import model
from qlearningAgents import PacmanQAgent
from backend import ReplayMemory
import layout
import copy
import torch
import numpy as np
import os
from util import *
import util
from pacman import GameState
from game import Directions, Actions

class Agent:
    """
    An agent must define a getAction method, but may also define the
    following methods which will be called if they exist:

    def registerInitialState(self, state): # inspects the starting state
    """

    def __init__(self, index=0):
        self.index = index

    def getAction(self, state):
        """
        The Agent will receive a GameState (from either {pacman, capture, sonar}.py) and
        must return an action from Directions.{North, South, East, West, Stop}
        """
        raiseNotDefined()
def betterEvaluationFunction(currentGameState: GameState):
    """
    Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable
    evaluation function (question 5).

    DESCRIPTION: <write something here so we know what you did>
    """
    # Useful information you can extract from a GameState (pacman.py)
    if currentGameState.isLose():
        return -500
    if currentGameState.isWin():
        return 500
    kInf=1e100
    capsules_position = currentGameState.getCapsules()
    current_pos = currentGameState.getPacmanPosition()
    food_positions = currentGameState.getFood().asList()
    # print(f"action:{action}, action_vec:{action_vec}")
    current_ghost_positions = currentGameState.getGhostPositions()
    current_ghost_scared_times = [ghostState.scaredTimer for ghostState in currentGameState.getGhostStates()]
    not_scared_ghosts_positions = [current_ghost_positions[i] for i in range(len(current_ghost_positions)) if current_ghost_scared_times[i]==0]
    scared_ghosts_positions = [current_ghost_positions[i] for i in range(len(current_ghost_positions)) if current_ghost_scared_times[i]>0 and current_ghost_scared_times[i]<=1.2*util.manhattanDistance(current_ghost_positions[i],current_pos)+2]
    edible_ghosts_positions = [current_ghost_positions[i] for i in range(len(current_ghost_positions)) if current_ghost_scared_times[i]>1.2*util.manhattanDistance(current_ghost_positions[i],current_pos)+2]
    current_self_position = currentGameState.getPacmanPosition()
    def DotProduct(a,b):
        return a[0]*b[0]+a[1]*b[1]
    def CrossProduct(a,b):
        return a[0]*b[1]-a[1]*b[0]
    def EuclideanDistance(a,b):
        return ((a[0]-b[0])**2+(a[1]-b[1])**2)**0.5
    def DistanceAnalysis(current_self_position,object_postion_list,flag="None"):
        if len(object_postion_list)==0:
            return 0
        if current_self_position in object_postion_list:
            return kInf
        res=0
        for obj_pos in object_postion_list:
            if flag=="Ghost" and util.manhattanDistance(current_self_position,obj_pos)>=6:
                continue
            distance_to_obj=util.manhattanDistance(current_self_position,obj_pos)
            res=max(res,1/distance_to_obj)
        return res

    da_for_foods=DistanceAnalysis(current_self_position,food_positions)
    da_for_unscared_ghosts=DistanceAnalysis(current_self_position,not_scared_ghosts_positions,"Ghost")
    da_for_scared_ghosts=DistanceAnalysis(current_self_position,scared_ghosts_positions)
    da_for_capsules=DistanceAnalysis(current_self_position,capsules_position)
    da_for_edible_ghosts=DistanceAnalysis(current_self_position,edible_ghosts_positions)
    res=da_for_capsules*2-da_for_unscared_ghosts*2-da_for_scared_ghosts*0.2+da_for_foods*0.2+da_for_edible_ghosts*1
    if da_for_unscared_ghosts<1/6:
        res+=(da_for_foods*0.2+da_for_edible_ghosts*1)*5
    # res*=random.uniform(0.9, 1.1)
    res*=100
    global last_score
    res+=(currentGameState.getScore()-last_score)*10
    # print(f"res:{res}")
    return res
class MultiAgentSearchAgent(Agent):
    """
    This class provides some common elements to all of your
    multi-agent searchers.  Any methods defined here will be available
    to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent.

    You *do not* need to make any changes here, but you can if you want to
    add functionality to all your adversarial search agents.  Please do not
    remove anything, however.

