"""
Implement Agents and Environments. (Chapters 1-2)

The class hierarchies are as follows:

Thing ## A physical object that can exist in an environment
    Agent
        Wumpus
    Dirt
    Wall
    ...

Environment ## An environment holds objects, runs simulations
    XYEnvironment
        VacuumEnvironment
        WumpusEnvironment

An agent program is a callable instance, taking percepts and choosing actions
    SimpleReflexAgentProgram
    ...

EnvGUI ## A window with a graphical representation of the Environment

EnvToolbar ## contains buttons for controlling EnvGUI

EnvCanvas ## Canvas to display the environment of an EnvGUI
"""

# TODO
# Speed control in GUI does not have any effect -- fix it.

from .utils import distance_squared, turn_heading
from statistics import mean
from ipythonblocks import BlockGrid
from IPython.display import HTML, display, clear_output
from time import sleep

import random
import copy
import collections
import numbers


# ______________________________________________________________________________


class Thing:
    """This represents any physical object that can appear in an Environment.
    You subclass Thing to get the things you want. Each thing can have a
    .__name__  slot (used for output only)."""

    def __repr__(self):
        return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__))

    def is_alive(self):
        """Things that are 'alive' should return true."""
        return hasattr(self, 'alive') and self.alive

    def show_state(self):
        """Display the agent's internal state. Subclasses should override."""
        print("I don't know how to show_state.")

    def display(self, canvas, x, y, width, height):
        """Display an image of this Thing on the canvas."""
        # Do we need this?
        pass


class Agent(Thing):
    """An Agent is a subclass of Thing with one required instance attribute 
    (aka slot), .program, which should hold a function that takes one argument,
    the percept, and returns an action. (What counts as a percept or action 
    will depend on the specific environment in which the agent exists.)
    Note that 'program' is a slot, not a method. If it were a method, then the
    program could 'cheat' and look at aspects of the agent. It's not supposed
    to do that: the program can only look at the percepts. An agent program
    that needs a model of the world (and of the agent itself) will have to
    build and maintain its own model. There is an optional slot, .performance,
    which is a number giving the performance measure of the agent in its
    environment."""

    def __init__(self, program=None):
        self.alive = True
        self.bump = False
        self.holding = []
        self.performance = 0
        if program is None or not isinstance(program, collections.abc.Callable):
            print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__))

            def program(percept):
                return eval(input('Percept={}; action? '.format(percept)))

        self.program = program

    def can_grab(self, thing):
        """Return True if this agent can grab this thing.
        Override for appropriate subclasses of Agent and Thing."""
        return False


def TraceAgent(agent):
    """Wrap the agent's program to print its input and output. This will let
    you see what the agent is doing in the environment."""
    old_program = agent.program

    def new_program(percept):
        action = old_program(percept)
        print('{} perceives {} and does {}'.format(agent, percept, action))
        return action

    agent.program = new_program
    return agent


# ______________________________________________________________________________


def TableDrivenAgentProgram(table):
    """
    [Figure 2.7]
    This agent selects an action based on the percept sequence.
    It is practical only for tiny domains.
    To customize it, provide as table a dictionary of all
    {percept_sequence:action} pairs.
    """
    percepts = []

    def program(percept):
        percepts.append(percept)
        action = table.get(tuple(percepts))
        return action

    return program


def RandomAgentProgram(actions):
    """An agent that chooses an action at random, ignoring all percepts.
    >>> list = ['Right', 'Left', 'Suck', 'NoOp']
    >>> program = RandomAgentProgram(list)
    >>> agent = Agent(program)
    >>> environment = TrivialVacuumEnvironment()
    >>> environment.add_thing(agent)
    >>> environment.run()
    >>> environment.status == {(1, 0): 'Clean' , (0, 0): 'Clean'}
    True
    """
    return lambda percept: random.choice(actions)


