#!/usr/bin/env python

import argparse  # parse input arguments
import numpy as np  # arithmetic library
import time
import gym
from agents import agent_register
import pybullet as p
import kerasrl_utils

np.set_printoptions(precision=3, suppress=True, linewidth=10000)


def add_opts(parser):
	parser.add_argument('--agent', type=str, default="KerasDQNAgent", help="Agent to be trained with.")
	parser.add_argument('--env', type=str, default="2DDetachedCartPolev0Env", help="Environment to be trained in.")
	parser.add_argument('--use-latest', action='store_true', help="Should the trainer retrain/show with the most recent save?")
	parser.add_argument('--train-for', type=int, default=100, help="The number of epochs to train for.")
	parser.add_argument('--test-for', type=int, default=0, help="The number of epoch to test for.")
	parser.add_argument('--load-file', type=str, default=None, help="The weight file to load for training.")
	parser.add_argument('--save-file', type=str, default=None, help="The weight file to save after training.")
	

class Trainer:
	'''
	The trainer class helps to easily set up a gym training session using an agent(representing the learning algorithm and the gym (being the environment)
	'''
	# TODO: Make training fail-safe by catching "not connected to server" and save the current state to disk (see primitive examples, they can do it)

	def __init__(self):
		# initialize random seed
		np.random.seed(int(time.time()))

		cid = p.connect(p.SHARED_MEMORY) # only show graphics if the browser is already running....
		self.visualize = (cid >= 0)
		if cid < 0:
			cid = p.connect(p.DIRECT)  # If no shared memory browser is active, we switch to headless mode (DIRECT is much faster)

	def setup_exercise(self, opts):

			# setup agent
			agent = agent_register.make(opts.agent, opts=opts)

			# setup environment
			env = gym.make(opts.env)
			if self.visualize:
				mode = "human"
			else:
				mode = "none"
			env.render(mode=mode)
			
			# configurations
			env.seed(int(time.time()))
			#env.configureActions(agent.metadata['discrete_actions']) # configure environment to accepts discrete actions
			if agent.metadata['discrete_actions']:
				agent.configure(env.observation_space.shape, env.action_space.n) # configure agent to use the environment properties
			else:
				agent.configure(env.observation_space.shape, env.action_space.shape[0]) # configure agent to use the environment properties

			if opts.use_latest:
				properties = kerasrl_utils.get_latest_save("checkpoints/", opts.agent, opts.env, 0)
				if properties == []:
					print("No previous weight saves found for %s-%s" % (opts.agent, opts.env))
				else:
					opts.load_file = "checkpoints/%s-%s-%s.h5" % (properties[0], properties[1], properties[2])
					print("Continue from [%s] " % opts.load_file)

			if opts.load_file is not None:
				print("loading weights from [%s]" % opts.load_file)
				agent.load_weights(opts.load_file)
			
			# Okay, now it's time to learn something! We visualize the training here for show, but this
			# slows down training quite a lot. You can always safely abort the training prematurely using
			# Ctrl + C.
			agent.train(env, nb_steps=opts.train_for, visualize=self.visualize, verbosity=1)
			
			# After training is done, we save the final weights.
			if opts.save_file is not None:
				print("saving weights to [%s]" % opts.save_file)
				agent.save_weights(opts.save_file, overwrite=True)

			# Finally, evaluate our algorithm.
			agent.test(env, nb_episodes=opts.test_for, visualize=self.visualize)

if __name__ == "__main__":
	"""
    You can also run the trainer as a main class if you want to start your own agent/environment combination. If you know your precise arguments, just run this as your main.
	"""

	trainer = Trainer.Trainer()

	parser = argparse.ArgumentParser()

	# add all parsing options
	Trainer.add_opts(parser)

	opts, unknown = parser.parse_known_args() # parse agent and environment to add their opts

	exec("from agents import %s" % opts.agent) # import agent type
	exec("from envs import %s" % opts.env) # import env type
	exec("%s.add_opts(parser)" % opts.agent)
	exec("%s.add_opts(parser)" % opts.env)

	# parse arguments
	opts, unknown = parser.parse_known_args()
	print("OPTS", opts)
	print("UNKNOWN", unknown)

	
	trainer.setup_exercise(opts)