from tqdm import tqdm

from sentence_transformers import SentenceTransformer
from cde_benchmark.embedders.base_embedder import Embedder


class NaiveContextualEmbedder(Embedder):
    def __init__(
        self,
        model: SentenceTransformer = None,
        batch_size: int = 16,
        show_progress_bar: bool = True,
    ):
        super().__init__(is_contextual_model=True)
        self.model: SentenceTransformer = model
        self.show_progress_bar = show_progress_bar
        self.batch_size = batch_size

    def embed_queries(self, queries):
        return self.model.encode(
            queries,
            show_progress_bar=self.show_progress_bar,
            batch_size=self.batch_size,
        )

    def embed_documents(self, documents):
        # documents is a list of list of documents
        # This is just for the demo, but here it's not contextual at all
        embeddings = []
        for document in tqdm(documents):
            embeddings.append(
                self.model.encode(
                    document,
                    show_progress_bar=False,
                    batch_size=self.batch_size,
                )
            )
        return embeddings