import gym
import gfootball.env as grf_env
import numpy as np

class MultiAgentEnv(gym.Env):

	def __init__(self, scenario, num_controlled_lagents, num_controlled_ragents, reward_type, render):
		self.env = grf_env.create_environment(
			env_name=scenario,
			stacked=False,
			representation='multiagent',
			rewards=reward_type,
			write_goal_dumps=False,
			write_full_episode_dumps=False,
			render=render,
			dump_frequency=0,
			logdir='/tmp/maddpg_test',
			extra_players=None,
			number_of_left_players_agent_controls=num_controlled_lagents,
			number_of_right_players_agent_controls=num_controlled_ragents,
			channel_dimensions=(3, 3)
			)
		self.num_controlled_lagents = num_controlled_lagents
		self.num_controlled_ragents = num_controlled_ragents
		self.num_controlled_agents = num_controlled_lagents + num_controlled_ragents
		self.num_lagents = self.env.num_lteam_players
		self.num_ragents = self.env.num_rteam_players
		if self.num_controlled_agents > 1:
			action_space = gym.spaces.Discrete(self.env.action_space.nvec[1])
		else:
			action_space = self.env.action_space
		if self.num_controlled_agents > 1:
			observation_space = gym.spaces.Box(
		        low=self.env.observation_space.low[0],
		        high=self.env.observation_space.high[0],
				dtype=self.env.observation_space.dtype)
		else:
			observation_space = gym.spaces.Box(
		        low=self.env.observation_space.low,
		        high=self.env.observation_space.high,
				dtype=self.env.observation_space.dtype)			
		self.action_space = [action_space for _ in range(self.num_controlled_agents)]
		self.observation_space = [observation_space for _ in range(self.num_controlled_agents)]

	def seed(self, seed=None):
		if seed is None:
			np.random.seed(1)
		else:
			np.random.seed(seed)

	# def extract_obs(self, original_obs):
	# 	lteam_pos = original_obs[0 : 2*self.num_lagents]
	# 	rteam_pos = original_obs[4*self.num_lagents : 4*self.num_lagents+2*self.num_ragents]
	# 	ball_pos = original_obs[4*self.num_lagents+4*self.num_ragents : 4*self.num_lagents+4*self.num_ragents+3]
	# 	ball_side = original_obs[4*self.num_lagents+4*self.num_ragents+6 : 4*self.num_lagents+4*self.num_ragents+9]
	# 	game_mode = original_obs[4*self.num_lagents+4*self.num_ragents+9 : 4*self.num_lagents+4*self.num_ragents+16]
	# 	return np.concatenate((lteam_pos, rteam_pos, ball_pos, ball_side, game_mode))	

	def reset(self):
		original_obs = self.env.reset()
		obs = []
		for x in range(self.num_controlled_agents):
			if self.num_controlled_agents > 1:
				obs.append(original_obs[x])
			else:
				obs.append(original_obs)
		return obs

	def step(self, actions):
		o, r, d, i = self.env.step(actions)
		if self.num_controlled_agents == 1:
			o = [o]
			r = [r]
		next_obs = [o[x] for x in range(len(o))]
		rewards = r
		dones = [d for x in range(len(o))]
		infos = i

		return next_obs, rewards, dones, infos


b676d7ce58f425cfb26b63f896ddd52f90d94f71
<mask token>


class MultiAgentEnv(gym.Env):
    <mask token>
    <mask token>
    <mask token>

    def step(self, actions):
        o, r, d, i = self.env.step(actions)
        if self.num_controlled_agents == 1:
            o = [o]
            r = [r]
        next_obs = [o[x] for x in range(len(o))]
        rewards = r
        dones = [d for x in range(len(o))]
        infos = i
        return next_obs, rewards, dones, infos

<mask token>


class MultiAgentEnv(gym.Env):

    def __init__(self, scenario, num_controlled_lagents,
        num_controlled_ragents, reward_type, render):
        self.env = grf_env.create_environment(env_name=scenario, stacked=
            False, representation='multiagent', rewards=reward_type,
            write_goal_dumps=False, write_full_episode_dumps=False, render=
            render, dump_frequency=0, logdir='/tmp/maddpg_test',
            extra_players=None, number_of_left_players_agent_controls=
            num_controlled_lagents, number_of_right_players_agent_controls=
            num_controlled_ragents, channel_dimensions=(3, 3))
        self.num_controlled_lagents = num_controlled_lagents
        self.num_controlled_ragents = num_controlled_ragents
        self.num_controlled_agents = (num_controlled_lagents +
            num_controlled_ragents)
        self.num_lagents = self.env.num_lteam_players
        self.num_ragents = self.env.num_rteam_players
        if self.num_controlled_agents > 1:
            action_space = gym.spaces.Discrete(self.env.action_space.nvec[1])
        else:
            action_space = self.env.action_space
        if self.num_controlled_agents > 1:
            observation_space = gym.spaces.Box(low=self.env.
                observation_space.low[0], high=self.env.observation_space.
                high[0], dtype=self.env.observation_space.dtype)
        else:
            observation_space = gym.spaces.Box(low=self.env.
                observation_space.low, high=self.env.observation_space.high,
                dtype=self.env.observation_space.dtype)
        self.action_space = [action_space for _ in range(self.
            num_controlled_agents)]
        self.observation_space = [observation_space for _ in range(self.
            num_controlled_agents)]

