import os
from pathlib import Path
from glob import glob
import unidecode
import difflib

from app.settings import types
from app.utils import logger_utils

log = logger_utils.Logger.getLogger()

# Get list of names based on Dataset type
def get_names(enum_dataset):
  if enum_dataset == types.Dataset.LFW:
    dir_lfw = '/data_store_hdd/datasets/people/lfw/media/original/'
    names_orig = [x for x in os.listdir(dir_lfw)]
    names_query = [x.replace('_', ' ') for x in names_orig]
    result = {'names_orig': names_orig, 'names_query': names_query}
  elif enum_dataset == types.Dataset.YOUTUBE_FACES:
    names = [x for x in names if 'labeled faces.txt' not in x]
  else:
    log.warn(f'{enum_dataset} not yet implemented')
    result = {}
  return result

def similarity(a, b):
  return difflib.SequenceMatcher(a=a.lower(), b=b.lower()).ratio()

def names_match(name_a, name_b, threshold=0.9, as_float=False, compound_score=False):
  '''Returns boolean if names are similar enough
  '''
  # strip spaces and split names into list of plain text words
  name_a_clean = [unidecode.unidecode(x.strip().lower()) for x in name_a.strip().split(' ')]
  name_b_clean = [unidecode.unidecode(x.strip().lower()) for x in name_b.strip().split(' ')]
  
  # assign short long vars
  len_a = len(name_a_clean)
  len_b = len(name_b_clean)
  len_min = min(len_a, len_b)
  len_max = max(len_a, len_b)
  
  # compute scores
  scores = []
  for i in range(len(name_a_clean)):
    word_a = name_a_clean[i]
    subscores = []
    for j in range(len(name_b_clean)):
      word_b = name_b_clean[j]
      score = similarity(word_a, word_b)
      subscores.append(score)
    scores.append(subscores)
  
  # return result
  ratio_similar = sum(max(x) for x in scores) / len_min

  if compound_score:
    # combine with any missing letters/words
    letters_a = sum(len(x) for x in name_a_clean)
    letters_b = sum(len(x) for x in name_b_clean)
    ratio_letters = min(letters_a, letters_b) / max(letters_a, letters_b)
    score = (0.8 * ratio_similar) + (0.2 * ratio_letters)
  else:
    score = ratio_similar

  if as_float:
    return score
  else:
    return score > threshold