# Copyright 2018/2019 The RLgraph authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from rlgraph import get_backend
from rlgraph.components.distributions.distribution import Distribution
from rlgraph.spaces import Tuple, FloatBox
from rlgraph.spaces.space_utils import sanity_check_space
from rlgraph.utils.decorators import rlgraph_api, graph_fn

if get_backend() == "tf":
    import tensorflow_probability as tfp
elif get_backend() == "pytorch":
    pass


class MultivariateNormal(Distribution):
    """
    A multivariate Gaussian distribution.
    """
    def __init__(self, parameterize_via_diagonal=True,
                 parameterize_via_covariance=False, scope="multivariate-normal", **kwargs):
        """
        Args:
            parameterize_via_diagonal (bool): Whether we are parameterizing via the diagonal stddev values.
                Note that
            parameterize_via_covariance (bool): Whether we are parameterizing via the full covariance values.
        """
        super(MultivariateNormal, self).__init__(scope=scope, **kwargs)
        self.parameterize_via_diagonal = parameterize_via_diagonal
        self.parameterize_via_covariance = parameterize_via_covariance
        assert self.parameterize_via_diagonal != self.parameterize_via_covariance, \
            "ERROR: Exactly one of `parameterize_via_diagonal` and `parameterize_via_covariance` must be True!"

    def check_input_spaces(self, input_spaces, action_space=None):
        # Must be a Tuple of len 2 (mean and stddev OR mean and full co-variance matrix).
        in_space = input_spaces["parameters"]
        sanity_check_space(in_space, allowed_types=[Tuple])
        assert len(in_space) == 2, "ERROR: Expected Tuple of len=2 as input Space to MultivariateNormal!"
        sanity_check_space(in_space[0], allowed_types=[FloatBox])
        sanity_check_space(in_space[1], allowed_types=[FloatBox])

        if self.parameterize_via_diagonal:
            # Make sure mean and stddev have the same last rank.
            assert in_space[0].shape[-1] == in_space[1].shape[-1],\
                "ERROR: `parameters` in_space must have the same last rank for mean as for (diagonal) stddev values!"

    @rlgraph_api
    def _graph_fn_get_distribution(self, parameters):
        if get_backend() == "tf":
            if self.parameterize_via_diagonal:
                return tfp.distributions.MultivariateNormalDiag(
                    loc=parameters[0], scale_diag=parameters[1]
                )
            # TODO: support parameterization through full covariance matrix.
            #else:
            #    mean, covariance_matrix = tf.split(parameters, num_or_size_splits=[1, self.num_events], axis=-1)
            #    mean = tf.squeeze(mean, axis=-1)
            #    return tfp.distributions.MultivariateNormalFullCovariance(
            #        loc=mean, covariance_matrix=covariance_matrix
            #    )

    @graph_fn
    def _graph_fn_sample_deterministic(self, distribution):
            return distribution.mean()
