"""Gaussian GRU Policy.

A policy represented by a Gaussian distribution
which is parameterized by a Gated Recurrent Unit (GRU).
"""
# pylint: disable=wrong-import-order
import akro
import numpy as np
import tensorflow as tf

from garage.experiment import deterministic
from garage.tf.models import GaussianGRUModel
from garage.tf.policies.policy import StochasticPolicy


class GaussianGRUPolicy(StochasticPolicy):
    """Gaussian GRU Policy.

    A policy represented by a Gaussian distribution
    which is parameterized by a Gated Recurrent Unit (GRU).

    Args:
        env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
        name (str): Model name, also the variable scope.
        hidden_dim (int): Hidden dimension for GRU cell for mean.
        hidden_nonlinearity (Callable): Activation function for intermediate
            dense layer(s). It should return a tf.Tensor. Set it to
            None to maintain a linear activation.
        hidden_w_init (Callable): Initializer function for the weight
            of intermediate dense layer(s). The function should return a
            tf.Tensor.
        hidden_b_init (Callable): Initializer function for the bias
            of intermediate dense layer(s). The function should return a
            tf.Tensor.
        recurrent_nonlinearity (Callable): Activation function for recurrent
            layers. It should return a tf.Tensor. Set it to None to
            maintain a linear activation.
        recurrent_w_init (Callable): Initializer function for the weight
            of recurrent layer(s). The function should return a
            tf.Tensor.
        output_nonlinearity (Callable): Activation function for output dense
            layer. It should return a tf.Tensor. Set it to None to
            maintain a linear activation.
        output_w_init (Callable): Initializer function for the weight
            of output dense layer(s). The function should return a
            tf.Tensor.
        output_b_init (Callable): Initializer function for the bias
            of output dense layer(s). The function should return a
            tf.Tensor.
        hidden_state_init (Callable): Initializer function for the
            initial hidden state. The functino should return a tf.Tensor.
        hidden_state_init_trainable (bool): Bool for whether the initial
            hidden state is trainable.
        learn_std (bool): Is std trainable.
        std_share_network (bool): Boolean for whether mean and std share
            the same network.
        init_std (float): Initial value for std.
        layer_normalization (bool): Bool for using layer normalization or not.
        state_include_action (bool): Whether the state includes action.
            If True, input dimension will be
            (observation dimension + action dimension).

    """

    def __init__(self,
                 env_spec,
                 hidden_dim=32,
                 name='GaussianGRUPolicy',
                 hidden_nonlinearity=tf.nn.tanh,
                 hidden_w_init=tf.initializers.glorot_uniform(
                     seed=deterministic.get_tf_seed_stream()),
                 hidden_b_init=tf.zeros_initializer(),
                 recurrent_nonlinearity=tf.nn.sigmoid,
                 recurrent_w_init=tf.initializers.glorot_uniform(
                     seed=deterministic.get_tf_seed_stream()),
                 output_nonlinearity=None,
                 output_w_init=tf.initializers.glorot_uniform(
                     seed=deterministic.get_tf_seed_stream()),
                 output_b_init=tf.zeros_initializer(),
                 hidden_state_init=tf.zeros_initializer(),
                 hidden_state_init_trainable=False,
                 learn_std=True,
                 std_share_network=False,
                 init_std=1.0,
                 layer_normalization=False,
                 state_include_action=True):
        if not isinstance(env_spec.action_space, akro.Box):
            raise ValueError('GaussianGRUPolicy only works with '
                             'akro.Box action space, but not {}'.format(
                                 env_spec.action_space))
        super().__init__(name, env_spec)
        self._obs_dim = env_spec.observation_space.flat_dim
        self._action_dim = env_spec.action_space.flat_dim

        self._hidden_dim = hidden_dim
        self._hidden_nonlinearity = hidden_nonlinearity
        self._hidden_w_init = hidden_w_init
        self._hidden_b_init = hidden_b_init
        self._recurrent_nonlinearity = recurrent_nonlinearity
        self._recurrent_w_init = recurrent_w_init
        self._output_nonlinearity = output_nonlinearity
        self._output_w_init = output_w_init
        self._output_b_init = output_b_init
        self._hidden_state_init = hidden_state_init
        self._hidden_state_init_trainable = hidden_state_init_trainable
        self._learn_std = learn_std
        self._std_share_network = std_share_network
        self._init_std = init_std
        self._layer_normalization = layer_normalization
        self._state_include_action = state_include_action

