# 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 ***"

    def traverse(problem, node, visited):

        # Add nextNode to visited list
        visited.append(node[0])

        # Recursive base case
        if problem.isGoalState(node[0]):
            return [node[1]]

        pq = util.PriorityQueueWithFunction(lambda child: child[2])

        # Get all successors and push them into a priority queue based on cost
        children = problem.getSuccessors(node[0])
        for nextNode in children:
            # Check that nextNode has not been visited
            if nextNode[0] in visited:
                continue

            # Push the child if it was not visited
            pq.push(nextNode)

        while not pq.isEmpty():
            # Pop lowest cost child from the queue
            nextNode = pq.pop()

            # Recursive call to nextNode
            path = traverse(problem, nextNode, visited)

            # Check if recursive call returned values
            if path != None:
                # Check to not add the initial node
                if node[1] != None:
                    # Add the node the start of the list (reverse it)
                    path.insert(0, node[1])
                return path

    return traverse(problem, (problem.getStartState(), None, 0), [])


def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""

    # main search function

    # initialize
    queue = util.Queue()
    visited = []
    path = []
    start = (problem.getStartState(), None, 0)

    queue.push(start)
    path.append(start)
    visited.append(problem.getStartState())

    def getActions(list, goal):
        start = problem.getStartState()
        path = []
        list = list[1:]
        list = list[::-1]
        state = next((node for node in list if node[0][0] == goal), None)
        while state[0][1] is not start:
            path.insert(0, state[0][1])
            state = next((node for node in list if node[0] == state[1]), None)
            if state is None:
                return path
        return path

    while not queue.isEmpty():
        state = queue.pop()
        if problem.isGoalState(state[0]):
            return getActions(path, state[0])
        for successor in problem.getSuccessors(state[0]):
            if successor[0] not in visited:
                visited.append(successor[0])
                path.append((successor, state))
                queue.push(successor)


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

    # lambda to get the total cost of a node
    cost = lambda node: problem.getCostOfActions(node[3])
    pq = util.PriorityQueueWithFunction(cost)
    pq.push((problem.getStartState(), None, 0, []))
    visited = {}

    while not pq.isEmpty():
        node = pq.pop()

        # Add nextNode to visited list
        visited[node[0]] = cost(node)

        # base case
        if problem.isGoalState(node[0]):
            return node[3]

        # Get all successors and push them into a priority queue based on cost
        nextNodes = problem.getSuccessors(node[0])
        for nextNode in nextNodes:
            # Append the node's path + nextNode's direction
            nextNode = nextNode + (node[3] + [nextNode[1]],)

            # Check that nextNode has not been visited
            if visited.has_key(nextNode[0]) and visited[nextNode[0]] < cost(nextNode):
                continue

            # Push the child if it was not visited
            pq.push(nextNode)


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 ***"
    util.raiseNotDefined()


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