# 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
import time


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:
    """

    "*** YOUR CODE HERE ***"
    # ---- start state ----x
    current_state = problem.getStartState()
    visted = []
    # ---- initialize stack: stack contains FRINGE trajectories ----x
    fringe = util.Stack()
    fringe.push([[current_state, 0, 0]])

    # ---- while stack not empty ----x
    while not fringe.isEmpty():
        # ---- current trajectory ----x
        current_trajectory = fringe.pop()

        # ---- fringe state in trajectory ----x
        current_state = current_trajectory[len(current_trajectory)-1]

        # ---- check if new node/not visited ----x
        if current_state[0] not in visted:
            visted.append(current_state[0])

            # ---- check if goal reached ----x
            if problem.isGoalState(current_state[0]):
                print('---------------------- GOAL STATE LOCATED! ----------------------')
                # ---- return trajectory ----x
                curr = current_trajectory.copy()
                trajectory = [i[1] for i in curr[1:]]
                return trajectory


            # ---- neighbours ----x
            neighbours = problem.getSuccessors(current_state[0])
            new_states = [i for i in neighbours if i[0] not in visted]

            # --- if new states available ----x
            if new_states:
                # ---- Now add each new successor as a new trajectory ----x
                for n in new_states:
                    to_append = current_trajectory.copy()
                    to_append.append(n)
                    fringe.push(to_append)

    util.raiseNotDefined()




def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""
     # ---- start state ----x
    current_state = problem.getStartState()
    visted = []
    # ---- initialize stack: stack contains FRINGE trajectories ----x
    fringe = util.Queue()
    fringe.push([[current_state, 0, 0]])

    # ---- while stack not empty ----x
    while not fringe.isEmpty():
        # ---- current trajectory ----x
        current_trajectory = fringe.pop()

        # ---- fringe state in trajectory ----x
        current_state = current_trajectory[len(current_trajectory)-1]

        # ---- check if new node/not visited ----x
        if current_state[0] not in visted:
            visted.append(current_state[0])

            # ---- check if goal reached ----x
            if problem.isGoalState(current_state[0]):
                print('---------------------- GOAL STATE LOCATED! ----------------------')
                # ---- return trajectory ----x
                curr = current_trajectory.copy()
                trajectory = [i[1] for i in curr[1:]]
                return trajectory


            # ---- neighbours ----x
            neighbours = problem.getSuccessors(current_state[0])
            new_states = [i for i in neighbours if i[0] not in visted]

            # --- if new states available ----x
            if new_states:
                # ---- Now add each new successor as a new trajectory ----x
                for n in new_states:
                    to_append = current_trajectory.copy()
                    to_append.append(n)
                    fringe.push(to_append)

    util.raiseNotDefined()





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

    # ---- start state ----x
    current_state = problem.getStartState()
    visted = []
    # ---- initialize stack: stack contains FRINGE trajectories ----x
    fringe = util.PriorityQueue()
    fringe.push([(current_state, 0, 0)], 0)

    # ---- while stack not empty ----x
    while not fringe.isEmpty():
        # ---- current trajectory ----x
        current_trajectory = fringe.pop()

        # ---- fringe state in trajectory ----x
        current_state = current_trajectory[len(current_trajectory)-1]

        # ---- check if new node/not visited ----x
        if current_state[0] not in visted:
            visted.append(current_state[0])

            # ---- check if goal reached ----x
            if problem.isGoalState(current_state[0]):
                print('---------------------- GOAL STATE LOCATED! ----------------------')
                # ---- return trajectory ----x
                trajectory = [i[1] for i in current_trajectory[1:]]
                return trajectory

            # ---- compute current cost ----x
            gx = sum([i[2] for i in current_trajectory])

            # ---- neighbours ----x
            neighbours = problem.getSuccessors(current_state[0])
            new_states = [i for i in neighbours if i[0] not in visted]

            # --- if new states available ----x
            if new_states:

                # ---- Now add each new successor as a new trajectory ----x
                for n in new_states:
                    to_append = current_trajectory.copy()
                    to_append.append(n)
                    # ---- Cost priority ----x
                    fringe.push(to_append, gx + n[2])




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 ***"

    # ---- start state ----x
    current_state = problem.getStartState()
    visted = []
    # ---- initialize stack: stack contains FRINGE trajectories ----x
    fringe = util.PriorityQueue()
    fringe.push([(current_state, 0, 0)], heuristic(current_state, problem=problem))

    # ---- while stack not empty ----x
    while not fringe.isEmpty():
        # ---- current trajectory ----x
        current_trajectory = fringe.pop()

        # ---- fringe state in trajectory ----x
        current_state = current_trajectory[len(current_trajectory)-1]

        # ---- check if new node/not visited ----x
        if current_state[0] not in visted:
            visted.append(current_state[0])

            # ---- check if goal reached ----x
            if problem.isGoalState(current_state[0]):
                print('---------------------- GOAL STATE LOCATED! ----------------------')
                # ---- return trajectory ----x
                trajectory = [i[1] for i in current_trajectory[1:]]
                return trajectory

            # ---- compute current cost ----x
            gx = sum([i[2] for i in current_trajectory])

            # ---- neighbours ----x
            neighbours = problem.getSuccessors(current_state[0])
            new_states = [i for i in neighbours if i[0] not in visted]

            # --- if new states available ----x
            if new_states:
                visted.append(new_states[0])

                # ---- Now add each new successor as a new trajectory ----x
                for n in new_states:
                    to_append = current_trajectory.copy()
                    to_append.append(n)
                    # ---- A* priority ----x
                    fx = heuristic(n[0], problem=problem) + gx + n[2]
                    fringe.push(to_append, fx)





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