<gh_stars>0
from gym_workflow.envs.montage_wf_env import MontageWfEnv
from gym_workflow.envs.database import DatabaseEnv
from gym.spaces import Discrete
import random
import sys
import numpy as np


class Version4(MontageWfEnv):
	"""
		@version 4.0:
			- Using Percentage as the value to gain reward.
			- 10% of the run will be the sample of experiment scales
	"""
	
	def __init__(self):
		# Montage Experiment Variable
		super(Version4, self).__init__()
		self.action_space = Discrete(3)

		self.observation_space = Discrete(3)  # , Discrete(8), Discrete(3)

		# Episode Conf
		# Best exec_time: None or 1, depends on reward version
		self.best_exec_time = None
		self.last_exec_time = None
		self.last_action = None
		self.last_reward = None
		self.total_reward = 0.0
		self.all_makespan_record = list()
		self.all_benchmark_record = list()
		# 0: Ntg, 1: improve, 2: degrade
		self.is_improve = 0
		self.seed()
		self.reset()

	def step(self, action, training=False):
		assert self.action_space.contains(action)

		reward = 0.0
		self.last_action = action
		done = False
		if action == 1:
			self.clusters_size += 1
		elif action == 2:
			self.clusters_size -= 1

		# Range Guarding Function
		if self.clusters_size <= 0:
			reward -= 1.0
			self.clusters_size = 1
		elif self.clusters_size > 30:
			reward -= 1.0
			self.clusters_size = 30
		else:
			# res = self.run_static_experiment(self.clusters_size, self.clusters_num)
			res = self.run_demo_cn_gen_experiment(self.clusters_size)

			self.all_makespan_record.append(res)
			self.exec_time = res

			# Rewarding / Penalty Judgement
			percentile = np.percentile(self.all_makespan_record, 10)
			benchmark = np.mean(self.all_benchmark_record)

			# Calc improve percentage
			if len(self.all_benchmark_record) == 0:
				self.all_benchmark_record.append(percentile)
			elif abs(percentile - benchmark) / benchmark * 100 > 10:
				self.all_benchmark_record.append(percentile)
				benchmark = np.mean(self.all_benchmark_record)

			if self.exec_time < benchmark:
				reward = 2
			else:
				reward = -1
			self.total_reward += reward
			if self.total_reward > 10:
				done = True

		return self._get_obs(), reward, done, {
			"exec": self.exec_time,
			"overhead": self.exec_time,
			"makespan": self.exec_time,
			"benchmark": np.mean(self.all_benchmark_record)
		}

	def render(self, mode='human'):
		outfile = StringIO() if mode == 'ansi' else sys.stdout
		init_msg = "Current Experiment Parameters: degree: %d\t cluster.size: %d\t cluster.num: %d\t\n" % (
			self.degree, self.clusters_size, self.clusters_num)
		outfile.write(init_msg)
		# if self.last_action is not None:
		# 	cs, cn = action
		result_str = "Current Execution Time: \t"
		expect_str = "Best Execution Time: \t"
		action_str = "Current Action: \t"
		# Process Outputs
		outfile.write(result_str + (" %s " % self.exec_time) + "\n")
		outfile.write(expect_str + (" %s " % self.best_exec_time) + "\n")
		outfile.write(action_str + (" %s " % self.last_action) + "\n")

		return outfile

	def reset(self):
		# Reset method should always return a new sets of episode settings
		# self.degree = 0.1
		# self.clusters_size = 1
		# self.clusters_num = 1
		# self.band_num = 1
		# self.best_exec_time = None
		if self.exec_time is not None:
			self.last_exec_time = self.exec_time
		self.wall_time = None
		self.cum_wall_time = None
		self.total_reward = 0
		self.clusters_size = np.random.randint(1, 30)
		self.clusters_num = np.random.randint(1, 30)

		# print("Environment had been reset!")
		return self.clusters_size

	def _get_obs(self):
		return self.clusters_size
