import numpy as np
from pathlib import Path
import faiss
from towhee import register
from towhee.operator import PyOperator, SharedType
from towhee.utils.thirdparty.faiss_utils import KVStorage
# from towhee.functional.entity import Entity


@register(output_schema=['result'])
class Faiss(PyOperator):
    """
    Search for embedding vectors in Faiss. Note that the index has data before searching,
    refer to DataCollection Mixin `to_faiss`.

    Args:
        findex (`str` or `faiss.INDEX`):
            The path to faiss index file(defaults to './index.bin') or faiss index.
        kwargs
            The kwargs with index.search, refer to https://github.com/facebookresearch/faiss/wiki. And the parameter `k` defaults to 10.
    """
    def __init__(self, findex, **kwargs):
        self.faiss_index = findex
        self.kwargs = kwargs
        self.kv_storage = None
        if isinstance(findex, str):
            kv_file = findex.strip('./').replace('.', '_kv.')
            index_file = Path(findex)
            self.faiss_index = faiss.read_index(str(index_file))
            if Path(kv_file).exists():
                self.kv_storage = KVStorage(kv_file)

    def __call__(self, query: list):
        if 'k' not in self.kwargs:
            self.kwargs['k'] = 10

        query = np.array([query])
        scores, ids = self.faiss_index.search(query, **self.kwargs)

        ids = ids[0].tolist()
        result = []
        for i in range(len(ids)):
            if self.kv_storage is not None:
                k = self.kv_storage.get(ids[i])
            else:
                k = ids[i]
            result.append([k, scores[0][i]])
            # result.append(Entity(**{'key': k, 'score': scores[0][i]}))
        return result
    
    @property
    def shared_type(self):
        return SharedType.NotShareable