import tensorflow as tf
from tensorflow_probability import distributions as tfd

from dreamerv2 import agent
from dreamerv2 import common


class Random(common.Module):

    def __init__(self, config, act_space, wm, tfstep, reward):
        self.config = config
        self.act_space = self.act_space

    def actor(self, feat):
        shape = feat.shape[:-1] + self.act_space.shape
        if self.config.actor.dist == 'onehot':
            return common.OneHotDist(tf.zeros(shape))
        else:
            dist = tfd.Uniform(-tf.ones(shape), tf.ones(shape))
            return tfd.Independent(dist, 1)

    def train(self, start, context, data):
        return None, {}


class Plan2Explore(common.Module):

    def __init__(self, config, act_space, wm, tfstep, reward):
        self.config = config
        self.reward = reward
        self.wm = wm
        self.ac = agent.ActorCritic(config, act_space, tfstep)
        self.actor = self.ac.actor
        stoch_size = config.rssm.stoch
        if config.rssm.discrete:
            stoch_size *= config.rssm.discrete
        size = {
            'embed': 32 * config.encoder.cnn_depth,
            'stoch': stoch_size,
            'deter': config.rssm.deter,
            'feat': config.rssm.stoch + config.rssm.deter,
        }[self.config.disag_target]
        self._networks = [
            common.MLP(size, **config.expl_head)
            for _ in range(config.disag_models)]
        self.opt = common.Optimizer('expl', **config.expl_opt)
        self.extr_rewnorm = common.StreamNorm(**self.config.expl_reward_norm)
        self.intr_rewnorm = common.StreamNorm(**self.config.expl_reward_norm)

    def train(self, start, context, data):
        metrics = {}
        stoch = start['stoch']
        if self.config.rssm.discrete:
            stoch = tf.reshape(
                stoch, stoch.shape[:-2] + (stoch.shape[-2] * stoch.shape[-1]))
        target = {
            'embed': context['embed'],
            'stoch': stoch,
            'deter': start['deter'],
            'feat': context['feat'],
        }[self.config.disag_target]
        inputs = context['feat']
        if self.config.disag_action_cond:
            action = tf.cast(data['action'], inputs.dtype)
            inputs = tf.concat([inputs, action], -1)
        metrics.update(self._train_ensemble(inputs, target))
        metrics.update(self.ac.train(
            self.wm, start, data['is_terminal'], self._intr_reward))
        return None, metrics

    def _intr_reward(self, seq):
        inputs = seq['feat']
        if self.config.disag_action_cond:
            action = tf.cast(seq['action'], inputs.dtype)
            inputs = tf.concat([inputs, action], -1)
        preds = [head(inputs).mode() for head in self._networks]
        disag = tf.tensor(preds).std(0).mean(-1)
        if self.config.disag_log:
            disag = tf.math.log(disag)
        reward = self.config.expl_intr_scale * self.intr_rewnorm(disag)[0]
        if self.config.expl_extr_scale:
            reward += self.config.expl_extr_scale * self.extr_rewnorm(
                self.reward(seq))[0]
        return reward

    def _train_ensemble(self, inputs, targets):
        if self.config.disag_offset:
            targets = targets[:, self.config.disag_offset:]
            inputs = inputs[:, :-self.config.disag_offset]
        targets = tf.stop_gradient(targets)
        inputs = tf.stop_gradient(inputs)
        with tf.GradientTape() as tape:
            preds = [head(inputs) for head in self._networks]
            loss = -sum([pred.log_prob(targets).mean() for pred in preds])
        metrics = self.opt(tape, loss, self._networks)
        return metrics


class ModelLoss(common.Module):

    def __init__(self, config, act_space, wm, tfstep, reward):
        self.config = config
        self.reward = reward
        self.wm = wm
        self.ac = agent.ActorCritic(config, act_space, tfstep)
        self.actor = self.ac.actor
        self.head = common.MLP([], **self.config.expl_head)
        self.opt = common.Optimizer('expl', **self.config.expl_opt)

    def train(self, start, context, data):
        metrics = {}
        target = tf.cast(context[self.config.expl_model_loss], tf.float32)
        with tf.GradientTape() as tape:
            loss = -self.head(context['feat']).log_prob(target).mean()
        metrics.update(self.opt(tape, loss, self.head))
        metrics.update(self.ac.train(
            self.wm, start, data['is_terminal'], self._intr_reward))
        return None, metrics

    def _intr_reward(self, seq):
        reward = self.config.expl_intr_scale * self.head(seq['feat']).mode()
        if self.config.expl_extr_scale:
            reward += self.config.expl_extr_scale * self.reward(seq)
        return reward