# Path: misc/priority_queue.py
# class Queue(object): # FIFO
#   def __init__(self, array=[]):
#     self.queue = deque(array)
#   def peek(self):
#     return self.queue[0]
#   def push(self, _, element):
#     self.queue.append(element)
#   def pop(self):
#     return self.queue.popleft()
#   def empty(self):
#     return len(self) == 0
#   def __len__(self):
#     return len(self.queue)
#   def __repr__(self):
#     return '{}{}'.format(self.__class__.__name__, list(self.queue))
# 
# class FIFOPriorityQueue(PriorityQueue):
#   sign = 1


import random
from misc.priority_queue import Queue, FIFOPriorityQueue
                new_achieved.add(condition)
                new_causal_links += [(None, condition)]
              else:
                new_agenda.append(condition)

          consts = self.constraints
          for condition in operator.cond():
            consts = add_constraint((condition, subgoal), consts)
          yield PartialPlan(self.initial,
                            new_achieved,
                            new_actions,
                            consts,
                            new_agenda,
                            new_causal_links)

# TODO - way of weighting the partial order planning progress

def get_initial_plan(initial, goal):
  achieved = {atom for atom in initial.values.items()}
  agenda = []
  for condition in goal.cond():
    if condition not in achieved:
      if initial[condition[0]] == condition[1]:
        achieved.add(condition)
      else:
        agenda.append(condition)
  return PartialPlan(initial, achieved, set(), set(), agenda, [(None, atom) for atom in achieved])




# Path: misc/priority_queue.py
# class Queue(object): # FIFO
#   def __init__(self, array=[]):
#     self.queue = deque(array)
#   def peek(self):
#     return self.queue[0]
#   def push(self, _, element):
#     self.queue.append(element)
#   def pop(self):
#     return self.queue.popleft()
#   def empty(self):
#     return len(self) == 0
#   def __len__(self):
#     return len(self.queue)
#   def __repr__(self):
#     return '{}{}'.format(self.__class__.__name__, list(self.queue))
# 
# class FIFOPriorityQueue(PriorityQueue):
#   sign = 1


# Path: sas/relaxed_pop.py
import random
from misc.priority_queue import Queue, FIFOPriorityQueue

                new_achieved.add(condition)
                new_causal_links += [(None, condition)]
              else:
                new_agenda.append(condition)

          consts = self.constraints
          for condition in operator.cond():
            consts = add_constraint((condition, subgoal), consts)
          yield PartialPlan(self.initial,
                            new_achieved,
                            new_actions,
                            consts,
                            new_agenda,
                            new_causal_links)

# TODO - way of weighting the partial order planning progress

def get_initial_plan(initial, goal):
  achieved = {atom for atom in initial.values.items()}
  agenda = []
  for condition in goal.cond():
    if condition not in achieved:
      if initial[condition[0]] == condition[1]:
        achieved.add(condition)
      else:
        agenda.append(condition)
  return PartialPlan(initial, achieved, set(), set(), agenda, [(None, atom) for atom in achieved])




def relaxed_pop(initial, goal, operators, QueueClass=FIFOPriorityQueue):