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import numpy as np
from baselines.common.runners import AbstractEnvRunner
from baselines.common.vec_env.vec_frame_stack import VecFrameStack
from gym import spaces


class Runner(AbstractEnvRunner):

    def __init__(self, env, model, nsteps):
        super().__init__(env=env, model=model, nsteps=nsteps)
        assert isinstance(env.action_space, spaces.Discrete), 'This ACER implementation works only with discrete action spaces!'
        assert isinstance(env, VecFrameStack)

        self.nact = env.action_space.n
        nenv = self.nenv
        self.nbatch = nenv * nsteps
        self.batch_ob_shape = (nenv*(nsteps+1),) + env.observation_space.shape

        self.obs = env.reset()
        self.obs_dtype = env.observation_space.dtype
        self.ac_dtype = env.action_space.dtype
        self.nstack = self.env.nstack
        self.nc = self.batch_ob_shape[-1] // self.nstack


    def run(self):
        # enc_obs = np.split(self.obs, self.nstack, axis=3)  # so now list of obs steps
        enc_obs = np.split(self.env.stackedobs, self.env.nstack, axis=-1)
        mb_obs, mb_actions, mb_mus, mb_dones, mb_rewards = [], [], [], [], []
        for _ in range(self.nsteps):
            actions, mus, states = self.model._step(self.obs, S=self.states, M=self.dones)
            mb_obs.append(np.copy(self.obs))
            mb_actions.append(actions)
            mb_mus.append(mus)
            mb_dones.append(self.dones)
            obs, rewards, dones, _ = self.env.step(actions)
            # states information for statefull models like LSTM
            self.states = states
            self.dones = dones
            self.obs = obs
            mb_rewards.append(rewards)
            enc_obs.append(obs[..., -self.nc:])
        mb_obs.append(np.copy(self.obs))
        mb_dones.append(self.dones)

        enc_obs = np.asarray(enc_obs, dtype=self.obs_dtype).swapaxes(1, 0)
        mb_obs = np.asarray(mb_obs, dtype=self.obs_dtype).swapaxes(1, 0)
        mb_actions = np.asarray(mb_actions, dtype=self.ac_dtype).swapaxes(1, 0)
        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
        mb_mus = np.asarray(mb_mus, dtype=np.float32).swapaxes(1, 0)

        mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)

        mb_masks = mb_dones # Used for statefull models like LSTM's to mask state when done
        mb_dones = mb_dones[:, 1:] # Used for calculating returns. The dones array is now aligned with rewards

        # shapes are now [nenv, nsteps, []]
        # When pulling from buffer, arrays will now be reshaped in place, preventing a deep copy.

        return enc_obs, mb_obs, mb_actions, mb_rewards, mb_mus, mb_dones, mb_masks

	import numpy as np
	from baselines.common.runners import AbstractEnvRunner
	from baselines.common.vec_env.vec_frame_stack import VecFrameStack
	from gym import spaces


	class Runner(AbstractEnvRunner):

	def __init__(self, env, model, nsteps):
	super().__init__(env=env, model=model, nsteps=nsteps)
	assert isinstance(env.action_space, spaces.Discrete), 'This ACER implementation works only with discrete action spaces!'
	assert isinstance(env, VecFrameStack)

	self.nact = env.action_space.n
	nenv = self.nenv
	self.nbatch = nenv * nsteps
	self.batch_ob_shape = (nenv*(nsteps+1),) + env.observation_space.shape

	self.obs = env.reset()
	self.obs_dtype = env.observation_space.dtype
	self.ac_dtype = env.action_space.dtype
	self.nstack = self.env.nstack
	self.nc = self.batch_ob_shape[-1] // self.nstack


	def run(self):
	# enc_obs = np.split(self.obs, self.nstack, axis=3) # so now list of obs steps
	enc_obs = np.split(self.env.stackedobs, self.env.nstack, axis=-1)
	mb_obs, mb_actions, mb_mus, mb_dones, mb_rewards = [], [], [], [], []
	for _ in range(self.nsteps):
	actions, mus, states = self.model._step(self.obs, S=self.states, M=self.dones)
	mb_obs.append(np.copy(self.obs))
	mb_actions.append(actions)
	mb_mus.append(mus)
	mb_dones.append(self.dones)
	obs, rewards, dones, _ = self.env.step(actions)
	# states information for statefull models like LSTM
	self.states = states
	self.dones = dones
	self.obs = obs
	mb_rewards.append(rewards)
	enc_obs.append(obs[..., -self.nc:])
	mb_obs.append(np.copy(self.obs))
	mb_dones.append(self.dones)

	enc_obs = np.asarray(enc_obs, dtype=self.obs_dtype).swapaxes(1, 0)
	mb_obs = np.asarray(mb_obs, dtype=self.obs_dtype).swapaxes(1, 0)
	mb_actions = np.asarray(mb_actions, dtype=self.ac_dtype).swapaxes(1, 0)
	mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
	mb_mus = np.asarray(mb_mus, dtype=np.float32).swapaxes(1, 0)

	mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)

	mb_masks = mb_dones # Used for statefull models like LSTM's to mask state when done
	mb_dones = mb_dones[:, 1:] # Used for calculating returns. The dones array is now aligned with rewards

	# shapes are now [nenv, nsteps, []]
	# When pulling from buffer, arrays will now be reshaped in place, preventing a deep copy.

	return enc_obs, mb_obs, mb_actions, mb_rewards, mb_mus, mb_dones, mb_masks