from queue import PriorityQueue

DEFAULT_COST_VALUE = 1

def is_border(x, y, max_x, max_y):
    return 0 <= x < max_x and 0 <= y < max_y

def is_obstacle(x, y, obstacles):
    return (x, y) in obstacles

def succ(current_state, max_x, max_y, obstacles):
    successors = []
    x, y, direction = current_state

    # Akcja: Do przodu
    direction_x, direction_y = {'N': (0, -1), 'E': (1, 0), 'S': (0, 1), 'W': (-1, 0)}[direction] # Słownik przesunięć w zależności od kierunku
    new_x, new_y = x + direction_x, y + direction_y

    if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
        successors.append(((new_x, new_y, direction), 'Go Forward'))

    # Akcja: Obrót w lewo
    left_turns = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'} # Słownik kierunków po obrocie w lewo
    successors.append(((x, y, left_turns[direction]), 'Turn Left')) 

    # Akcja: Obrót w prawo
    right_turns = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'} # Słownik kierunków po obrocie w prawo
    successors.append(((x, y, right_turns[direction]), 'Turn Right'))

    return successors

def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
    fringe = PriorityQueue()
    explored = set()

    fringe.put((0, (istate, None , None)))

    while not fringe.empty():
        _, node = fringe.get()
        state, _, _ = node

        if goaltest(state, goal):
            return build_action_sequence(node)

        explored.add(state)

        successors = succ(state, max_x, max_y, obstacles)

        for new_state, action in successors:
            new_node = (new_state, node, action)

            p_new_state = current_cost(node, cost_map) + heuristic(state, goal)

            if not is_state_in_queue(new_state, fringe) and new_state not in explored:
                fringe.put((p_new_state, new_node))

            elif is_state_in_queue(new_state, fringe):
                for i, (p_existing_state, (existing_state, _, _)) in enumerate(fringe.queue):
                    if existing_state == new_state and p_existing_state > p_new_state:
                        fringe.queue[i] = (p_new_state, new_node)
                    else:
                        break

    return False

def is_state_in_queue(state, queue):
    for _, (s, _, _) in queue.queue:
        if s == state:
            return True
    return False    

def build_action_sequence(node):
    actions = []
    while node[1] is not None:  # Dopóki nie dojdziemy do korzenia
        _, parent, action = node
        actions.append(action)
        node = parent
    actions.reverse()
    return actions

def goaltest(state, goal):
    x, y, _ = state
    goal_x, goal_y = goal
    return (x,y) == (goal_x, goal_y)

def current_cost(node, cost_map):
    cost = 0
    while node[1] is not None:  # Dopóki nie dojdziemy do korzenia
        _, parent, action = node
        # Dodaj koszt pola z mapy kosztów tylko jeśli akcja to "Forward"
        if action == 'Go Forward':
            state, _, _ = node
            cost += cost_map.get(state[:2], DEFAULT_COST_VALUE)  # Pobiera koszt przejścia przez dane pole, a jeśli koszt nie jest zdefiniowany to bierze wartość domyślną

        if action == 'Turn Right' or action == 'Turn Left':
            cost += DEFAULT_COST_VALUE
            
        node = parent  # Przejdź do rodzica
    return cost

def heuristic(state, goal):
    x, y, _ = state
    goal_x, goal_y = goal
    return abs(x - goal_x) + abs(y - goal_y)  # Odległość Manhattana do celu

def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
    adult_animal_cost = 15  # Default : 15
    baby_animal_cost = 10   # Default : 10
    puddle_cost = 50        # Default : 50
    bush_cost = 20          # Default : 20

    for animal in Animals:
        if animal.adult:
            # cost_map[(animal.x + 1, animal.y + 1)] = adult_animal_cost
            # cost_map[(animal.x + 1, animal.y)] = adult_animal_cost
            # cost_map[(animal.x, animal.y + 1)] = adult_animal_cost
            cost_map[(animal.x, animal.y)] = adult_animal_cost
        else:
            cost_map[(animal.x, animal.y)] = baby_animal_cost

    for terrain_obstacle in Terrain_Obstacles:
        if terrain_obstacle.type == 'puddle':
            cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = puddle_cost
        else:
            cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost

    return cost_map