o eiS@sZdZddlmZmZddlZddlZddlmZddlm Z ddl m Z GdddZ dS) zGWrappers for Flair embedding classes Authors * Sylvain de Langen 2024 )ListUnionNSentence) Embeddings)fetchc@seZdZdZdeddfddZe  d dd d Ze d fd e e e e e e fd ej dej fddZde dej fddZdS)FlairEmbeddingsz Simple wrapper for generic Flair embeddings. Arguments --------- embeddings : Embeddings The Flair embeddings object. If you do not have one initialized, use :meth:`~FlairEmbeddings.from_hf` instead. embeddingsreturnNcCs ||_dS)N)r )selfr r g/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/lobes/models/flair/embeddings.py__init__s zFlairEmbeddings.__init__./model_checkpoints model.bincOsD|d|ddd}tt|||d}t||g|Ri|S)aFetches and load flair embeddings according to the :func:`speechbrain.utils.fetching.fetch` semantics. Embedding files will be saved into a unique subdirectory in `save_path`. Arguments --------- embeddings_class : class The class to use to initialize the model, e.g. `FastTextEmbeddings`. source : str The location of the model (a directory or HF repo, for instance). save_path : str, optional The saving location for the model (i.e. the root for the download or symlink location). filename : str, optional The filename of the model. The default is the usual filename for this kind of model. *args Extra positional arguments to pass to the flair class constructor **kwargs Extra keyword arguments to pass to the flair class constructor Returns ------- FlairEmbeddings z /flair-emb--/z--)savedir)replacestrrr)embeddings_classsource save_pathfilenameargskwargstarget local_pathr r r from_hfs$zFlairEmbeddings.from_hf)inputs pad_tensorcst|tr tddd|D}|j|tj|jj  ddd|D}t dd|Dfdd|D}t |S) aSExtract embeddings for a batch of sentences. Arguments --------- inputs : list of sentences (str or list of tokens) Sentences to embed, in the form of batches of lists of tokens (list of str) or a str. In the case of token lists, tokens do *not* need to be already tokenized for this specific sequence tagger. However, a token may be considered as a single word. Similarly, out-of-vocabulary handling depends on the underlying embedding class. pad_tensor : torch.Tensor, optional What embedding tensor (of shape `[]`, living on the same device as the embeddings to insert as padding. Returns ------- torch.Tensor Batch of shape `[len(inputs), max_len, embed_size]` z.Expected a list of sentences, not a single strcSsg|]}t|qSr r.0sentencer r r esz,FlairEmbeddings.__call__..rcSs g|] }tdd|DqS)cSsg|]}|jqSr ) embedding)r"tokenr r r r$osz7FlairEmbeddings.__call__...)torchstackr!r r r r$nscss|]}|dVqdS)rN)sizer"embr r r rsz+FlairEmbeddings.__call__..c s0g|]}tj||ddgddqS)rr)dim)r'catrepeatr)r* longest_embr r r r$ss ) isinstancer ValueErrorr embedtoflairdevice broadcast_toembedding_length unsqueezemaxr'r()r rr sentences sentence_embsr r0r __call__Gs$     zFlairEmbeddings.__call__wordcCs||gddddfS)a:Embeds a single word. Arguments --------- word : str Word to embed. Out-of-vocabulary handling depends on the underlying embedding class. Returns ------- torch.Tensor Embedding for a single word, of shape `[embed_size]` rNr )r r?r r r embed_word{szFlairEmbeddings.embed_word)rr)r r)__name__ __module__ __qualname____doc__rr staticmethodrr'zerosrrrTensorr>r@r r r r rs$  * 4r) rDtypingrrr6r' flair.datarflair.embeddingsrspeechbrain.utils.fetchingrrr r r r s