    Note: this is an abstract class: one that should not be instantiated.  It's
    only partially specified, and designed to be extended.  Agent (game.py)
    is another abstract class.
    """

    def __init__(self, evalFn = 'scoreEvaluationFunction', depth = '2'):
        self.index = 0 # Pacman is always agent index 0
        self.evaluationFunction = betterEvaluationFunction
        self.depth = int(depth)
last_score=0
class ExpectimaxAgent(MultiAgentSearchAgent):
    """
      Your expectimax agent (question 4)
    """

    def ExpectMaxSearch(self,gameState: GameState,depth_remain:int,agentIndex:int) -> tuple[int, list[Actions]]:
        if depth_remain==0:
            # print(f"depth_remain:{depth_remain}")
            # print(f"returning leaf {self.evaluationFunction(gameState)}, {[]}")
            return self.evaluationFunction(gameState),[]
        legal_actions = gameState.getLegalActions(agentIndex)
        if len(legal_actions)==0:
            # print(f"depth_remain:{depth_remain}")
            # print(f"returning leaf {self.evaluationFunction(gameState)}, {[]}")
            return self.evaluationFunction(gameState),[]
        kInf=1e100
        res_action=[]
        res_val=0
        if agentIndex==0:
            # Max
            res_val = -kInf
            for action in legal_actions:
                successorGameState = gameState.generateSuccessor(agentIndex,action)
                nxt_depth=depth_remain-1 if agentIndex==gameState.getNumAgents()-1 else depth_remain
                val,action_list=self.ExpectMaxSearch(successorGameState,nxt_depth,(agentIndex+1)%gameState.getNumAgents())
                if action=="Stop":
                    val-=100
                if val>res_val:
                    res_val=val
                    # print(f"action:{action}, action_list:{action_list}")
                    res_action=[action]+action_list
        else:
            # Mins
            res_val = kInf
            val_list=[]
            for action in legal_actions:
                successorGameState = gameState.generateSuccessor(agentIndex,action)
                nxt_depth=depth_remain-1 if agentIndex==gameState.getNumAgents()-1 else depth_remain
                val,action_list=self.ExpectMaxSearch(successorGameState,nxt_depth,(agentIndex+1)%gameState.getNumAgents())
                val_list.append(val)
                if val<res_val:
                    res_val=val
                    res_action=[action]+action_list
            res_val=sum(val_list)/len(val_list)
        # print(f"depth_remain:{depth_remain}")
        # print(f"returning {res_val}, {res_action}")
        return res_val,res_action

    def getAction(self, gameState: GameState):
        """
        Returns the expectimax action using self.depth and self.evaluationFunction

        All ghosts should be modeled as choosing uniformly at random from their
        legal moves.
        """
        global last_score
        last_score=gameState.getScore()
        stat = self.ExpectMaxSearch(gameState,self.depth,0)
        # print(f"stat:{stat}")
        return stat[1][0]


class PacmanDeepQAgent(PacmanQAgent):
    def __init__(self, layout_input="smallGrid", target_update_rate=300, doubleQ=True, **args):
        PacmanQAgent.__init__(self, **args)
        self.model = None
        self.target_model = None
        self.target_update_rate = target_update_rate
        self.update_amount = 0
        self.epsilon_explore = 1.0
        self.epsilon0 = 0.4
        self.minimal_epsilon = 0.01
        if model.kProductionMode:
            self.epsilon_explore=0.01
            self.epsilon0=0.01
            self.minimal_epsilon=0.01
            print("in production mode, epsilon set to 0.01")
        self.epsilon = self.epsilon0
        self.discount = 0.95
        self.update_frequency = 3
        self.counts = None
        self.replay_memory = ReplayMemory(50000)
        self.min_transitions_before_training = 10000
        self.td_error_clipping = 10

        # Initialize Q networks:
        if isinstance(layout_input, str):
            layout_instantiated = layout.getLayout(layout_input)
        else:
            layout_instantiated = layout_input
        self.state_dim = self.get_state_dim(layout_instantiated)
        self.initialize_q_networks(self.state_dim)

        self.doubleQ = doubleQ
        if self.doubleQ:
            self.target_update_rate = -1
        self.guiding_agent = ExpectimaxAgent()

    def get_state_dim(self, layout):
        pac_ft_size = 2
        ghost_ft_size = 2 * layout.getNumGhosts()
        food_capsule_ft_size = layout.width * layout.height
        return pac_ft_size + ghost_ft_size + food_capsule_ft_size

    def get_features(self, state):
        pacman_state = np.array(state.getPacmanPosition())
        ghost_state = np.array(state.getGhostPositions())
        capsules = state.getCapsules()
        food_locations = np.array(state.getFood().data).astype(np.float32)
        for x, y in capsules:
            food_locations[x][y] = 2
        return np.concatenate((pacman_state, ghost_state.flatten(), food_locations.flatten()))