# ______________________________________________________________________________


def SimpleReflexAgentProgram(rules, interpret_input):
    """
    [Figure 2.10]
    This agent takes action based solely on the percept.
    """

    def program(percept):
        state = interpret_input(percept)
        rule = rule_match(state, rules)
        action = rule.action
        return action

    return program


def ModelBasedReflexAgentProgram(rules, update_state, model):
    """
    [Figure 2.12]
    This agent takes action based on the percept and state.
    """

    def program(percept):
        program.state = update_state(program.state, program.action, percept, model)
        rule = rule_match(program.state, rules)
        action = rule.action
        return action

    program.state = program.action = None
    return program


def rule_match(state, rules):
    """Find the first rule that matches state."""
    for rule in rules:
        if rule.matches(state):
            return rule


# ______________________________________________________________________________


loc_A, loc_B = (0, 0), (1, 0)  # The two locations for the Vacuum world


def RandomVacuumAgent():
    """Randomly choose one of the actions from the vacuum environment.
    >>> agent = RandomVacuumAgent()
    >>> environment = TrivialVacuumEnvironment()
    >>> environment.add_thing(agent)
    >>> environment.run()
    >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
    True
    """
    return Agent(RandomAgentProgram(['Right', 'Left', 'Suck', 'NoOp']))


def TableDrivenVacuumAgent():
    """Tabular approach towards vacuum world as mentioned in [Figure 2.3]
    >>> agent = TableDrivenVacuumAgent()
    >>> environment = TrivialVacuumEnvironment()
    >>> environment.add_thing(agent)
    >>> environment.run()
    >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
    True
    """
    table = {((loc_A, 'Clean'),): 'Right',
             ((loc_A, 'Dirty'),): 'Suck',
             ((loc_B, 'Clean'),): 'Left',
             ((loc_B, 'Dirty'),): 'Suck',
             ((loc_A, 'Dirty'), (loc_A, 'Clean')): 'Right',
             ((loc_A, 'Clean'), (loc_B, 'Dirty')): 'Suck',
             ((loc_B, 'Clean'), (loc_A, 'Dirty')): 'Suck',
             ((loc_B, 'Dirty'), (loc_B, 'Clean')): 'Left',
             ((loc_A, 'Dirty'), (loc_A, 'Clean'), (loc_B, 'Dirty')): 'Suck',
             ((loc_B, 'Dirty'), (loc_B, 'Clean'), (loc_A, 'Dirty')): 'Suck'}
    return Agent(TableDrivenAgentProgram(table))


def ReflexVacuumAgent():
    """
    [Figure 2.8]
    A reflex agent for the two-state vacuum environment.
    >>> agent = ReflexVacuumAgent()
    >>> environment = TrivialVacuumEnvironment()
    >>> environment.add_thing(agent)
    >>> environment.run()
    >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
    True
    """

    def program(percept):
        location, status = percept
        if status == 'Dirty':
            return 'Suck'
        elif location == loc_A:
            return 'Right'
        elif location == loc_B:
            return 'Left'

    return Agent(program)


def ModelBasedVacuumAgent():
    """An agent that keeps track of what locations are clean or dirty.
    >>> agent = ModelBasedVacuumAgent()
    >>> environment = TrivialVacuumEnvironment()
    >>> environment.add_thing(agent)
    >>> environment.run()
    >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
    True
    """
    model = {loc_A: None, loc_B: None}

    def program(percept):
        """Same as ReflexVacuumAgent, except if everything is clean, do NoOp."""
        location, status = percept
        model[location] = status  # Update the model here
        if model[loc_A] == model[loc_B] == 'Clean':
            return 'NoOp'
        elif status == 'Dirty':
            return 'Suck'
        elif location == loc_A:
            return 'Right'
        elif location == loc_B:
            return 'Left'

    return Agent(program)


