from random import randint

import pygame
import configparser

config = configparser.ConfigParser()
config.read("config.ini")

from domain.commands.random_cat_move_command import RandomCatMoveCommand
from domain.commands.vacuum_move_command import VacuumMoveCommand
from domain.entities.cat import Cat
from domain.entities.entity import Entity
from domain.entities.vacuum import Vacuum
from domain.entities.garbage import Garbage
from domain.entities.earring import Earring
from domain.entities.docking_station import Doc_Station
from domain.world import World
from view.renderer import Renderer
from AI_brain.genetic_algorytm import Path

if not config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
    from AI_brain.image_recognition import VacuumRecognizer

# from AI_brain.movement import GoAnyDirectionBFS, State
# from AI_brain.rotate_and_go_bfs import RotateAndGoBFS, State
from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State


class Main:
    def __init__(self):
        tiles_x = 10
        tiles_y = 10

        self.renderer = Renderer(600, 600, tiles_x, tiles_y)

        self.world = generate_world(tiles_x, tiles_y)

        self.commands = []

        self.clock = pygame.time.Clock()
        self.running = True
        self.fps = 60

    def run(self):
        while self.running:
            self.process_input()
            self.update()
            self.renderer.render(self.world)
            self.clock.tick(self.fps)

        pygame.quit()

    def run_robot(self):
        self.renderer.render(self.world)

        # path_searcher = GoAnyDirectionBFS(self.world, start_state, end_state)
        # path_searcher = RotateAndGoBFS(self.world, start_state, end_state)

        if config["AI_BRAIN"]["mode"] == "to_station":
            start_state = State(self.world.vacuum.x, self.world.vacuum.y)
            end_state = State(self.world.doc_station.x, self.world.doc_station.y)

            path_searcher = RotateAndGoAStar(self.world, start_state, end_state)

            if not path_searcher.search():
                print("No solution")
                exit(0)
            print(path_searcher.actions)
            print(path_searcher.cost)
        elif config["AI_BRAIN"]["mode"] == "full_clean":
            x = Path()
            x.random_walk(self.world.dustList)
            x.calculate_distance(self.world)
            print(x.walk)
            print(x.distance)
            exit(0)
        else:
            print("Wrong mode")
            exit(0)

        while self.running:
            for event in pygame.event.get():
                if event.type == pygame.QUIT:
                    self.running = False

            if len(path_searcher.actions) > 0:
                action_direction = path_searcher.actions.pop(0)
                # self.handle_action1(action_direction)
                self.handle_action2(action_direction)

            self.update()
            self.renderer.render(self.world)
            self.clock.tick(5)

        pygame.quit()

    def handle_action1(self, action):
        if action == "UP":
            self.commands.append(
                VacuumMoveCommand(self.world, self.world.vacuum, (0, -1))
            )
        elif action == "DOWN":
            self.commands.append(
                VacuumMoveCommand(self.world, self.world.vacuum, (0, 1))
            )
        elif action == "LEFT":
            self.commands.append(
                VacuumMoveCommand(self.world, self.world.vacuum, (-1, 0))
            )
        elif action == "RIGHT":
            self.commands.append(
                VacuumMoveCommand(self.world, self.world.vacuum, (1, 0))
            )

    def handle_action2(self, action):
        if action == "GO":
            self.commands.append(
                VacuumMoveCommand(
                    self.world, self.world.vacuum, self.world.vacuum.direction
                )
            )
        elif action == "RR":
            self.world.vacuum.direction = (
                -self.world.vacuum.direction[1],
                self.world.vacuum.direction[0],
            )
        elif action == "RL":
            self.world.vacuum.direction = (
                self.world.vacuum.direction[1],
                -self.world.vacuum.direction[0],
            )

