WiN RddlZddlmZddlmZddlmZddlmZGddeZ y)N)tqdm)List) BaseEmbedder)Word2VecKeyedVectorscXeZdZdZdeffd Zddeedede jfdZ xZ S) GensimBackendaGensim Embedding Model. The Gensim embedding model is typically used for word embeddings with GloVe, Word2Vec or FastText. Arguments: embedding_model: A Gensim embedding model Examples: ```python from bertopic.backend import GensimBackend import gensim.downloader as api ft = api.load('fasttext-wiki-news-subwords-300') ft_embedder = GensimBackend(ft) ``` embedding_modelcft|t|tr||_yt d)Nz|Please select a correct Gensim model: `import gensim.downloader as api` `ft = api.load('fasttext-wiki-news-subwords-300')`)super__init__ isinstancerr ValueError)selfr __class__s d/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/bertopic/backend/_gensim.pyr zGensimBackend.__init__s4  o'; <#2D E  documentsverbosereturncV|jjtt|jjj d}t j|}g}t|| ddD]}|jDcgc]5}||jjvr|jj|7}}t|dkDr'|jt j|d|j|t j|}|Scc}w)aEmbed a list of n documents/words into an n-dimensional matrix of embeddings. Arguments: documents: A list of documents or words to be embedded verbose: Controls the verbosity of the process Returns: Document/words embeddings with shape (n, m) with `n` documents/words that each have an embeddings size of `m` rT)disablepositionleave)axis)r get_vectornextiter index_to_keyshapenpzerosrsplit key_to_indexlenappendmeanarray) rrr vector_shape empty_vector embeddingsdocword embeddings rembedzGensimBackend.embed's++66tDAUAUAbAbr<s );=L=r