After reading multiple papers on stuff like polyomino and coverings etc over the weekend, I sat down to formulate an ILP approach. All the way through I had at the back of my mind “surely he would not expect people to solve something which requires reading research papers, there must be some angle to this which makes it easier”. I don’t think I have ever been more right in my life and I am really glad I made the obvious fail and succeed checks based on areas lol.

import numpy as np
import itertools as it
from pathlib import Path
from time import time

cwd = Path(__file__).parent.resolve()

def timing(f):
  def wrap(*args, **kw):
    ts = time()
    result = f(*args, **kw)
    te = time()
    print(f"func{f.__name__} args: {args} took: {te-ts:.4f} sec")

    return result
  return wrap

def parse_input(file_path):
  with file_path.open("r") as fp:
    data = list(map(str.strip, fp.readlines()))

  objects = []
  for i in range(6):
    i0 = data.index(f"{i}:")
    obj = np.array(list(map(list, data[i0+1:i0+4])))
    obj[obj=='#']=1
    obj[obj=='.']=0
    objects.append(obj.astype(int))

  i0 = data.index("5:")+5
  placements = []

  for line in data[i0:]:
    dims = list(map(int, line.split(':')[0].split('x')))
    nobjs = list(map(int, line.split(': ')[-1].split(' ')))
    placements.append((dims, nobjs))

  return objects, placements

@timing
def solve_problem(file_name):

  ref_objects, placements = parse_input(Path(cwd, file_name))
  areas = [np.count_nonzero(obj==1) for obj in ref_objects]

  counter_succesful = 0

  for grid_shape, nobjs in placements:
    obj_area = np.sum(np.array(nobjs)*areas)
    grid_area = np.prod(grid_shape)
    worse_area =  np.sum(np.array(nobjs)*9)

    if worse_area<=grid_area:
      counter_succesful += 1
      continue

    if obj_area>grid_area:
      continue

  return counter_succesful

if __name__ == "__main__":

  assert solve_problem("input") == 583