a  di@s&dZddlZddlZddlZddlmZddlmZmZm Z m Z m Z m Z ddl ZddlmZddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#ererddl$Z$e"%e&Z'e dZ(Gd d d eZ)Gd d d eZ*ee*j+e*_+e*j+jdur"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__n/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/transformers/feature_extraction_utils.pyr"Ks 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$r)r'r'r( __getitem__Os  zBatchFeature.__getitem__)r)cCs(z |j|WSty"tYn0dSN)rr-AttributeErrorr.r'r'r( __getattr__Ys  zBatchFeature.__getattr__cCs d|jiSNrrr$r'r'r( __getstate___szBatchFeature.__getstate__cCsd|vr|d|_dSr3r4)r$stater'r'r( __setstate__bszBatchFeature.__setstate__cCs |jSr0)rkeysr5r'r'r(r9gszBatchFeature.keyscCs |jSr0)rvaluesr5r'r'r(r:kszBatchFeature.valuescCs |jSr0)ritemsr5r'r'r(r;oszBatchFeature.itemsr cs|dur |St|tst|}|tjkrLts6tdddl}|j|j}nv|tjkrt sdtdddl fddj}nB|tj krt stdddl m}|jt}nd fdd t}|D]L\}}z||s|}|||<Wq|d krtd td Yq0q|S) a5 Convert the inner content to tensors. Args: tensor_type (`str` or [`~utils.TensorType`], *optional*): The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If `None`, no modification is done. 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This should support casting in different `dtypes` and sending the `BatchFeature` to a different `device`. Args: args (`Tuple`): Will be passed to the `to(...)` function of the tensors. kwargs (`Dict`, *optional*): Will be passed to the `to(...)` function of the tensors. Returns: [`BatchFeature`]: The same instance after modification. rDrNdevicez*Attempting to cast a BatchFeature to type z. This is not supported.)rV)rrDgetr>rr+r,rintrRr;Zis_floating_pointtor) r$argskwargsrDZnew_datarVargkvr'r'r(rYs&    zBatchFeature.to)NN)N)__name__ __module__ __qualname____doc__rrr,rrr r"r/r2r6r8r9r:r;r#rY __classcell__r'r'r%r(r<s, Irc@s&eZdZdZdZddZedddZee ee j fe dd d Z d$e ee j fed d dZee ee j feeeefeeeffdddZeeeefe dddZeeefdddZee ee j fe dddZedddZe ee j fdddZdd Zed%d"d#ZdS)&FeatureExtractionMixinz This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature extractors. Nc Ksz|dd|_|D]^\}}zt|||Wqtyr}z,td|d|d||WYd}~qd}~00qdS)z'Set elements of `kwargs` as attributes.processor_classNz Can't set z with value z for )pop_processor_classr;setattrr1loggererror)r$r[rTrCerrr'r'r(r"szFeatureExtractionMixin.__init__)recCs ||_dS)z%Sets processor class as an attribute.N)rg)r$rer'r'r(_set_processor_classsz+FeatureExtractionMixin._set_processor_class)pretrained_model_name_or_pathr*cKs(|j|fi|\}}|j|fi|S)a 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. use_auth_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} ```)get_feature_extractor_dict from_dict)clsrmr[feature_extractor_dictr'r'r(from_pretrainedsRz&FeatureExtractionMixin.from_pretrainedF)save_directory push_to_hubcKstj|rtd|dtj|dd|rp|dd}|d|tjjd}|j|fi|}| |}|j durt |||d tj |t }||td ||r|j|||||d d |gS) a[ 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. 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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 )rfr;hasattrrhappendrir)rprqr[rZfeature_extractorZ to_removerTrCr'r'r(ros    z FeatureExtractionMixin.from_dictrUcCst|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*cCsNt|ddd}|}Wdn1s,0Yt|}|fi|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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