"""
Index all of the FAISS datasets
"""

import os
import click

from app.utils.file_utils import load_recipe, load_csv
from app.settings import app_cfg as cfg

@click.command()
@click.pass_context
def cli(ctx):
  """train the FAISS index"""
  
  recipe = {
    "dim": 128,
    "factory_type": "Flat"
  }

  datasets = []
  for fn in glob.iglob(os.path.join(cfg.DIR_FAISS_DATASETS, "*")):
    name = os.path.basename(fn)
    recipe_fn = os.path.join(cfg.DIR_FAISS_RECIPES, name + ".json")
    if os.path.exists(recipe_fn):
      train(name, load_recipe(recipe_fn))
    else:
      train(name, recipe)

def train(name, recipe):
  vec_fn = os.path.join(cfg.DIR_FAISS_DATASETS, name, "vecs.csv")
  index_fn = os.path.join(cfg.DIR_FAISS_INDEXES, name + ".index")

  index = faiss.index_factory(recipe.dimension, recipe.factory)

  keys, rows = file_utils.load_csv_safe(vec_fn)
  feats = np.array([ float(x[1].split(",")) for x in rows]).astype('float32')
  n, d = feats.shape

  train_start = time.time()
  index.train(feats)
  train_end = time.time()
  train_time = train_end - train_start
  print("{} train time: {:.1f}s".format(name, train_time))

  faiss.write_index(index, index_fn)