import os.path as osp

import time

import joblib

import numpy as np

import tensorflow as tf

from baselines import logger

from baselines.common import set_global_seeds, explained_variance

from baselines.common.runners import AbstractEnvRunner

from baselines.common import tf_util

from baselines.a2c.utils import discount_with_dones

from baselines.a2c.utils import Scheduler, make_path, find_trainable_variables

from baselines.a2c.utils import cat_entropy, mse

class Model(object):

    def __init__(self, policy, ob_space, ac_space, nenvs, nsteps,

            ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4,

            alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6), lrschedule='linear'):

        sess = tf_util.make_session()

        nbatch = nenvs*nsteps

        A = tf.placeholder(tf.int32, [nbatch])

        ADV = tf.placeholder(tf.float32, [nbatch])

        R = tf.placeholder(tf.float32, [nbatch])

        LR = tf.placeholder(tf.float32, [])

        step_model = policy(sess, ob_space, ac_space, nenvs, 1, reuse=False)

        train_model = policy(sess, ob_space, ac_space, nenvs*nsteps, nsteps, reuse=True)

        neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A)

        pg_loss = tf.reduce_mean(ADV * neglogpac)

        vf_loss = tf.reduce_mean(mse(tf.squeeze(train_model.vf), R))

        entropy = tf.reduce_mean(cat_entropy(train_model.pi))

        loss = pg_loss - entropy*ent_coef + vf_loss * vf_coef

        params = find_trainable_variables("model")

        grads = tf.gradients(loss, params)

        if max_grad_norm is not None:

            grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)

        grads = list(zip(grads, params))

        trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon)

        _train = trainer.apply_gradients(grads)

        lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule)

        def train(obs, states, rewards, masks, actions, values):

            advs = rewards - values

            for step in range(len(obs)):

                cur_lr = lr.value()

            td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, LR:cur_lr}

            if states is not None:

                td_map[train_model.S] = states

                td_map[train_model.M] = masks

            policy_loss, value_loss, policy_entropy, _ = sess.run(

                [pg_loss, vf_loss, entropy, _train],

                td_map

            )

            return policy_loss, value_loss, policy_entropy

        def save(save_path):

            ps = sess.run(params)

            make_path(osp.dirname(save_path))

            joblib.dump(ps, save_path)

        def load(load_path):

            loaded_params = joblib.load(load_path)

            restores = []

            for p, loaded_p in zip(params, loaded_params):

                restores.append(p.assign(loaded_p))

            sess.run(restores)

        self.train = train

        self.train_model = train_model

        self.step_model = step_model

        self.step = step_model.step

        self.value = step_model.value

        self.initial_state = step_model.initial_state

        self.save = save

        self.load = load

        tf.global_variables_initializer().run(session=sess)

class Runner(AbstractEnvRunner):

    def __init__(self, env, model, nsteps=5, gamma=0.99):

        super().__init__(env=env, model=model, nsteps=nsteps)

        self.gamma = gamma

    def run(self):

        mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [],[],[],[],[]

        mb_states = self.states

        for n in range(self.nsteps):

            actions, values, states, _ = self.model.step(self.obs, self.states, self.dones)

            mb_obs.append(np.copy(self.obs))

            mb_actions.append(actions)

            mb_values.append(values)

            mb_dones.append(self.dones)

            obs, rewards, dones, _ = self.env.step(actions)

            self.states = states

            self.dones = dones

            for n, done in enumerate(dones):

                if done:

                    self.obs[n] = self.obs[n]*0

            self.obs = obs

            mb_rewards.append(rewards)

        mb_dones.append(self.dones)

        #batch of steps to batch of rollouts

        mb_obs = np.asarray(mb_obs, dtype=np.uint8).swapaxes(1, 0).reshape(self.batch_ob_shape)

        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)

        mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0)

        mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)

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

        mb_masks = mb_dones[:, :-1]

        mb_dones = mb_dones[:, 1:]

        last_values = self.model.value(self.obs, self.states, self.dones).tolist()

        #discount/bootstrap off value fn

        for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):

            rewards = rewards.tolist()

            dones = dones.tolist()

            if dones[-1] == 0:

                rewards = discount_with_dones(rewards+[value], dones+[0], self.gamma)[:-1]

            else:

                rewards = discount_with_dones(rewards, dones, self.gamma)

            mb_rewards[n] = rewards

        mb_rewards = mb_rewards.flatten()

        mb_actions = mb_actions.flatten()

        mb_values = mb_values.flatten()

        mb_masks = mb_masks.flatten()

        return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values

def learn(policy, env, seed, nsteps=5, total_timesteps=int(80e6), vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, lrschedule='linear', epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=100):

    tf.reset_default_graph()

    set_global_seeds(seed)

    nenvs = env.num_envs

    ob_space = env.observation_space

    ac_space = env.action_space

    model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,

        max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)

    runner = Runner(env, model, nsteps=nsteps, gamma=gamma)

    nbatch = nenvs*nsteps

    tstart = time.time()

    for update in range(1, total_timesteps//nbatch+1):

        obs, states, rewards, masks, actions, values = runner.run()

        policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)

        nseconds = time.time()-tstart

        fps = int((update*nbatch)/nseconds)

        if update % log_interval == 0 or update == 1:

            ev = explained_variance(values, rewards)

            logger.record_tabular("nupdates", update)

            logger.record_tabular("total_timesteps", update*nbatch)

            logger.record_tabular("fps", fps)

            logger.record_tabular("policy_entropy", float(policy_entropy))

            logger.record_tabular("value_loss", float(value_loss))

            logger.record_tabular("explained_variance", float(ev))

            logger.dump_tabular()

    env.close()