U ,-eq@s.dZddlZddlZddlZddlZddlmZddlmZm Z m Z m Z m Z m Z ddlZddlmZddlmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$ere rddl%Z%e#&e'Z(e dZ)Gd d d eZ*Gd d d eZ+ee+j,e+_,e+j,jdk r*e+j,jj-d ddde+j,_dS)zI Feature extraction saving/loading class for common feature extractors. N)UserDict) TYPE_CHECKINGAnyDictOptionalTupleUnion)custom_object_save)FEATURE_EXTRACTOR_NAMEPushToHubMixin TensorTypeadd_model_info_to_auto_map cached_file copy_func download_urlis_flax_available is_jax_tensoris_numpy_arrayis_offline_mode is_remote_urlis_tf_availableis_torch_availableis_torch_deviceis_torch_dtypeloggingrequires_backendsZSequenceFeatureExtractorcseZdZdZdeeeefedee fdfdd Z eeedddZ ed d d Z d d Z ddZddZddZddZdeeee fdddZddddZZS) BatchFeatureaI Holds the output of the [`~SequenceFeatureExtractor.pad`] and feature extractor specific `__call__` methods. This class is derived from a python dictionary and can be used as a dictionary. Args: data (`dict`): Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask', etc.). tensor_type (`Union[None, str, TensorType]`, *optional*): You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization. N)data tensor_typecst||j|ddS)Nr)super__init__convert_to_tensors)selfrr __class__f/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/feature_extraction_utils.pyr"Ls zBatchFeature.__init__)itemreturncCs t|tr|j|StddS)z If the key is a string, returns the value of the dict associated to `key` ('input_values', 'attention_mask', etc.). zRIndexing with integers is not available when using Python based feature extractorsN) isinstancestrrKeyErrorr$r)r'r'r( __getitem__Ps  zBatchFeature.__getitem__)r)cCs*z |j|WStk r$tYnXdSN)rr-AttributeErrorr.r'r'r( __getattr__Zs zBatchFeature.__getattr__cCs d|jiSNrrr$r'r'r( __getstate__`szBatchFeature.__getstate__cCsd|kr|d|_dSr3r4)r$stater'r'r( __setstate__cszBatchFeature.__setstate__cCs |jSr0)rkeysr5r'r'r(r9hszBatchFeature.keyscCs |jSr0)rvaluesr5r'r'r(r:lszBatchFeature.valuescCs |jSr0)ritemsr5r'r'r(r;pszBatchFeature.itemsr cs|dkr |St|tst|}|tjkrLts6tdddl}|j|j}nv|tjkrt sdtdddl fddj}nB|tj krt stdddl m}|jt}nd fdd t}|D]L\}}z||s|}|||<Wq|d krtd td YqXq|S) a5 Convert the inner content to tensors. Args: tensor_type (`str` or [`~utils.TensorType`], *optional*): The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If `None`, no modification is done. NzSUnable to convert output to TensorFlow tensors format, TensorFlow is not installed.rzMUnable to convert output to PyTorch tensors format, PyTorch is not installed.cs>t|ttfr4t|dkr4t|dtjr4t|}|S)Nr)r+listtuplelennpndarrayarraytensor)value)torchr'r( as_tensors* z2BatchFeature.convert_to_tensors..as_tensorzEUnable to convert output to JAX tensors format, JAX is not installed.csnt|ttfr`t|dtttjfr`dd|D}tt|dkr`|dkr`dd|Dtd}tj||dS)NrcSsg|] }t|qSr')r>.0valr'r'r( szFBatchFeature.convert_to_tensors..as_tensor..r cSsg|]}t|qSr')r?asarrayrFr'r'r(rIs)dtype) r+r<r=r?r@r>setobjectrJ)rCrKZ value_lens)rEr'r(rEs $Zoverflowing_valueszKUnable to create tensor returning overflowing values of different lengths. zUnable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.)N)r+r Z TENSORFLOWr ImportErrorZ tensorflowZconstant is_tensorZPYTORCHrrDZJAXrZ jax.numpynumpyrArrr; ValueError)r$rtfrOZjnpkeyrCrBr')rErDr(r#ssL          zBatchFeature.convert_to_tensorsr*c Ost|dgddl}i}|d}|dkr~t|dkr~|d}t|rHn6t|tsdt|sdt|trj|}nt dt|d| D]F\}}| |r|j ||||<q|dk r|j |d||<q|||<q||_ |S)a  Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in different `dtypes` and sending the `BatchFeature` to a different `device`. Args: args (`Tuple`): Will be passed to the `to(...)` function of the tensors. kwargs (`Dict`, *optional*): Will be passed to the `to(...)` function of the tensors. Returns: [`BatchFeature`]: The same instance after modification. rDrNdevicez*Attempting to cast a BatchFeature to type z. This is not supported.)rU)rrDgetr>rr+r,rintrQr;Zis_floating_pointtor) r$argskwargsrDZnew_datarUargkvr'r'r(rXs&    zBatchFeature.to)NN)N)__name__ __module__ __qualname____doc__rrr,rrr r"r/r2r6r8r9r:r;r#rX __classcell__r'r'r%r(r=s, Irc @sJeZdZdZdZddZedddZed&e ee j fe e ee j fe e e e ee fed d d Zd'e ee j fe d ddZee ee j feeeefeeeffdddZeeeefedddZeeefdddZee ee j fedddZedddZe ee j fddd Zd!d"Zed(d$d%ZdS))FeatureExtractionMixinz This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature extractors. Nc Ksx|dd|_|D]\\}}zt|||Wqtk rp}z$td|d|d||W5d}~XYqXqdS)z'Set elements of `kwargs` as attributes.processor_classNz Can't set z with value z for )pop_processor_classr;setattrr1loggererror)r$rZrSrCerrr'r'r(r"szFeatureExtractionMixin.__init__)rdcCs ||_dS)z%Sets processor class as an attribute.N)rf)r$rdr'r'r(_set_processor_classsz+FeatureExtractionMixin._set_processor_classFmain)pretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionc Ks||d<||d<||d<||d<|dd}|dk rTtdt|dk rPtd|}|dk rd||d <|j|f|\} }|j| f|S) az Instantiate a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a feature extractor, *e.g.