import pandas as pd
from scipy.sparse import csr_matrix
from sklearn.base import BaseEstimator
from typing import Mapping, List, Tuple


class BaseRepresentation(BaseEstimator):
    """The base representation model for fine-tuning topic representations."""

    def extract_topics(
        self,
        topic_model,
        documents: pd.DataFrame,
        c_tf_idf: csr_matrix,
        topics: Mapping[str, List[Tuple[str, float]]],
    ) -> Mapping[str, List[Tuple[str, float]]]:
        """Extract topics.

        Each representation model that inherits this class will have
        its arguments (topic_model, documents, c_tf_idf, topics)
        automatically passed. Therefore, the representation model
        will only have access to the information about topics related
        to those arguments.

        Arguments:
            topic_model: The BERTopic model that is fitted until topic
                         representations are calculated.
            documents: A dataframe with columns "Document" and "Topic"
                       that contains all documents with each corresponding
                       topic.
            c_tf_idf: A c-TF-IDF representation that is typically
                      identical to `topic_model.c_tf_idf_` except for
                      dynamic, class-based, and hierarchical topic modeling
                      where it is calculated on a subset of the documents.
            topics: A dictionary with topic (key) and tuple of word and
                    weight (value) as calculated by c-TF-IDF. This is the
                    default topics that are returned if no representation
                    model is used.
        """
        return topic_model.topic_representations_