from gensim.corpora.dictionary import Dictionary
import logging
from pyemd import emd
from nltk.corpus import stopwords
import fasttext
import json
import numpy as np

logger = logging.getLogger(__name__)

class FasttextEmbedding:
  def __init__(self, model_path):
    if model_path.endswith('.bin'):
      self.model = fasttext.load_model(model_path)
      self.full = True
    else:
      self.model = np.load(model_path)
      self.full = False
    self.stopwords = stopwords.words('english')

  def __getitem__(self, idx):
    if self.full:
      return self.model.get_word_vector(idx)
    else:
      if idx not in self.model:
        raise ValueError('Word not available.')
      return self.model[idx]