text: # 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=False, 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())
  