# ______________________________________________________________________________


class Environment:
    """Abstract class representing an Environment. 'Real' Environment classes
    inherit from this. Your Environment will typically need to implement:
        percept:           Define the percept that an agent sees.
        execute_action:    Define the effects of executing an action.
                           Also update the agent.performance slot.
    The environment keeps a list of .things and .agents (which is a subset
    of .things). Each agent has a .performance slot, initialized to 0.
    Each thing has a .location slot, even though some environments may not
    need this."""

    def __init__(self):
        self.things = []
        self.agents = []

    def thing_classes(self):
        return []  # List of classes that can go into environment

    def percept(self, agent):
        """Return the percept that the agent sees at this point. (Implement this.)"""
        raise NotImplementedError

    def execute_action(self, agent, action):
        """Change the world to reflect this action. (Implement this.)"""
        raise NotImplementedError

    def default_location(self, thing):
        """Default location to place a new thing with unspecified location."""
        return None

    def exogenous_change(self):
        """If there is spontaneous change in the world, override this."""
        pass

    def is_done(self):
        """By default, we're done when we can't find a live agent."""
        return not any(agent.is_alive() for agent in self.agents)

    def step(self):
        """Run the environment for one time step. If the
        actions and exogenous changes are independent, this method will
        do. If there are interactions between them, you'll need to
        override this method."""
        if not self.is_done():
            actions = []
            for agent in self.agents:
                if agent.alive:
                    actions.append(agent.program(self.percept(agent)))
                else:
                    actions.append("")
            for (agent, action) in zip(self.agents, actions):
                self.execute_action(agent, action)
            self.exogenous_change()

    def run(self, steps=1000):
        """Run the Environment for given number of time steps."""
        for step in range(steps):
            if self.is_done():
                return
            self.step()

    def list_things_at(self, location, tclass=Thing):
        """Return all things exactly at a given location."""
        if isinstance(location, numbers.Number):
            return [thing for thing in self.things
                    if thing.location == location and isinstance(thing, tclass)]
        return [thing for thing in self.things
                if all(x == y for x, y in zip(thing.location, location)) and isinstance(thing, tclass)]

    def some_things_at(self, location, tclass=Thing):
        """Return true if at least one of the things at location
        is an instance of class tclass (or a subclass)."""
        return self.list_things_at(location, tclass) != []

    def add_thing(self, thing, location=None):
        """Add a thing to the environment, setting its location. For
        convenience, if thing is an agent program we make a new agent
        for it. (Shouldn't need to override this.)"""
        if not isinstance(thing, Thing):
            thing = Agent(thing)
        if thing in self.things:
            print("Can't add the same thing twice")
        else:
            thing.location = location if location is not None else self.default_location(thing)
            self.things.append(thing)
            if isinstance(thing, Agent):
                thing.performance = 0
                self.agents.append(thing)

    def delete_thing(self, thing):
        """Remove a thing from the environment."""
        try:
            self.things.remove(thing)
        except ValueError as e:
            print(e)
            print("  in Environment delete_thing")
            print("  Thing to be removed: {} at {}".format(thing, thing.location))
            print("  from list: {}".format([(thing, thing.location) for thing in self.things]))
        if thing in self.agents:
            self.agents.remove(thing)


class Direction:
    """A direction class for agents that want to move in a 2D plane
        Usage:
            d = Direction("down")
            To change directions:
            d = d + "right" or d = d + Direction.R #Both do the same thing
            Note that the argument to __add__ must be a string and not a Direction object.
            Also, it (the argument) can only be right or left."""