        if state_include_action:
            self._input_dim = self._obs_dim + self._action_dim
        else:
            self._input_dim = self._obs_dim

        self._f_step_mean_std = None

        self.model = GaussianGRUModel(
            output_dim=self._action_dim,
            hidden_dim=hidden_dim,
            name='GaussianGRUModel',
            hidden_nonlinearity=hidden_nonlinearity,
            hidden_w_init=hidden_w_init,
            hidden_b_init=hidden_b_init,
            recurrent_nonlinearity=recurrent_nonlinearity,
            recurrent_w_init=recurrent_w_init,
            output_nonlinearity=output_nonlinearity,
            output_w_init=output_w_init,
            output_b_init=output_b_init,
            hidden_state_init=hidden_state_init,
            hidden_state_init_trainable=hidden_state_init_trainable,
            layer_normalization=layer_normalization,
            learn_std=learn_std,
            std_share_network=std_share_network,
            init_std=init_std)

        self._prev_actions = None
        self._prev_hiddens = None
        self._dist = None
        self._init_hidden = None

        self._initialize()

    def _initialize(self):
        """Initialize policy."""
        with tf.compat.v1.variable_scope(self.name) as vs:
            self._variable_scope = vs
            state_input = tf.compat.v1.placeholder(shape=(None, None,
                                                          self._input_dim),
                                                   name='state_input',
                                                   dtype=tf.float32)
            step_input_var = tf.compat.v1.placeholder(shape=(None,
                                                             self._input_dim),
                                                      name='step_input',
                                                      dtype=tf.float32)
            step_hidden_var = tf.compat.v1.placeholder(
                shape=(None, self._hidden_dim),
                name='step_hidden_input',
                dtype=tf.float32)
            (self._dist, step_mean, step_log_std, step_hidden,
             self._init_hidden) = self.model.build(state_input, step_input_var,
                                                   step_hidden_var).outputs

        self._f_step_mean_std = (
            tf.compat.v1.get_default_session().make_callable(
                [step_mean, step_log_std, step_hidden],
                feed_list=[step_input_var, step_hidden_var]))

    def build(self, state_input, name=None):
        """Build policy.

        Args:
            state_input (tf.Tensor) : State input.
            name (str): Name of the policy, which is also the name scope.

        Returns:
            tfp.distributions.MultivariateNormalDiag: Policy distribution.
            tf.Tensor: Step means, with shape :math:`(N, S^*)`.
            tf.Tensor: Step log std, with shape :math:`(N, S^*)`.
            tf.Tensor: Step hidden state, with shape :math:`(N, S^*)`.
            tf.Tensor: Initial hidden state, with shape :math:`(S^*)`.

        """
        with tf.compat.v1.variable_scope(self._variable_scope):
            _, step_input_var, step_hidden_var = self.model.inputs
            return self.model.build(state_input,
                                    step_input_var,
                                    step_hidden_var,
                                    name=name)

    @property
    def input_dim(self):
        """int: Dimension of the policy input."""
        return self._input_dim

    @property
    def vectorized(self):
        """Vectorized or not.

        Returns:
            Bool: True if primitive supports vectorized operations.

        """
        return True

    def reset(self, do_resets=None):
        """Reset the policy.

        Note:
            If `do_resets` is None, it will be by default `np.array([True])`
            which implies the policy will not be "vectorized", i.e. number of
            parallel environments for training data sampling = 1.

        Args:
            do_resets (numpy.ndarray): Bool that indicates terminal state(s).

        """
        if do_resets is None:
            do_resets = np.array([True])
        if self._prev_actions is None or len(do_resets) != len(
                self._prev_actions):
            self._prev_actions = np.zeros(
                (len(do_resets), self.action_space.flat_dim))
            self._prev_hiddens = np.zeros((len(do_resets), self._hidden_dim))

        self._prev_actions[do_resets] = 0.
        self._prev_hiddens[do_resets] = self._init_hidden.eval()

    def get_action(self, observation):
        """Get single action from this policy for the input observation.

