from __future__ import absolute_import
import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import lasagne.init
import theano.tensor as TT
from rllab2.q_functions.base import QFunction
from rllab2.core.lasagne_powered import LasagnePowered
from rllab2.core.lasagne_layers import batch_norm
from rllab2.core.serializable import Serializable
from rllab2.misc import ext


class ContinuousMLPQFunction(QFunction, LasagnePowered):
    def __init__(
            self,
            env_spec,
            hidden_sizes=(32, 32),
            hidden_nonlinearity=NL.rectify,
            hidden_W_init=lasagne.init.HeUniform(),
            hidden_b_init=lasagne.init.Constant(0.),
            action_merge_layer=-2,
            output_nonlinearity=None,
            output_W_init=lasagne.init.Uniform(-3e-3, 3e-3),
            output_b_init=lasagne.init.Uniform(-3e-3, 3e-3),
            bn=False):
        Serializable.quick_init(self, locals())

        l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim), name=u"obs")
        l_action = L.InputLayer(shape=(None, env_spec.action_space.flat_dim), name=u"actions")

        n_layers = len(hidden_sizes) + 1

        if n_layers > 1:
            action_merge_layer = \
                (action_merge_layer % n_layers + n_layers) % n_layers
        else:
            action_merge_layer = 1

        l_hidden = l_obs

        for idx, size in enumerate(hidden_sizes):
            if bn:
                l_hidden = batch_norm(l_hidden)

            if idx == action_merge_layer:
                l_hidden = L.ConcatLayer([l_hidden, l_action])

            l_hidden = L.DenseLayer(
                l_hidden,
                num_units=size,
                W=hidden_W_init,
                b=hidden_b_init,
                nonlinearity=hidden_nonlinearity,
                name=u"h%d" % (idx + 1)
            )

        if action_merge_layer == n_layers:
            l_hidden = L.ConcatLayer([l_hidden, l_action])

        l_output = L.DenseLayer(
            l_hidden,
            num_units=1,
            W=output_W_init,
            b=output_b_init,
            nonlinearity=output_nonlinearity,
            name=u"output"
        )

        output_var = L.get_output(l_output, deterministic=True).flatten()

        self._f_qval = ext.compile_function([l_obs.input_var, l_action.input_var], output_var)
        self._output_layer = l_output
        self._obs_layer = l_obs
        self._action_layer = l_action
        self._output_nonlinearity = output_nonlinearity

        LasagnePowered.__init__(self, [l_output])

    def get_qval(self, observations, actions):
        return self._f_qval(observations, actions)

    def get_qval_sym(self, obs_var, action_var, **kwargs):
        qvals = L.get_output(
            self._output_layer,
            {self._obs_layer: obs_var, self._action_layer: action_var},
            **kwargs
        )
        return TT.reshape(qvals, (-1,))