    def process_input(self):
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                self.running = False
            if event.type == pygame.KEYDOWN:
                if event.key == pygame.K_LEFT:
                    self.commands.append(
                        VacuumMoveCommand(self.world, self.world.vacuum, (-1, 0))
                    )
                if event.key == pygame.K_RIGHT:
                    self.commands.append(
                        VacuumMoveCommand(self.world, self.world.vacuum, (1, 0))
                    )
                if event.key == pygame.K_UP:
                    self.commands.append(
                        VacuumMoveCommand(self.world, self.world.vacuum, (0, -1))
                    )
                if event.key == pygame.K_DOWN:
                    self.commands.append(
                        VacuumMoveCommand(self.world, self.world.vacuum, (0, 1))
                    )

    def update(self):
        if config.getboolean("APP", "cat"):
            self.commands.append(RandomCatMoveCommand(self.world, self.world.cat))
        for command in self.commands:
            command.run()
        self.commands.clear()


def generate_world(tiles_x: int, tiles_y: int) -> World:
    if config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
        world = World(tiles_x, tiles_y)
        world.vacuum = Vacuum(1, 1)
        world.doc_station = Doc_Station(9, 8)
        if config.getboolean("APP", "cat"):
            world.cat = Cat(7, 8)
            world.add_entity(world.cat)

        world.add_entity(world.doc_station)
        world.add_entity(world.vacuum)
        world.add_entity(Entity(2, 8, "PLANT1"))
        world.add_entity(Entity(4, 1, "PLANT1"))
        world.add_entity(Entity(3, 4, "PLANT2"))
        world.add_entity(Entity(8, 8, "PLANT2"))
        world.add_entity(Entity(9, 3, "PLANT3"))
        world.add_entity(Earring(9, 7))
        world.add_entity(Earring(5, 5))
        world.add_entity(Earring(4, 6))

        for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
            temp_x = randint(0, tiles_x - 1)
            temp_y = randint(0, tiles_y - 1)

            while world.is_entity_at(temp_x, temp_y):
                temp_x = randint(0, tiles_x - 1)
                temp_y = randint(0, tiles_y - 1)

            world.add_entity(Garbage(temp_x, temp_y))
    else:

        def world_adder(x, y, object, style=None):
            print(object)
            if object == "Plant":
                world.add_entity(Entity(x, y, f"PLANT{randint(1, 3)}"))
            if object == "Earings":
                world.add_entity(Earring(x, y))
            if object == "Banana":
                world.add_entity(Garbage(temp_x, temp_y))
            if object == "Cat" and config.getboolean("APP", "cat"):
                world.add_entity(Cat(x, y))

        neural_network = VacuumRecognizer()

        world = World(tiles_x, tiles_y)
        world.vacuum = Vacuum(1, 1)
        world.doc_station = Doc_Station(9, 8)
        if config.getboolean("APP", "cat"):
            world.cat = Cat(7, 8)
            world.add_entity(world.cat)

        for _ in range(config.getint("CONSTANT", "NumberOfPlants")):
            temp_x = randint(0, tiles_x - 1)
            temp_y = randint(0, tiles_y - 1)
            path = VacuumRecognizer.get_random_dir(neural_network, "Plant")
            world_adder(temp_x, temp_y, neural_network.recognize(path))

        for _ in range(config.getint("CONSTANT", "NumberOfEarrings")):
            temp_x = randint(0, tiles_x - 1)
            temp_y = randint(0, tiles_y - 1)
            path = VacuumRecognizer.get_random_dir(neural_network, "Earings")
            world_adder(temp_x, temp_y, neural_network.recognize(path))

        for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
            temp_x = randint(0, tiles_x - 1)
            temp_y = randint(0, tiles_y - 1)
            path = VacuumRecognizer.get_random_dir(neural_network, "Banana")
            world_adder(temp_x, temp_y, neural_network.recognize(path))

    for x in range(world.width):
        for y in range(world.height):
            if world.garbage_at(x, y):
                world.costs[x][y] = 1
            else:
                world.costs[x][y] = 10
    return world


if __name__ == "__main__":
    app = Main()
    if config["APP"]["movement"] == "human":
        app.run()
    elif config["APP"]["movement"] == "robot":
        app.run_robot()