# 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 ***"
    cur=problem.getStartState()
    if problem.isGoalState(cur):
        return []
    list1=util.Stack()
    list1.push((cur,[],0))
    passed=[]
    while True:
        if list1.isEmpty():
            return False
        cur,path,cost=list1.pop()
        if problem.isGoalState(cur):
            return path
        passed.append(cur)
        for i in problem.getSuccessors(cur):
            state,path1,cost1=i
            if state not in passed:
                list1.push((state,path+[path1],cost+cost1))

def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    cur=problem.getStartState()
    if problem.isGoalState(cur):
        return []
    list1=util.Queue()
    list1.push((cur,[],0))
    passed=[]
    while True:
        if list1.isEmpty():
            return False
        cur,path,cost=list1.pop()
        if problem.isGoalState(cur):
            return path
        passed.append(cur)
        for i in problem.getSuccessors(cur):
            state,path1,cost1=i
            if state not in passed:
                count=0
                for j in list1.list:
                    if j[0] == state:
                        count+=1
                if count == 0:
                    list1.push((state,path+[path1],cost+cost1))

def uniformCostSearch(problem):
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    cur=problem.getStartState()
    if problem.isGoalState(cur):
        return []
    list1=util.PriorityQueue()
    list1.push((cur,[],0),0)
    passed=[]
    while True:
        if list1.isEmpty():
            return False
        cur,path,cost=list1.pop()
        if problem.isGoalState(cur):
            return path
        passed.append(cur)
        for i in problem.getSuccessors(cur):
            state,path1,cost1=i
            if state not in passed:
                count1=0
                count2=0
                for p,c,j in list1.heap:
                    if j[0] == state:
                        count1+=1
                    if j[2] > cost + cost1:
                        count2+=1
                if count1 == 0:
                    list1.push((state,path+[path1],cost+cost1),cost+cost1)
                elif count2 > 0:
                    list1.update((state,path+[path1],cost+cost1),cost+cost1)

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 ***"
    cur=problem.getStartState()
    if problem.isGoalState(cur):
        return []
    list1=util.PriorityQueue()
    list1.push((cur,[],0),heuristic(cur,problem))
    passed=[]
    while True:
        if list1.isEmpty():
            return False
        cur,path,cost=list1.pop()
        if problem.isGoalState(cur):
            return path
        passed.append(cur)
        for i in problem.getSuccessors(cur):
            state,path1,cost1=i
            if state not in passed:
                count1=0
                count2=0
                for p,c,j in list1.heap:
                    if j[0] == state:
                        count1+=1
                    if j[2] > cost + cost1:
                        count2+=1
                if count1 == 0:
                    list1.push((state,path+[path1],cost+cost1),cost+cost1+heuristic(state,problem))
                elif count2 > 0:
                    list1.update((state,path+[path1],cost+cost1),cost+cost1)


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