# Base Dependencies

# ----------------

import numpy as np

from typing import List, Tuple

# Local Dependencies

# ------------------

from models import RelationCollection

# 3rd-Party Dependencies

# ----------------------

from gensim.models import KeyedVectors

from spacy.tokens import Doc

from sklearn.base import BaseEstimator

# Constants

# ---------

from constants import DATASETS

class EntityEmbedding(BaseEstimator):

    """

    Entity Embedding

    Obtains the vectors indexes of the two entities in the relation.

    Source: 

        Alimova and Tutubalina (2020) - Multiple features for clinical relation extraction: A machine learning approach

    """

    def __init__(self, dataset: str, model: KeyedVectors):

        if dataset not in DATASETS:

            raise ValueError("unsupported dataset '{}'".format(dataset))

        self.dataset = dataset

        self.model = model

    def get_feature_names(self, input_features=None):

        return ["ent_emb"]

    def create_entity_embedding(

        self, collection: RelationCollection

    ) -> Tuple[np.array, np.array]:

        e1_embs = []

        e2_embs = []

        entities1: List[Doc] = collection.entities1_tokens

        entities2: List[Doc] = collection.entities2_tokens

        assert len(entities1) == len(entities2)

        for e1, e2 in zip(entities1, entities2):

            e1_tokens: List[str] = list(map(lambda t: t.text.lower(), e1))

            e2_tokens: List[str] = list(map(lambda t: t.text.lower(), e2))

            e1_embs.append(self.model.get_mean_vector(e1_tokens))

            e2_embs.append(self.model.get_mean_vector(e2_tokens))

        return np.array(e1_embs), np.array(e2_embs)

    def fit(self, x: RelationCollection, y=None):

        return self

    def transform(self, x: RelationCollection) -> Tuple[np.array, np.array]:

        return self.create_entity_embedding(x)

    def fit_transform(self, x: RelationCollection, y=None) -> Tuple[np.array, np.array]:

        return self.create_entity_embedding(x)