    def seed(self, seed=None):
        if seed is None:
            np.random.seed(1)
        else:
            np.random.seed(seed)
    <mask token>

    def step(self, actions):
        o, r, d, i = self.env.step(actions)
        if self.num_controlled_agents == 1:
            o = [o]
            r = [r]
        next_obs = [o[x] for x in range(len(o))]
        rewards = r
        dones = [d for x in range(len(o))]
        infos = i
        return next_obs, rewards, dones, infos

<mask token>


class MultiAgentEnv(gym.Env):

    def __init__(self, scenario, num_controlled_lagents,
        num_controlled_ragents, reward_type, render):
        self.env = grf_env.create_environment(env_name=scenario, stacked=
            False, representation='multiagent', rewards=reward_type,
            write_goal_dumps=False, write_full_episode_dumps=False, render=
            render, dump_frequency=0, logdir='/tmp/maddpg_test',
            extra_players=None, number_of_left_players_agent_controls=
            num_controlled_lagents, number_of_right_players_agent_controls=
            num_controlled_ragents, channel_dimensions=(3, 3))
        self.num_controlled_lagents = num_controlled_lagents
        self.num_controlled_ragents = num_controlled_ragents
        self.num_controlled_agents = (num_controlled_lagents +
            num_controlled_ragents)
        self.num_lagents = self.env.num_lteam_players
        self.num_ragents = self.env.num_rteam_players
        if self.num_controlled_agents > 1:
            action_space = gym.spaces.Discrete(self.env.action_space.nvec[1])
        else:
            action_space = self.env.action_space
        if self.num_controlled_agents > 1:
            observation_space = gym.spaces.Box(low=self.env.
                observation_space.low[0], high=self.env.observation_space.
                high[0], dtype=self.env.observation_space.dtype)
        else:
            observation_space = gym.spaces.Box(low=self.env.
                observation_space.low, high=self.env.observation_space.high,
                dtype=self.env.observation_space.dtype)
        self.action_space = [action_space for _ in range(self.
            num_controlled_agents)]
        self.observation_space = [observation_space for _ in range(self.
            num_controlled_agents)]

    def seed(self, seed=None):
        if seed is None:
            np.random.seed(1)
        else:
            np.random.seed(seed)

    def reset(self):
        original_obs = self.env.reset()
        obs = []
        for x in range(self.num_controlled_agents):
            if self.num_controlled_agents > 1:
                obs.append(original_obs[x])
            else:
                obs.append(original_obs)
        return obs

    def step(self, actions):
        o, r, d, i = self.env.step(actions)
        if self.num_controlled_agents == 1:
            o = [o]
            r = [r]
        next_obs = [o[x] for x in range(len(o))]
        rewards = r
        dones = [d for x in range(len(o))]
        infos = i
        return next_obs, rewards, dones, infos

import gym
import gfootball.env as grf_env
import numpy as np


class MultiAgentEnv(gym.Env):

    def __init__(self, scenario, num_controlled_lagents,
        num_controlled_ragents, reward_type, render):
        self.env = grf_env.create_environment(env_name=scenario, stacked=
            False, representation='multiagent', rewards=reward_type,
            write_goal_dumps=False, write_full_episode_dumps=False, render=
            render, dump_frequency=0, logdir='/tmp/maddpg_test',
            extra_players=None, number_of_left_players_agent_controls=
            num_controlled_lagents, number_of_right_players_agent_controls=
            num_controlled_ragents, channel_dimensions=(3, 3))
        self.num_controlled_lagents = num_controlled_lagents
        self.num_controlled_ragents = num_controlled_ragents
        self.num_controlled_agents = (num_controlled_lagents +
            num_controlled_ragents)
        self.num_lagents = self.env.num_lteam_players
        self.num_ragents = self.env.num_rteam_players
        if self.num_controlled_agents > 1:
            action_space = gym.spaces.Discrete(self.env.action_space.nvec[1])
        else:
            action_space = self.env.action_space
        if self.num_controlled_agents > 1:
            observation_space = gym.spaces.Box(low=self.env.
                observation_space.low[0], high=self.env.observation_space.
                high[0], dtype=self.env.observation_space.dtype)
        else:
            observation_space = gym.spaces.Box(low=self.env.
                observation_space.low, high=self.env.observation_space.high,
                dtype=self.env.observation_space.dtype)
        self.action_space = [action_space for _ in range(self.
            num_controlled_agents)]
        self.observation_space = [observation_space for _ in range(self.
            num_controlled_agents)]

