# 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):


import collections
import logging
import os
import time
import torch
from src import dp_screenshot, helper, mk_downsampler, key2pad
        # Take screenshot of current Dolphin frame
        dp_screenshot.take_screenshot()
        screenshot_file = os.path.join(self.screenshot_dir, 'NABE01-1.png')
        # time.sleep(self.frame_delay)

        try:
            # Downsample the screenshot and calculate dictionary key
            ds_image = mk_downsampler.Downsampler(self.game_name, final_dim=15).downsample(screenshot_file)

            # Generate tensor from image
            x = helper.get_tensor(ds_image)

            if x is None:
                logger.info("Skipping frame - no input received.")
                return

            # add tensor to history
            self.previous_tensors.appendleft(x)
            # do not continue if not enough tensor history has been filled
            if len(self.previous_tensors) != self.history_length:
                logger.info("Skipping frame - not enough inputs yet")
                return

            # stack tensors
            tensors = torch.stack(self.previous_tensors)

            # Predict key presses using neural network
            prediction = self.model(tensors)

            # Choose which action to take from prediction

# 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/agents/mk_nn.py
import collections
import logging
import os
import time
import torch
from src import dp_screenshot, helper, mk_downsampler, key2pad

        # Take screenshot of current Dolphin frame
        dp_screenshot.take_screenshot()
        screenshot_file = os.path.join(self.screenshot_dir, 'NABE01-1.png')
        # time.sleep(self.frame_delay)

        try:
            # Downsample the screenshot and calculate dictionary key
            ds_image = mk_downsampler.Downsampler(self.game_name, final_dim=15).downsample(screenshot_file)

            # Generate tensor from image
            x = helper.get_tensor(ds_image)

            if x is None:
                logger.info("Skipping frame - no input received.")
                return

            # add tensor to history
            self.previous_tensors.appendleft(x)
            # do not continue if not enough tensor history has been filled
            if len(self.previous_tensors) != self.history_length:
                logger.info("Skipping frame - not enough inputs yet")
                return

            # stack tensors
            tensors = torch.stack(self.previous_tensors)

            # Predict key presses using neural network
            prediction = self.model(tensors)

            # Choose which action to take from prediction

            key_state = helper.get_key_state_from_vector(prediction)