import numpy as np
from tqdm import tqdm
from typing import List

from bertopic.backend import BaseEmbedder


class USEBackend(BaseEmbedder):
    """ Universal Sentence Encoder

    USE encodes text into high-dimensional vectors that
    are used for semantic similarity in BERTopic.

    Arguments:
        embedding_model: An USE embedding model

    Examples:

    ```python
    import tensorflow_hub
    from bertopic.backend import USEBackend

    embedding_model = tensorflow_hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
    use_embedder = USEBackend(embedding_model)
    ```
    """
    def __init__(self, embedding_model):
        super().__init__()

        try:
            embedding_model(["test sentence"])
            self.embedding_model = embedding_model
        except TypeError:
            raise ValueError("Please select a correct USE model: \n"
                             "`import tensorflow_hub` \n"
                             "`embedding_model = tensorflow_hub.load(path_to_model)`")

    def embed(self,
              documents: List[str],
              verbose: bool = False) -> np.ndarray:
        """ Embed 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`
        """
        embeddings = np.array(
            [
                self.embedding_model([doc]).cpu().numpy()[0]
                for doc in tqdm(documents, disable=not verbose)
            ]
        )
        return embeddings
