# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""

import util

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem.
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state.
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples, (successor,
        action, stepCost), where 'successor' is a successor to the current
        state, 'action' is the action required to get there, and 'stepCost' is
        the incremental cost of expanding to that successor.
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.
        The sequence must be composed of legal moves.
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
    sequence of moves will be incorrect, so only use this for tinyMaze.
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s, s, w, s, w, w, s, w]

def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first.

    Your search algorithm needs to return a list of actions that reaches the
    goal. Make sure to implement a graph search algorithm.

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    print("Start:", problem.getStartState())
    print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
    print("Start's successors:", problem.getSuccessors(problem.getStartState()))
    """
    "*** YOUR CODE HERE ***"
    from util import Stack
    Pilha_Caminho = Stack()
    Pilha_Estados = Stack()
    Caminho = []
    Visitados = []

    Pilha_Caminho.push(Caminho) # empilha caminho (vazio, no começo)
    Pilha_Estados.push(problem.getStartState()) # empilha estado inicial

    while (Pilha_Caminho.isEmpty() == False and Pilha_Estados.isEmpty() == False):
        Caminho_Andado = Pilha_Caminho.pop() # atualiza caminho
        Estado_Atual = Pilha_Estados.pop() # atualiza estado
        if problem.isGoalState(Estado_Atual): # caso estado atual seja o desejado,
            return Caminho_Andado # retorna o caminho total
        if Estado_Atual not in Visitados: # caso estado atual não tenha sido visitado
            Visitados.append(Estado_Atual) # marca estado como visitado
            for Sucessor in problem.getSuccessors(Estado_Atual): # busca sucessores
                if Sucessor[0] not in Visitados: # caso sucessor não tenha sido visitado
                    Pilha_Caminho.push(Caminho_Andado + [Sucessor[1]]) # atualiza caminho total na pilha
                    Pilha_Estados.push(Sucessor[0]) # atualiza estado
    return

def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    util.raiseNotDefined()

def uniformCostSearch(problem):
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    util.raiseNotDefined()

def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0

def aStarSearch(problem, heuristic=nullHeuristic):
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"
    def djikstra(Estado_Inicial):
        from util import PriorityQueue
        Fila_Estados = PriorityQueue() # cria fila de prioridade
        Fila_Estados.push((Estado_Inicial, []), 0) # insere tupla do estado inicial e vetor, com prioridade zero
        Distancia = dict() # cria dicionário para guardar distancia (afim de comparação)
        Distancia[Estado_Inicial] = 0 # seta distância inicial = 0
        while(not Fila_Estados.isEmpty()): # enquanto fila não está vazia,
            Estado_Atual, Caminho = Fila_Estados.pop() # desempilha estado atual e caminho até então
            if problem.isGoalState(Estado_Atual): # se estado atual é o desejado,
                return Caminho # retorna caminho
            for (Sucessor, Acao, Custo) in problem.getSuccessors(Estado_Atual): # verifica sucessores
                if Sucessor not in Distancia or Distancia[Sucessor] > Distancia[Estado_Atual] + Custo + heuristic(Sucessor, problem):
                    Distancia[Sucessor] = Distancia[Estado_Atual] + Custo + heuristic(Sucessor, problem) # atualiza distância de acordo com sucessor
                    Fila_Estados.push((Sucessor, Caminho + [Acao]), Distancia[Sucessor])
    return djikstra(problem.getStartState()) # recursividade

# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