* a derived class of [`SequenceFeatureExtractor`]. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - a path to a *directory* containing a feature extractor file saved using the [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] method, e.g., `./my_model_directory/`. - a path or url to a saved feature extractor 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 feature extractor 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 feature extractor files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. 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 feature extractor object. If `True`, then this functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored. kwargs (`Dict[str, Any]`, *optional*): The values in kwargs of any keys which are feature extractor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is controlled by the `return_unused_kwargs` keyword parameter. Returns: A feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`]. Examples: ```python # We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a # derived class: *Wav2Vec2FeatureExtractor* feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base-960h" ) # Download feature_extraction_config from huggingface.co and cache. feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "./test/saved_model/" ) # E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')* feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("./test/saved_model/preprocessor_config.json") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False ) assert feature_extractor.return_attention_mask is False feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False, return_unused_kwargs=True ) assert feature_extractor.return_attention_mask is False assert unused_kwargs == {"foo": False} ```rnrorprruse_auth_tokenNVThe `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.V`token` and `use_auth_token` are both specified. Please set only the argument `token`.rq)rewarningswarn FutureWarningrQget_feature_extractor_dict from_dict) clsrmrnrorprqrrrZrsfeature_extractor_dictr'r'r(from_pretraineds&Y z&FeatureExtractionMixin.from_pretrained)save_directory push_to_hubc Ks|dd}|dk r@tdt|dddk r8td||d<tj|r\t d|dtj |dd |r|d d}|d | tjj d }|j |f|}||}|jdk rt|||d tj|t}||td||r |j|||||dd|gS)az Save a feature_extractor object to the directory `save_directory`, so that it can be re-loaded using the [`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the feature extractor 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. rsNrtrqruzProvided path (z#) should be a directory, not a fileT)exist_okcommit_messagerepo_id)configzFeature extractor saved in )rrq)rervrwrxrVrQospathisfileAssertionErrormakedirssplitsepZ _create_repoZ_get_files_timestamps _auto_classr joinr to_json_filerhinfoZ_upload_modified_files) r$r~rrZrsrrZfiles_timestampsZoutput_feature_extractor_filer'r'r(save_pretrainedqsB      z&FeatureExtractionMixin.save_pretrained)rmr*c KsR|dd}|dd}|dd}|dd}|dd}|dd}|d d} |d d} |dk rtd t|dk rtd |}|d d} |dd} d| d} | dk r| | d<tr| stdd} t|}t j |}t j |rt j |t }t j |r|}d}nt|r2|}t|}nnt }z t||||||| || | d }WnHtk rnYn2tk rtd|d|dt dYnXz0t|ddd}|}W5QRXt|}Wn(tjk rtd|dYnX|rtd|ntd|d|d |krJ|sJt|d ||d <||fS)!a6 From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`] 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. Returns: `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the feature extractor object. rnNroFresume_downloadproxiesrqrsrprrrtruZ_from_pipelineZ _from_autofeature extractor) file_typefrom_auto_classZusing_pipelinez+Offline mode: forcing local_files_only=TrueT)rnrorrrprq user_agentrrz"Can't load feature extractor 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. 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Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~feature_extraction_utils.FeatureExtractionMixin.to_dict`] method. kwargs (`Dict[str, Any]`): Additional parameters from which to initialize the feature extractor object. Returns: [`~feature_extraction_utils.FeatureExtractionMixin`]: The feature extractor object instantiated from those parameters. return_unused_kwargsFNzFeature extractor )rer;hasattrrgappendrhr)r{r|rZrZfeature_extractorZ to_removerSrCr'r'r(rzs     z FeatureExtractionMixin.from_dictrTcCst|j}|jj|d<|S)z Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this feature extractor instance. Zfeature_extractor_type)copydeepcopy__dict__r&r^)r$outputr'r'r(to_dict?s  zFeatureExtractionMixin.to_dict) json_filer*c Cs6t|ddd}|}W5QRXt|}|f|S)a Instantiates a feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`] from the path to a JSON file of parameters. 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