import sys
import operator
from os import listdir, system
from random import randrange
from ale_py import ALEInterface, SDL_SUPPORT, Action
from colors import *
from PIL import Image
from matplotlib import pyplot as plt
import cv2
import pickle
import queue
from dataclasses import dataclass, field

from enum import Enum

from copy import deepcopy

import numpy as np

import readchar

from sample_factory.algo.utils.tensor_dict import TensorDict
from query_sample_factory_checkpoint import SampleFactoryNNQueryWrapper

import time

tempest_binary = "/home/spranger/projects/tempest-devel/ranking_release/bin/storm"
mdp_file = "simplified.prism"
rom_file = "/home/spranger/research/Skiing/env/lib/python3.8/site-packages/AutoROM/roms/skiing.bin"

class Verdict(Enum):
    INCONCLUSIVE = 1
    GOOD = 2
    BAD = 3

def convert(tuples):
    return dict(tuples)

@dataclass(frozen=True)
class State:
    x: int
    y: int
    ski_position: int
def default_value():
    return {'action' : None, 'choiceValue' : None}
@dataclass(frozen=True)
class StateValue:
    ranking: float
    choices: dict = field(default_factory=default_value)

def exec(command,verbose=True):
    if verbose: print(f"Executing {command}")
    system(f"echo {command} >> list_of_exec")
    return system(command)

def model_to_actual(ski_position):
    if ski_position == 1:
        return 1
    elif ski_position in [2,3]:
        return 2
    elif ski_position in [4,5]:
        return 3
    elif ski_position in [6,7]:
        return 4
    elif ski_position in [8,9]:
        return 5
    elif ski_position in [10,11]:
        return 6
    elif ski_position in [12,13]:
        return 7
    elif ski_position == 14:
        return 8

def input_to_action(char):
    if char == "0":
        return Action.NOOP
    if char == "1":
        return Action.RIGHT
    if char == "2":
        return Action.LEFT
    if char == "3":
        return "reset"
    if char == "4":
        return "set_x"
    if char == "5":
        return "set_vel"
    if char in ["w", "a", "s", "d"]:
        return char

def drawImportantStates(important_states):
    draw_commands = {1: list(), 2:list(), 3:list(), 4:list(), 5:list(), 6:list(), 7:list(), 8:list(), 9:list(), 10:list(), 11:list(), 12:list(), 13:list(), 14:list()}
    for state in important_states:
        x = state[0].x
        y = state[0].y
        markerSize = 2
        ski_position = state[0].ski_position
        draw_commands[ski_position].append(f"-fill 'rgba(255,204,0,{state[1].ranking})' -draw 'rectangle {x-markerSize},{y-markerSize} {x+markerSize},{y+markerSize} '")
    for i in range(1,15):
        command = f"convert images/1_full_scaled_down.png {' '.join(draw_commands[i])} first_try_{i:02}.png"
        exec(command)

ski_position_counter = {1: (Action.LEFT, 40), 2: (Action.LEFT, 35), 3: (Action.LEFT, 30), 4: (Action.LEFT, 10), 5: (Action.NOOP, 1), 6: (Action.RIGHT, 10), 7: (Action.RIGHT, 30), 8: (Action.RIGHT, 40) }
def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=200):
    print(f"Running Test from x: {x:04}, y: {y:04}, ski_position: {ski_position}", end="")
    for i, r in enumerate(ramDICT[y]):
        ale.setRAM(i,r)
    ski_position_setting = ski_position_counter[ski_position]
    for i in range(0,ski_position_setting[1]):
        ale.act(ski_position_setting[0])
        ale.setRAM(14,0)
        ale.setRAM(25,x)
    ale.setRAM(14,180)

    all_obs = list()
    speed_list = list()
    first_action_set = False
    first_action = 0
    for i in range(0,duration):
        resized_obs = cv2.resize(ale.getScreenGrayscale() , (84,84), interpolation=cv2.INTER_AREA)
        all_obs.append(resized_obs)
        if len(all_obs) >= 4:
            stack_tensor = TensorDict({"obs": np.array(all_obs[-4:])})
            action = nn_wrapper.query(stack_tensor)
            if not first_action_set:
                first_action_set = True
                first_action = input_to_action(str(action))
            ale.act(input_to_action(str(action)))
        else:
            ale.act(Action.NOOP)
        speed_list.append(ale.getRAM()[14])
        if len(speed_list) > 15 and sum(speed_list[-6:-1]) == 0:
            return (Verdict.BAD, first_action)
        time.sleep(0.005)
    return (Verdict.INCONCLUSIVE, first_action)

def optimalAction(choices):
    return max(choices.items(), key=operator.itemgetter(1))[0]

def computeStateRanking():
    command = f"{tempest_binary} --prism {mdp_file} --buildchoicelab --buildstateval --prop 'Rmax=? [C <= 1000]'"
    exec(command)

def fillStateRanking(file_name, match=""):
    state_ranking = dict()
    try:
        with open(file_name, "r") as f:
            file_content = f.readlines()
        for line in file_content:
            if not "move=0" in line: continue
            stateMapping = convert(re.findall(r"([a-zA-Z_]*[a-zA-Z])=(\d+)?", line))
            #print("stateMapping", stateMapping)
            choices = convert(re.findall(r"[a-zA-Z_]*(left|right|noop)[a-zA-Z_]*:(-?\d+\.?\d*)", line))
            choices = {key:float(value) for (key,value) in choices.items()}
            #print("choices", choices)
            ranking_value = float(re.search(r"Value:([+-]?(\d*\.\d+)|\d+)", line)[0].replace("Value:",""))
            #print("ranking_value", ranking_value)
            state = State(int(stateMapping["x"]), int(stateMapping["y"]), int(stateMapping["ski_position"]))
            value = StateValue(ranking_value, choices)
            state_ranking[state] = value
        return state_ranking

    except EnvironmentError:
        print("TODO file not available. Exiting.")
        sys.exit(1)

computeStateRanking()
ranking = fillStateRanking("action_ranking")
sorted_ranking = sorted(ranking.items(), key=lambda x: x[1].ranking)
ale = ALEInterface()


if SDL_SUPPORT:
    ale.setBool("sound", True)
    ale.setBool("display_screen", True)

# Load the ROM file
ale.loadROM(rom_file)

with open('all_positions_v2.pickle', 'rb') as handle:
    ramDICT = pickle.load(handle)
y_ram_setting = 60
x = 70


nn_wrapper = SampleFactoryNNQueryWrapper()

exec("cp testing_1.png /dev/shm/testing.png")
for important_state in sorted_ranking[-100:-1]:
    optimal_choice = optimalAction(important_state[1].choices)
    #print(important_state[1].choices, f"\t\tOptimal: {optimal_choice}")
    x = important_state[0].x
    y = important_state[0].y
    ski_pos = model_to_actual(important_state[0].ski_position)
    action_taken = run_single_test(ale,nn_wrapper,x,y,ski_pos, duration=50)
    print(f".... {action_taken}")
    markerSize = 1
    marker = f"-fill 'rgba(255,204,0,{important_state[1].ranking})' -draw 'point {x},{y} '"
    command = f"convert /dev/shm/testing.png {marker} /dev/shm/testing.png"
    exec(command, verbose=False)