    def initialize_q_networks(self, state_dim, action_dim=5):
        import model
        self.model = model.DeepQNetwork(state_dim, action_dim)
        self.target_model = model.DeepQNetwork(state_dim, action_dim)
        if os.path.exists('para.bin'):
            print("Loading model parameters from para.bin")
            checkpoint = torch.load('para.bin')
            self.model.load_state_dict(checkpoint['model_state_dict'])
            self.target_model.load_state_dict(checkpoint['target_model_state_dict'])
            self.model.optimizer.load_state_dict(checkpoint['model_optimizer_state_dict'])
            self.target_model.optimizer.load_state_dict(checkpoint['target_model_optimizer_state_dict'])
            self.replay_memory = checkpoint['memory']
            print(self.model.state_dict())
        else:
            print("Initializing new model parameters")
    def save_model(self, filename="para.bin"):
        if model.kProductionMode:
            print("in production mode, not saving model")
            return
        print(f"Saving model parameters to {filename}")
        torch.save({
            'model_state_dict': self.model.state_dict(),
            'target_model_state_dict': self.target_model.state_dict(),
            'model_optimizer_state_dict': self.model.optimizer.state_dict(),
            "target_model_optimizer_state_dict": self.target_model.optimizer.state_dict(),
            "memory": self.replay_memory
        }, filename)
        print(self.model.state_dict())

    def getQValue(self, state, action):
        """
          Should return Q(state,action) as predicted by self.model
        """
        feats = self.get_features(state)
        legalActions = self.getLegalActions(state)
        action_index = legalActions.index(action)
        state = torch.tensor(np.array([feats]).astype("float64"), dtype=torch.double)
        return self.model.run(state).data[0][action_index]


    def shape_reward(self, reward):
        if reward > 100:
            reward = 10
        elif reward > 0 and reward < 10:
            reward = 2
        elif reward == -1:
            reward = 0
        elif reward < -100:
            reward = -10
        return reward


    def compute_q_targets(self, minibatch, network = None, target_network=None, doubleQ=False):
        """Prepare minibatches
        Args:
            minibatch (List[Transition]): Minibatch of `Transition`
        Returns:
            float: Loss value
        """
        if network is None:
            network = self.model
        if target_network is None:
            target_network = self.target_model
        states = np.vstack([x.state for x in minibatch])
        states = torch.tensor(states, dtype=torch.double)
        actions = np.array([x.action for x in minibatch])
        rewards = np.array([x.reward for x in minibatch])
        next_states = np.vstack([x.next_state for x in minibatch])
        next_states = torch.tensor(next_states)
        done = np.array([x.done for x in minibatch])

        Q_predict = network.run(states).data.detach().cpu().numpy()
        Q_target = np.copy(Q_predict )
        state_indices = states.int().detach().numpy()
        state_indices = (state_indices[:, 0], state_indices[:, 1])
        exploration_bonus = 1 / (2 * np.sqrt((self.counts[state_indices] / 100)))

        replace_indices = np.arange(actions.shape[0])
        action_indices = np.argmax(network.run(next_states).data.cpu(), axis=1)
        target = rewards + exploration_bonus + (1 - done) * self.discount * target_network.run(next_states).data[replace_indices, action_indices].detach().cpu().numpy()

        Q_target[replace_indices, actions] = target

        if self.td_error_clipping is not None:
            Q_target = Q_predict + np.clip(
                     Q_target - Q_predict, -self.td_error_clipping, self.td_error_clipping)

        return Q_target

    def update(self, state, action, nextState, reward):
        legalActions = self.getLegalActions(state)
        action_index = legalActions.index(action)
        done = nextState.isLose() or nextState.isWin()
        reward = self.shape_reward(reward)

        if self.counts is None:
            x, y = np.array(state.getFood().data).shape
            self.counts = np.ones((x, y))

        state = self.get_features(state)
        nextState = self.get_features(nextState)
        self.counts[int(state[0])][int(state[1])] += 1

        transition = (state, action_index, reward, nextState, done)
        self.replay_memory.push(*transition)


        if len(self.replay_memory) < self.min_transitions_before_training:
            self.epsilon = self.epsilon_explore
        else:
            self.epsilon = max(self.epsilon0 * (1 - self.update_amount / 20000), self.minimal_epsilon)


        if len(self.replay_memory) > self.min_transitions_before_training and self.update_amount % self.update_frequency == 0:
            minibatch = self.replay_memory.pop(self.model.batch_size)
            states = np.vstack([x.state for x in minibatch])
            states = torch.tensor(states.astype("float64"), dtype=torch.double)
            Q_target1 = self.compute_q_targets(minibatch, self.model, self.target_model, doubleQ=self.doubleQ)
            Q_target1 = torch.tensor(Q_target1.astype("float64"), dtype=torch.double)

            if self.doubleQ:
                Q_target2 = self.compute_q_targets(minibatch, self.target_model, self.model, doubleQ=self.doubleQ)
                Q_target2 = torch.tensor(Q_target2.astype("float64"), dtype=torch.double)
            
            self.model.gradient_update(states, Q_target1)
            if self.doubleQ:
                self.target_model.gradient_update(states, Q_target2)

        if self.target_update_rate > 0 and self.update_amount % self.target_update_rate == 0:
            self.target_model.set_weights(copy.deepcopy(self.model.parameters))

        self.update_amount += 1


    def final(self, state):
        """Called at the end of each game."""
        PacmanQAgent.final(self, state)