    R = "right"
    L = "left"
    U = "up"
    D = "down"

    def __init__(self, direction):
        self.direction = direction

    def __add__(self, heading):
        """
        >>> d = Direction('right')
        >>> l1 = d.__add__(Direction.L)
        >>> l2 = d.__add__(Direction.R)
        >>> l1.direction
        'up'
        >>> l2.direction
        'down'
        >>> d = Direction('down')
        >>> l1 = d.__add__('right')
        >>> l2 = d.__add__('left')
        >>> l1.direction == Direction.L
        True
        >>> l2.direction == Direction.R
        True
        """
        if self.direction == self.R:
            return {
                self.R: Direction(self.D),
                self.L: Direction(self.U),
            }.get(heading, None)
        elif self.direction == self.L:
            return {
                self.R: Direction(self.U),
                self.L: Direction(self.D),
            }.get(heading, None)
        elif self.direction == self.U:
            return {
                self.R: Direction(self.R),
                self.L: Direction(self.L),
            }.get(heading, None)
        elif self.direction == self.D:
            return {
                self.R: Direction(self.L),
                self.L: Direction(self.R),
            }.get(heading, None)

    def move_forward(self, from_location):
        """
        >>> d = Direction('up')
        >>> l1 = d.move_forward((0, 0))
        >>> l1
        (0, -1)
        >>> d = Direction(Direction.R)
        >>> l1 = d.move_forward((0, 0))
        >>> l1
        (1, 0)
        """
        # get the iterable class to return
        iclass = from_location.__class__
        x, y = from_location
        if self.direction == self.R:
            return iclass((x + 1, y))
        elif self.direction == self.L:
            return iclass((x - 1, y))
        elif self.direction == self.U:
            return iclass((x, y - 1))
        elif self.direction == self.D:
            return iclass((x, y + 1))


class XYEnvironment(Environment):
    """This class is for environments on a 2D plane, with locations
    labelled by (x, y) points, either discrete or continuous.

    Agents perceive things within a radius. Each agent in the
    environment has a .location slot which should be a location such
    as (0, 1), and a .holding slot, which should be a list of things
    that are held."""

    def __init__(self, width=10, height=10):
        super().__init__()

        self.width = width
        self.height = height
        self.observers = []
        # Sets iteration start and end (no walls).
        self.x_start, self.y_start = (0, 0)
        self.x_end, self.y_end = (self.width, self.height)

    perceptible_distance = 1

    def things_near(self, location, radius=None):
        """Return all things within radius of location."""
        if radius is None:
            radius = self.perceptible_distance
        radius2 = radius * radius
        return [(thing, radius2 - distance_squared(location, thing.location))
                for thing in self.things if distance_squared(
                location, thing.location) <= radius2]

    def percept(self, agent):
        """By default, agent perceives things within a default radius."""
        return self.things_near(agent.location)

    def execute_action(self, agent, action):
        agent.bump = False
        if action == 'TurnRight':
            agent.direction += Direction.R
        elif action == 'TurnLeft':
            agent.direction += Direction.L
        elif action == 'Forward':
            agent.bump = self.move_to(agent, agent.direction.move_forward(agent.location))
        elif action == 'Grab':
            things = [thing for thing in self.list_things_at(agent.location) if agent.can_grab(thing)]
            if things:
                agent.holding.append(things[0])
                print("Grabbing ", things[0].__class__.__name__)
                self.delete_thing(things[0])
        elif action == 'Release':
            if agent.holding:
                dropped = agent.holding.pop()
                print("Dropping ", dropped.__class__.__name__)
                self.add_thing(dropped, location=agent.location)

    def default_location(self, thing):
        location = self.random_location_inbounds()
        while self.some_things_at(location, Obstacle):
            # we will find a random location with no obstacles
            location = self.random_location_inbounds()
        return location

    def move_to(self, thing, destination):
        """Move a thing to a new location. Returns True on success or False if there is an Obstacle.
        If thing is holding anything, they move with him."""
        thing.bump = self.some_things_at(destination, Obstacle)
        if not thing.bump:
            thing.location = destination
            for o in self.observers:
                o.thing_moved(thing)
            for t in thing.holding:
                self.delete_thing(t)
                self.add_thing(t, destination)
                t.location = destination
        return thing.bump