    def seed(self, seed=None):
        if seed is None:
            np.random.seed(1)
        else:
            np.random.seed(seed)

    def reset(self):
        original_obs = self.env.reset()
        obs = []
        for x in range(self.num_controlled_agents):
            if self.num_controlled_agents > 1:
                obs.append(original_obs[x])
            else:
                obs.append(original_obs)
        return obs

    def step(self, actions):
        o, r, d, i = self.env.step(actions)
        if self.num_controlled_agents == 1:
            o = [o]
            r = [r]
        next_obs = [o[x] for x in range(len(o))]
        rewards = r
        dones = [d for x in range(len(o))]
        infos = i
        return next_obs, rewards, dones, infos

import gym
import gfootball.env as grf_env
import numpy as np

class MultiAgentEnv(gym.Env):

	def __init__(self, scenario, num_controlled_lagents, num_controlled_ragents, reward_type, render):
		self.env = grf_env.create_environment(
			env_name=scenario,
			stacked=False,
			representation='multiagent',
			rewards=reward_type,
			write_goal_dumps=False,
			write_full_episode_dumps=False,
			render=render,
			dump_frequency=0,
			logdir='/tmp/maddpg_test',
			extra_players=None,
			number_of_left_players_agent_controls=num_controlled_lagents,
			number_of_right_players_agent_controls=num_controlled_ragents,
			channel_dimensions=(3, 3)
			)
		self.num_controlled_lagents = num_controlled_lagents
		self.num_controlled_ragents = num_controlled_ragents
		self.num_controlled_agents = num_controlled_lagents + num_controlled_ragents
		self.num_lagents = self.env.num_lteam_players
		self.num_ragents = self.env.num_rteam_players
		if self.num_controlled_agents > 1:
			action_space = gym.spaces.Discrete(self.env.action_space.nvec[1])
		else:
			action_space = self.env.action_space
		if self.num_controlled_agents > 1:
			observation_space = gym.spaces.Box(
		        low=self.env.observation_space.low[0],
		        high=self.env.observation_space.high[0],
				dtype=self.env.observation_space.dtype)
		else:
			observation_space = gym.spaces.Box(
		        low=self.env.observation_space.low,
		        high=self.env.observation_space.high,
				dtype=self.env.observation_space.dtype)			
		self.action_space = [action_space for _ in range(self.num_controlled_agents)]
		self.observation_space = [observation_space for _ in range(self.num_controlled_agents)]

	def seed(self, seed=None):
		if seed is None:
			np.random.seed(1)
		else:
			np.random.seed(seed)

	# def extract_obs(self, original_obs):
	# 	lteam_pos = original_obs[0 : 2*self.num_lagents]
	# 	rteam_pos = original_obs[4*self.num_lagents : 4*self.num_lagents+2*self.num_ragents]
	# 	ball_pos = original_obs[4*self.num_lagents+4*self.num_ragents : 4*self.num_lagents+4*self.num_ragents+3]
	# 	ball_side = original_obs[4*self.num_lagents+4*self.num_ragents+6 : 4*self.num_lagents+4*self.num_ragents+9]
	# 	game_mode = original_obs[4*self.num_lagents+4*self.num_ragents+9 : 4*self.num_lagents+4*self.num_ragents+16]
	# 	return np.concatenate((lteam_pos, rteam_pos, ball_pos, ball_side, game_mode))	

	def reset(self):
		original_obs = self.env.reset()
		obs = []
		for x in range(self.num_controlled_agents):
			if self.num_controlled_agents > 1:
				obs.append(original_obs[x])
			else:
				obs.append(original_obs)
		return obs

	def step(self, actions):
		o, r, d, i = self.env.step(actions)
		if self.num_controlled_agents == 1:
			o = [o]
			r = [r]
		next_obs = [o[x] for x in range(len(o))]
		rewards = r
		dones = [d for x in range(len(o))]
		infos = i

		return next_obs, rewards, dones, infos

