import numpy as np
from typing import List
from bertopic.backend._base import BaseEmbedder
from bertopic.backend._utils import select_backend


class WordDocEmbedder(BaseEmbedder):
    """ Combine a document- and word-level embedder
    """
    def __init__(self,
                 embedding_model,
                 word_embedding_model):
        super().__init__()

        self.embedding_model = select_backend(embedding_model)
        self.word_embedding_model = select_backend(word_embedding_model)

    def embed_words(self,
                    words: List[str],
                    verbose: bool = False) -> np.ndarray:
        """ Embed a list of n words into an n-dimensional
        matrix of embeddings

        Arguments:
            words: A list of words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Word embeddings with shape (n, m) with `n` words
            that each have an embeddings size of `m`

        """
        return self.word_embedding_model.embed(words, verbose)

    def embed_documents(self,
                        document: List[str],
                        verbose: bool = False) -> np.ndarray:
        """ Embed a list of n words into an n-dimensional
        matrix of embeddings

        Arguments:
            document: A list of documents to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document embeddings with shape (n, m) with `n` documents
            that each have an embeddings size of `m`
        """
        return self.embedding_model.embed(document, verbose)