    def add_thing(self, thing, location=None, exclude_duplicate_class_items=False):
        """Add things to the world. If (exclude_duplicate_class_items) then the item won't be
        added if the location has at least one item of the same class."""
        if location is None:
            super().add_thing(thing)
        elif self.is_inbounds(location):
            if (exclude_duplicate_class_items and
                    any(isinstance(t, thing.__class__) for t in self.list_things_at(location))):
                return
            super().add_thing(thing, location)

    def is_inbounds(self, location):
        """Checks to make sure that the location is inbounds (within walls if we have walls)"""
        x, y = location
        return not (x < self.x_start or x > self.x_end or y < self.y_start or y > self.y_end)

    def random_location_inbounds(self, exclude=None):
        """Returns a random location that is inbounds (within walls if we have walls)"""
        location = (random.randint(self.x_start, self.x_end),
                    random.randint(self.y_start, self.y_end))
        if exclude is not None:
            while location == exclude:
                location = (random.randint(self.x_start, self.x_end),
                            random.randint(self.y_start, self.y_end))
        return location

    def delete_thing(self, thing):
        """Deletes thing, and everything it is holding (if thing is an agent)"""
        if isinstance(thing, Agent):
            del thing.holding

        super().delete_thing(thing)
        for obs in self.observers:
            obs.thing_deleted(thing)

    def add_walls(self):
        """Put walls around the entire perimeter of the grid."""
        for x in range(self.width):
            self.add_thing(Wall(), (x, 0))
            self.add_thing(Wall(), (x, self.height - 1))
        for y in range(1, self.height - 1):
            self.add_thing(Wall(), (0, y))
            self.add_thing(Wall(), (self.width - 1, y))

        # Updates iteration start and end (with walls).
        self.x_start, self.y_start = (1, 1)
        self.x_end, self.y_end = (self.width - 1, self.height - 1)

    def add_observer(self, observer):
        """Adds an observer to the list of observers.
        An observer is typically an EnvGUI.

        Each observer is notified of changes in move_to and add_thing,
        by calling the observer's methods thing_moved(thing)
        and thing_added(thing, loc)."""
        self.observers.append(observer)

    def turn_heading(self, heading, inc):
        """Return the heading to the left (inc=+1) or right (inc=-1) of heading."""
        return turn_heading(heading, inc)


class Obstacle(Thing):
    """Something that can cause a bump, preventing an agent from
    moving into the same square it's in."""
    pass


class Wall(Obstacle):
    pass


# ______________________________________________________________________________


class GraphicEnvironment(XYEnvironment):
    def __init__(self, width=10, height=10, boundary=True, color={}, display=False):
        """Define all the usual XYEnvironment characteristics,
        but initialise a BlockGrid for GUI too."""
        super().__init__(width, height)
        self.grid = BlockGrid(width, height, fill=(200, 200, 200))
        if display:
            self.grid.show()
            self.visible = True
        else:
            self.visible = False
        self.bounded = boundary
        self.colors = color

    def get_world(self):
        """Returns all the items in the world in a format
        understandable by the ipythonblocks BlockGrid."""
        result = []
        x_start, y_start = (0, 0)
        x_end, y_end = self.width, self.height
        for x in range(x_start, x_end):
            row = []
            for y in range(y_start, y_end):
                row.append(self.list_things_at((x, y)))
            result.append(row)
        return result

    """
    def run(self, steps=1000, delay=1):
        "" "Run the Environment for given number of time steps,
        but update the GUI too." ""
        for step in range(steps):
            sleep(delay)
            if self.visible:
                self.reveal()
            if self.is_done():
                if self.visible:
                    self.reveal()
                return
            self.step()
        if self.visible:
            self.reveal()
    """

    def run(self, steps=1000, delay=1):
        """Run the Environment for given number of time steps,
        but update the GUI too."""
        for step in range(steps):
            self.update(delay)
            if self.is_done():
                break
            self.step()
        self.update(delay)

    def update(self, delay=1):
        sleep(delay)
        self.reveal()

