# Path: src/dp_screenshot.py
# def take_screenshot():
# 
# Path: src/helper.py
# def validate_function_key(slot_key):
# def get_output_folder():
# def get_models_folder():
# def get_dataset_folder():
# def get_home_folder():
# def pickle_object(data, name, directory=get_output_folder()):
# def generate_img_key(image):
# def get_tensor(image):
# def get_output_vector(presses):
# def get_key_state_from_vector(vector):
# 
# Path: src/mk_downsampler.py
# class Downsampler:
#     def __init__(self, game_name, blur_size=25, intermediate_dim=300, final_dim=10):
#     def downsample_dir(self, save_imgs=False, clean_data=False):
#     def downsample(self, file_path, output_name=None, save_img=False, clean_data=False):
#     def save_image(self, img, output_name):
# 
# Path: src/key2pad.py
# class KeyPadMap:
#     def __init__(self):
#     def update(self, keys):
#     def convert_key(self, key, is_press):
# 
# Path: src/dp_frames.py
# def advance(slot_key):
# def inc_speed(slot_key):
# def dec_speed(slot_key):
# def res_speed(slot_key):


import logging
import pickle
import os
import time
import random
from src import dp_screenshot, helper, mk_downsampler, key2pad, dp_frames
        state. If the random number is above the probability threshold, the action is taken. If a state has never been
        encountered before, an action is chosen based on how often that action is taken in total out of all encountered
        states.
    3) The basic Mario Kart AI agent takes the desired action by sending controller inputs to the Dolphin emulator using
        fifo pipes and advancing the game by 1 frame, at which point the process starts again based on the next frame
        (state)
"""

logger = logging.getLogger(__name__)


class MarioKartAgent:
    """ Class implementing a basic Mario Kart AI agent using conditional probability. """
    game_name = "NABE01"

    def __init__(self, pickled_model_path, delay=0.2):
        """ Create a MarioKart Agent instance.

        Args:
            pickled_model_path: Path to the stored state-decision model file.
            screenshot_folder: Name of the folder where the current Dolphin game's screenshots are stored.
        """
        saved_model_file = open(pickled_model_path, 'rb')
        saved_model_obj = pickle.load(saved_model_file)
        self.decision_map = saved_model_obj.get("model")
        self.default_map = saved_model_obj.get("defaults")
        saved_model_file.close()

        self.frame_delay = delay
        self.screenshot_dir = os.path.join(helper.get_home_folder(), '.dolphin-emu', 'ScreenShots', self.game_name)

# Path: src/dp_screenshot.py
# def take_screenshot():
# 
# Path: src/helper.py
# def validate_function_key(slot_key):
# def get_output_folder():
# def get_models_folder():
# def get_dataset_folder():
# def get_home_folder():
# def pickle_object(data, name, directory=get_output_folder()):
# def generate_img_key(image):
# def get_tensor(image):
# def get_output_vector(presses):
# def get_key_state_from_vector(vector):
# 
# Path: src/mk_downsampler.py
# class Downsampler:
#     def __init__(self, game_name, blur_size=25, intermediate_dim=300, final_dim=10):
#     def downsample_dir(self, save_imgs=False, clean_data=False):
#     def downsample(self, file_path, output_name=None, save_img=False, clean_data=False):
#     def save_image(self, img, output_name):
# 
# Path: src/key2pad.py
# class KeyPadMap:
#     def __init__(self):
#     def update(self, keys):
#     def convert_key(self, key, is_press):
# 
# Path: src/dp_frames.py
# def advance(slot_key):
# def inc_speed(slot_key):
# def dec_speed(slot_key):
# def res_speed(slot_key):


# Path: src/agents/mk_naive_agent.py
import logging
import pickle
import os
import time
import random
from src import dp_screenshot, helper, mk_downsampler, key2pad, dp_frames

        state. If the random number is above the probability threshold, the action is taken. If a state has never been
        encountered before, an action is chosen based on how often that action is taken in total out of all encountered
        states.
    3) The basic Mario Kart AI agent takes the desired action by sending controller inputs to the Dolphin emulator using
        fifo pipes and advancing the game by 1 frame, at which point the process starts again based on the next frame
        (state)
"""

logger = logging.getLogger(__name__)


class MarioKartAgent:
    """ Class implementing a basic Mario Kart AI agent using conditional probability. """
    game_name = "NABE01"

    def __init__(self, pickled_model_path, delay=0.2):
        """ Create a MarioKart Agent instance.

        Args:
            pickled_model_path: Path to the stored state-decision model file.
            screenshot_folder: Name of the folder where the current Dolphin game's screenshots are stored.
        """
        saved_model_file = open(pickled_model_path, 'rb')
        saved_model_obj = pickle.load(saved_model_file)
        self.decision_map = saved_model_obj.get("model")
        self.default_map = saved_model_obj.get("defaults")
        saved_model_file.close()

        self.frame_delay = delay
        self.screenshot_dir = os.path.join(helper.get_home_folder(), '.dolphin-emu', 'ScreenShots', self.game_name)

        self.key_map = key2pad.KeyPadMap()