old_contents: """ This module implements a basic Mario Kart AI agent using a saved state-decision map.
  
  The basic Mario Kart AI agent functions as follows:
      1) The state-decision map contains the probabilities of taking an action in a certain state (downsampled image)
      2) Using the state-decision map, the basic Mario Kart AI chooses an action to take by drawing a uniformly
          distributed random number between 0 and 1 and comparing it to the probability of pressing a button in a given
          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)
  """
  
  import pickle
  import os
  import random
  from src import dp_screenshot, helper, mk_downsampler, key2pad
  
  class MarioKartAgent:
      """ Class implementing a basic Mario Kart AI agent using conditional probability. """
  
      def __init__(self, pickled_model_path, screenshot_folder):
          """ 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.screenshot_dir = os.path.join(helper.get_home_folder(), '.dolphin-emu', 'ScreenShots')
          self.screenshot_folder = screenshot_folder
  
      def process_frame(self):
  
  
      def run(self):
          # TODO complete
          # take screenshot
          screen.get_screen()
          # load img
          screen_dir = os.path.join(self.screenshot_dir, self.game_name)
  
          # downsample
          downsampling.Downsampler('NABE01', final_dim=15).downsample_dir(save_imgs=True)
          # look up game state
  
          # generate random number [0,1]
          rand=random.uniform(0,1)
          # take diff of key press state - update with current pressed keys
          # update controller inputs through pipe
          # delete screenshot?
          os.remove(os.path.join(self.screenshot_dir, self.game_name))
          pass
  
new_contents: """ This module implements a basic Mario Kart AI agent using a saved state-decision map.
  
  The basic Mario Kart AI agent functions as follows:
      1) The state-decision map contains the probabilities of taking an action in a certain state (downsampled image)
      2) Using the state-decision map, the basic Mario Kart AI chooses an action to take by drawing a uniformly
          distributed random number between 0 and 1 and comparing it to the probability of pressing a button in a given
          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)
  """
  
  import pickle
  import os
  import random
  from src import dp_screenshot, helper, mk_downsampler, key2pad
  
  class MarioKartAgent:
      """ Class implementing a basic Mario Kart AI agent using conditional probability. """
  
      def __init__(self, pickled_model_path, screenshot_folder):
          """ 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.screenshot_dir = os.path.join(helper.get_home_folder(), '.dolphin-emu', 'ScreenShots')
          self.screenshot_folder = screenshot_folder
  
      def process_frame(self):
          """ Process a single frame of the current Dolphin game.
  
          Note:
              Process means read the state of the game and decide what action to take in this context.
          """
          # Take screenshot of current Dolphin frame
          dp_screenshot.take_screenshot()
          screenshot_path = os.path.join(self.screenshot_dir, self.screenshot_folder)
          screenshot_file = os.path.join(screenshot_path, 'NABE01-1.png')
  
          # Downsample the screenshot
          ds_image = mk_downsampler('NABE01', final_dim = 15).downsample(screenshot_file)
          
  
  
  
  
  
  
      def run(self):
          # TODO complete
          # take screenshot
          screen.get_screen()
          # load img
          screen_dir = os.path.join(self.screenshot_dir, self.game_name)
  
          # downsample
          downsampling.Downsampler('NABE01', final_dim=15).downsample_dir(save_imgs=True)
          # look up game state
  
          # generate random number [0,1]
          rand=random.uniform(0,1)
          # take diff of key press state - update with current pressed keys
          # update controller inputs through pipe
          # delete screenshot?
          os.remove(os.path.join(self.screenshot_dir, self.game_name))
          pass
  
subject: Update process frame