    def reveal(self):
        """Display the BlockGrid for this world - the last thing to be added
        at a location defines the location color."""
        self.draw_world()
        # wait for the world to update and
        # apply changes to the same grid instead
        # of making a new one.
        clear_output(1)
        self.grid.show()
        self.visible = True

    def draw_world(self):
        self.grid[:] = (200, 200, 200)
        world = self.get_world()
        for x in range(0, len(world)):
            for y in range(0, len(world[x])):
                if len(world[x][y]):
                    self.grid[y, x] = self.colors[world[x][y][-1].__class__.__name__]

    def conceal(self):
        """Hide the BlockGrid for this world"""
        self.visible = False
        display(HTML(''))


# ______________________________________________________________________________
# Continuous environment

class ContinuousWorld(Environment):
    """Model for Continuous World"""

    def __init__(self, width=10, height=10):
        super().__init__()
        self.width = width
        self.height = height

    def add_obstacle(self, coordinates):
        self.things.append(PolygonObstacle(coordinates))


class PolygonObstacle(Obstacle):

    def __init__(self, coordinates):
        """Coordinates is a list of tuples."""
        super().__init__()
        self.coordinates = coordinates


# ______________________________________________________________________________
# Vacuum environment


class Dirt(Thing):
    pass


class VacuumEnvironment(XYEnvironment):
    """The environment of [Ex. 2.12]. Agent perceives dirty or clean,
    and bump (into obstacle) or not; 2D discrete world of unknown size;
    performance measure is 100 for each dirt cleaned, and -1 for
    each turn taken."""

    def __init__(self, width=10, height=10):
        super().__init__(width, height)
        self.add_walls()

    def thing_classes(self):
        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,
                TableDrivenVacuumAgent, ModelBasedVacuumAgent]

    def percept(self, agent):
        """The percept is a tuple of ('Dirty' or 'Clean', 'Bump' or 'None').
        Unlike the TrivialVacuumEnvironment, location is NOT perceived."""
        status = ('Dirty' if self.some_things_at(
            agent.location, Dirt) else 'Clean')
        bump = ('Bump' if agent.bump else 'None')
        return status, bump

    def execute_action(self, agent, action):
        agent.bump = False
        if action == 'Suck':
            dirt_list = self.list_things_at(agent.location, Dirt)
            if dirt_list != []:
                dirt = dirt_list[0]
                agent.performance += 100
                self.delete_thing(dirt)
        else:
            super().execute_action(agent, action)

        if action != 'NoOp':
            agent.performance -= 1


class TrivialVacuumEnvironment(Environment):
    """This environment has two locations, A and B. Each can be Dirty
    or Clean. The agent perceives its location and the location's
    status. This serves as an example of how to implement a simple
    Environment."""

    def __init__(self):
        super().__init__()
        self.status = {loc_A: random.choice(['Clean', 'Dirty']),
                       loc_B: random.choice(['Clean', 'Dirty'])}

    def thing_classes(self):
        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent, TableDrivenVacuumAgent, ModelBasedVacuumAgent]

    def percept(self, agent):
        """Returns the agent's location, and the location status (Dirty/Clean)."""
        return agent.location, self.status[agent.location]

    def execute_action(self, agent, action):
        """Change agent's location and/or location's status; track performance.
        Score 10 for each dirt cleaned; -1 for each move."""
        if action == 'Right':
            agent.location = loc_B
            agent.performance -= 1
        elif action == 'Left':
            agent.location = loc_A
            agent.performance -= 1
        elif action == 'Suck':
            if self.status[agent.location] == 'Dirty':
                agent.performance += 10
            self.status[agent.location] = 'Clean'

    def default_location(self, thing):
        """Agents start in either location at random."""
        return random.choice([loc_A, loc_B])


