# Copyright 2017 Artem Artemev @awav
#
# 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.

import sys
import tensorflow as tf

from . import optimizer
from .. import misc
from ..models.model import Model


_REGISTERED_TENSORFLOW_OPTIMIZERS = {}


class _TensorFlowOptimizer(optimizer.Optimizer):
    def __init__(self, *args, **kwargs):
        name = self.__class__.__name__
        tf_optimizer = _get_registered_optimizer(name)
        self._model = None
        super().__init__()
        self._optimizer = tf_optimizer(*args, **kwargs)
        self._minimize_operation = None

    def minimize(self, model, session=None, var_list=None, feed_dict=None,
                 maxiter=1000, initialize=True, anchor=True, **kwargs):
        """
        Minimizes objective function of the model.

        :param model: GPflow model with objective tensor.
        :param session: Session where optimization will be run.
        :param var_list: List of extra variables which should be trained during optimization.
        :param feed_dict: Feed dictionary of tensors passed to session run method.
        :param maxiter: Number of run interation.
        :param initialize: If `True` model parameters will be re-initialized even if they were
            initialized before for gotten session.
        :param anchor: If `True` trained variable values computed during optimization at
            particular session will be synchronized with internal parameter values.
        :param kwargs: This is a dictionary of extra parameters for session run method.
        """

        if model is None or not isinstance(model, Model):
            raise ValueError('Unknown type passed for optimization.')

        session = model.enquire_session(session)

        self._model = model
        objective = model.objective

        with session.graph.as_default():
            full_var_list = self._gen_var_list(model, var_list)

            # Create optimizer variables before initialization.
            self._minimize_operation = self.optimizer.minimize(
                objective, var_list=full_var_list, **kwargs)

            model.initialize(session=session, force=initialize)
            self._initialize_optimizer(session, full_var_list)

            feed_dict = self._gen_feed_dict(model, feed_dict)
            for _i in range(maxiter):
                session.run(self.minimize_operation, feed_dict=feed_dict)

        if anchor:
            model.anchor(session)

    def _initialize_optimizer(self, session, var_list):
        """
        TODO(@awav): AdamOptimizer creates beta1 and beta2 variables which are
        not included in slots.
        """
        def get_optimizer_slots():
            for name in self.optimizer.get_slot_names():
                for var in var_list:
                    slot = self.optimizer.get_slot(var, name)
                    if slot is not None:
                        yield slot
        extra_vars = [v for v in self.optimizer.__dict__.values() if isinstance(v, tf.Variable)]
        optimizer_vars = list(get_optimizer_slots())
        full_var_list = list(set(optimizer_vars + extra_vars))
        misc.initialize_variables(full_var_list, session=session, force=False)

    @property
    def minimize_operation(self):
        return self._minimize_operation

    @property
    def model(self):
        return self._model

    @property
    def optimizer(self):
        return self._optimizer

    @model.setter
    def model(self, value):
        self._model = value
        self._optimizer = None
        self._minimize_operation = None


def _get_registered_optimizer(name):
    tf_optimizer = _REGISTERED_TENSORFLOW_OPTIMIZERS.get(name, None)
    if tf_optimizer is None:
        raise TypeError('Optimizer not found.')
    return tf_optimizer


def _register_optimizer(name, optimizer_type):
    if optimizer_type.__base__ is not tf.train.Optimizer:
        raise ValueError('Wrong TensorFlow optimizer type passed: "{0}".'
                         .format(optimizer_type))
    gp_optimizer = type(name, (_TensorFlowOptimizer, ), {})
    _REGISTERED_TENSORFLOW_OPTIMIZERS[name] = optimizer_type
    module = sys.modules[__name__]
    setattr(module, name, gp_optimizer)


# Create GPflow optimizer classes with same names as TensorFlow optimizers
for key, train_type in tf.train.__dict__.items():
    suffix = 'Optimizer'
    if key != suffix and key.endswith(suffix):
        _register_optimizer(key, train_type)


__all__ = list(_REGISTERED_TENSORFLOW_OPTIMIZERS.keys())