# Base Dependencies

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

import numpy as np

from typing import List

# Local Dependencies

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

from models import RelationCollection

# 3rd-Party Dependencies

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

from gensim.models import KeyedVectors

from sklearn.base import BaseEstimator

class SentenceEmbedding(BaseEstimator):

    """

    Sentence Embedding

    Obtains the word embedding indexes of the sentence.

    Source: 

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

    """

    def __init__(self, model: KeyedVectors):

        self.model = model

    def get_feature_names(self, input_features=None):

        return ["sentence_embedding"]

    def create_sentence_embedding(self, collection: RelationCollection) -> np.array:

        sent_embs = []

        for doc in collection.tokens:

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

            sent_embs.append(self.model.get_mean_vector(sent_tokens))

        return np.array(sent_embs)

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

        return self

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

        return self.create_sentence_embedding(x)

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

        return self.create_sentence_embedding(x)