# ______________________________________________________________________________
# The Wumpus World


class Gold(Thing):

    def __eq__(self, rhs):
        """All Gold are equal"""
        return rhs.__class__ == Gold

    pass


class Bump(Thing):
    pass


class Glitter(Thing):
    pass


class Pit(Thing):
    pass


class Breeze(Thing):
    pass


class Arrow(Thing):
    pass


class Scream(Thing):
    pass


class Wumpus(Agent):
    screamed = False
    pass


class Stench(Thing):
    pass


class Explorer(Agent):
    holding = []
    has_arrow = True
    killed_by = ""
    direction = Direction("right")

    def can_grab(self, thing):
        """Explorer can only grab gold"""
        return thing.__class__ == Gold


class WumpusEnvironment(XYEnvironment):
    pit_probability = 0.2  # Probability to spawn a pit in a location. (From Chapter 7.2)

    # Room should be 4x4 grid of rooms. The extra 2 for walls

    def __init__(self, agent_program, width=6, height=6):
        super().__init__(width, height)
        self.init_world(agent_program)

    def init_world(self, program):
        """Spawn items in the world based on probabilities from the book"""

        "WALLS"
        self.add_walls()

        "PITS"
        for x in range(self.x_start, self.x_end):
            for y in range(self.y_start, self.y_end):
                if random.random() < self.pit_probability:
                    self.add_thing(Pit(), (x, y), True)
                    self.add_thing(Breeze(), (x - 1, y), True)
                    self.add_thing(Breeze(), (x, y - 1), True)
                    self.add_thing(Breeze(), (x + 1, y), True)
                    self.add_thing(Breeze(), (x, y + 1), True)

        "WUMPUS"
        w_x, w_y = self.random_location_inbounds(exclude=(1, 1))
        self.add_thing(Wumpus(lambda x: ""), (w_x, w_y), True)
        self.add_thing(Stench(), (w_x - 1, w_y), True)
        self.add_thing(Stench(), (w_x + 1, w_y), True)
        self.add_thing(Stench(), (w_x, w_y - 1), True)
        self.add_thing(Stench(), (w_x, w_y + 1), True)

        "GOLD"
        self.add_thing(Gold(), self.random_location_inbounds(exclude=(1, 1)), True)

        "AGENT"
        self.add_thing(Explorer(program), (1, 1), True)

    def get_world(self, show_walls=True):
        """Return the items in the world"""
        result = []
        x_start, y_start = (0, 0) if show_walls else (1, 1)

        if show_walls:
            x_end, y_end = self.width, self.height
        else:
            x_end, y_end = self.width - 1, self.height - 1

        for x in range(x_start, x_end):
            row = []
            for y in range(y_start, y_end):
                row.append(self.list_things_at((x, y)))
            result.append(row)
        return result

    def percepts_from(self, agent, location, tclass=Thing):
        """Return percepts from a given location,
        and replaces some items with percepts from chapter 7."""
        thing_percepts = {
            Gold: Glitter(),
            Wall: Bump(),
            Wumpus: Stench(),
            Pit: Breeze()}

        """Agents don't need to get their percepts"""
        thing_percepts[agent.__class__] = None

        """Gold only glitters in its cell"""
        if location != agent.location:
            thing_percepts[Gold] = None

        result = [thing_percepts.get(thing.__class__, thing) for thing in self.things
                  if thing.location == location and isinstance(thing, tclass)]
        return result if len(result) else [None]

    def percept(self, agent):
        """Return things in adjacent (not diagonal) cells of the agent.
        Result format: [Left, Right, Up, Down, Center / Current location]"""
        x, y = agent.location
        result = []
        result.append(self.percepts_from(agent, (x - 1, y)))
        result.append(self.percepts_from(agent, (x + 1, y)))
        result.append(self.percepts_from(agent, (x, y - 1)))
        result.append(self.percepts_from(agent, (x, y + 1)))
        result.append(self.percepts_from(agent, (x, y)))

