from pulp import lpSum
from random import randint, sample

def build_constraints(solver_run, problem, items):
  total_form_items = solver_run.total_form_items
  constraints = solver_run.constraints

  for constraint in constraints:
    attribute = constraint.reference_attribute
    min = constraint.minimum
    max = constraint.maximum

    if attribute.type == 'metadata':
      con = dict(zip([item.id for item in solver_run.items],
                    [item.attribute_exists(attribute)
                    for item in solver_run.items]))
      problem += lpSum([con[item.id]
                      * items[item.id]
                      for item in solver_run.items]) >= round(total_form_items * (min / 100)), f'{attribute.id} - {attribute.value} - min'
      problem += lpSum([con[item.id]
                      * items[item.id]
                      for item in solver_run.items]) <= round(total_form_items * (max / 100)), f'{attribute.id} - {attribute.value} - max'
    elif attribute.type == 'bundle':
      bundles = solver_run.bundles(attribute.id)
      total_bundles = randint(constraint.minimum, constraint.maximum)
      selected_bundles = sample(bundles, total_bundles)

      for bundle in selected_bundles:
        problem += lpSum([items[item.id] for item in solver_run.items if getattr(item, bundle["type"], None) == bundle['id']]) == bundle['count'], f'Bundle constraint for {bundle["type"]} ({bundle["id"]})'


  return problem