        Args:
            observation (numpy.ndarray): Observation from environment.

        Returns:
            numpy.ndarray: Actions
            dict: Predicted action and agent information.

        Note:
            It returns an action and a dict, with keys
            - mean (numpy.ndarray): Mean of the distribution.
            - log_std (numpy.ndarray): Log standard deviation of the
                distribution.
            - prev_action (numpy.ndarray): Previous action, only present if
                self._state_include_action is True.

        """
        actions, agent_infos = self.get_actions([observation])
        return actions[0], {k: v[0] for k, v in agent_infos.items()}

    def get_actions(self, observations):
        """Get multiple actions from this policy for the input observations.

        Args:
            observations (numpy.ndarray): Observations from environment.

        Returns:
            numpy.ndarray: Actions
            dict: Predicted action and agent information.

        Note:
            It returns an action and a dict, with keys
            - mean (numpy.ndarray): Means of the distribution.
            - log_std (numpy.ndarray): Log standard deviations of the
                distribution.
            - prev_action (numpy.ndarray): Previous action, only present if
                self._state_include_action is True.

        """
        observations = self.observation_space.flatten_n(observations)
        if self._state_include_action:
            assert self._prev_actions is not None
            all_input = np.concatenate([observations, self._prev_actions],
                                       axis=-1)
        else:
            all_input = observations
        means, log_stds, hidden_vec = self._f_step_mean_std(
            all_input, self._prev_hiddens)
        rnd = np.random.normal(size=means.shape)
        samples = rnd * np.exp(log_stds) + means
        samples = self.action_space.unflatten_n(samples)
        prev_actions = self._prev_actions
        self._prev_actions = samples
        self._prev_hiddens = hidden_vec
        agent_infos = dict(mean=means, log_std=log_stds)
        if self._state_include_action:
            agent_infos['prev_action'] = np.copy(prev_actions)
        return samples, agent_infos

    @property
    def distribution(self):
        """Policy distribution.

        Returns:
            tfp.Distribution.MultivariateNormalDiag: Policy distribution.

        """
        return self._dist

    @property
    def state_info_specs(self):
        """State info specifcation.

        Returns:
            List[str]: keys and shapes for the information related to the
                policy's state when taking an action.

        """
        if self._state_include_action:
            return [
                ('prev_action', (self._action_dim, )),
            ]

        return []

    def clone(self, name):
        """Return a clone of the policy.

        It copies the configuration of the primitive and also the parameters.

        Args:
            name (str): Name of the newly created policy. It has to be
                different from source policy if cloned under the same
                computational graph.

        Returns:
            garage.tf.policies.GaussianGRUPolicy: Newly cloned policy.

        """
        new_policy = self.__class__(
            name=name,
            env_spec=self._env_spec,
            hidden_dim=self._hidden_dim,
            hidden_nonlinearity=self._hidden_nonlinearity,
            hidden_w_init=self._hidden_w_init,
            hidden_b_init=self._hidden_b_init,
            recurrent_nonlinearity=self._recurrent_nonlinearity,
            recurrent_w_init=self._recurrent_w_init,
            output_nonlinearity=self._output_nonlinearity,
            output_w_init=self._output_w_init,
            output_b_init=self._output_b_init,
            hidden_state_init=self._hidden_state_init,
            hidden_state_init_trainable=self._hidden_state_init_trainable,
            learn_std=self._learn_std,
            std_share_network=self._std_share_network,
            init_std=self._init_std,
            layer_normalization=self._layer_normalization,
            state_include_action=self._state_include_action)
        new_policy.model.parameters = self.model.parameters
        return new_policy

    def __getstate__(self):
        """Object.__getstate__.

        Returns:
            dict: the state to be pickled for the instance.

        """
        new_dict = super().__getstate__()
        del new_dict['_f_step_mean_std']
        del new_dict['_dist']
        del new_dict['_init_hidden']
        return new_dict

    def __setstate__(self, state):
        """Object.__setstate__.

        Args:
            state (dict): Unpickled state.

        """
        super().__setstate__(state)
        self._initialize()