        """The wumpus gives out a loud scream once it's killed."""
        wumpus = [thing for thing in self.things if isinstance(thing, Wumpus)]
        if len(wumpus) and not wumpus[0].alive and not wumpus[0].screamed:
            result[-1].append(Scream())
            wumpus[0].screamed = True

        return result

    def execute_action(self, agent, action):
        """Modify the state of the environment based on the agent's actions.
        Performance score taken directly out of the book."""

        if isinstance(agent, Explorer) and self.in_danger(agent):
            return

        agent.bump = False
        if action in ['TurnRight', 'TurnLeft', 'Forward', 'Grab']:
            super().execute_action(agent, action)
            agent.performance -= 1
        elif action == 'Climb':
            if agent.location == (1, 1):  # Agent can only climb out of (1,1)
                agent.performance += 1000 if Gold() in agent.holding else 0
                self.delete_thing(agent)
        elif action == 'Shoot':
            """The arrow travels straight down the path the agent is facing"""
            if agent.has_arrow:
                arrow_travel = agent.direction.move_forward(agent.location)
                while self.is_inbounds(arrow_travel):
                    wumpus = [thing for thing in self.list_things_at(arrow_travel)
                              if isinstance(thing, Wumpus)]
                    if len(wumpus):
                        wumpus[0].alive = False
                        break
                    arrow_travel = agent.direction.move_forward(agent.location)
                agent.has_arrow = False

    def in_danger(self, agent):
        """Check if Explorer is in danger (Pit or Wumpus), if he is, kill him"""
        for thing in self.list_things_at(agent.location):
            if isinstance(thing, Pit) or (isinstance(thing, Wumpus) and thing.alive):
                agent.alive = False
                agent.performance -= 1000
                agent.killed_by = thing.__class__.__name__
                return True
        return False

    def is_done(self):
        """The game is over when the Explorer is killed
        or if he climbs out of the cave only at (1,1)."""
        explorer = [agent for agent in self.agents if isinstance(agent, Explorer)]
        if len(explorer):
            if explorer[0].alive:
                return False
            else:
                print("Death by {} [-1000].".format(explorer[0].killed_by))
        else:
            print("Explorer climbed out {}."
                  .format("with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]"))
        return True

    # TODO: Arrow needs to be implemented


# ______________________________________________________________________________


def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000):
    """See how well each of several agents do in n instances of an environment.
    Pass in a factory (constructor) for environments, and several for agents.
    Create n instances of the environment, and run each agent in copies of
    each one for steps. Return a list of (agent, average-score) tuples.
    >>> environment = TrivialVacuumEnvironment
    >>> agents = [ModelBasedVacuumAgent, ReflexVacuumAgent]
    >>> result = compare_agents(environment, agents)
    >>> performance_ModelBasedVacuumAgent = result[0][1]
    >>> performance_ReflexVacuumAgent = result[1][1]
    >>> performance_ReflexVacuumAgent <= performance_ModelBasedVacuumAgent
    True
    """
    envs = [EnvFactory() for i in range(n)]
    return [(A, test_agent(A, steps, copy.deepcopy(envs)))
            for A in AgentFactories]


def test_agent(AgentFactory, steps, envs):
    """Return the mean score of running an agent in each of the envs, for steps
    >>> def constant_prog(percept):
    ...     return percept
    ...
    >>> agent = Agent(constant_prog)
    >>> result = agent.program(5)
    >>> result == 5
    True
    """

    def score(env):
        agent = AgentFactory()
        env.add_thing(agent)
        env.run(steps)
        return agent.performance

    return mean(map(score, envs))


# _________________________________________________________________________


__doc__ += """
>>> a = ReflexVacuumAgent()
>>> a.program((loc_A, 'Clean'))
'Right'
>>> a.program((loc_B, 'Clean'))
'Left'
>>> a.program((loc_A, 'Dirty'))
'Suck'
>>> a.program((loc_A, 'Dirty'))
'Suck'

>>> e = TrivialVacuumEnvironment()
>>> e.add_thing(ModelBasedVacuumAgent())
>>> e.run(5)

"""
