)F)r)Br __module__ __qualname__rrurrr/rr0r1r~rrrrrr}rrZrYrcrr=model_input_namesr r4rrMrrWrrrdictr\rbrr r<rqrBASE_VIDEO_PROCESSOR_DOCSTRINGrKboolr floatrr{ classmethodrPathLikerrtuplerrrrrrrrrr __classcell__rIrIrGrJr*s$  <  4      B  q=  ,  "r*rrzvideo processor file)object object_class object_files)?rrrrtypingrrrnumpyrjdynamic_module_utilsrimage_processing_utilsrrimage_processing_utils_fastr image_utilsr r r processing_utilsr rutilsrrrrrrrrrrrrrutils.import_utilsr video_utilsrrrr r!r"r#r$r]r%torchvision.transforms.v2r&rRtorchvision.transforms get_loggerrr:rr*r__doc__formatrIrIrIrJsT  < $    C \