text: # Copyright 2018 Tensorforce Team. 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 tensorforce.core.optimizers import MetaOptimizer
  
  
  class MetaOptimizerWrapper(MetaOptimizer):
      """
      Meta optimizer wrapper (specification key: `meta_optimizer_wrapper`).
  
      Args:
          name (string): Module name
              (<span style="color:#0000C0"><b>internal use</b></span>).
          optimizer (specification): Optimizer configuration
              (<span style="color:#C00000"><b>required</b></span>).
          multi_step (parameter, int > 0): Number of optimization steps
              (<span style="color:#00C000"><b>default</b></span>: single step).
          subsampling_fraction (parameter, 0.0 < float <= 1.0): Fraction of batch timesteps to
              subsample (<span style="color:#00C000"><b>default</b></span>: no subsampling).
          clipping_threshold (parameter, float > 0.0): Clipping threshold
              (<span style="color:#00C000"><b>default</b></span>: no clipping).
          optimizing_iterations (parameter, int >= 0):  Maximum number of line search iterations
              (<span style="color:#00C000"><b>default</b></span>: no optimizing).
          summary_labels ('all' | iter[string]): Labels of summaries to record
              (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      """
  
      def __init__(
          self, name, optimizer, multi_step=1, subsampling_fraction=1.0, clipping_threshold=None,
          optimizing_iterations=0, summary_labels=None, **kwargs
      ):
          optimizer = dict(type=optimizer)
          optimizer.update(kwargs)
          if optimizing_iterations > 0:
              optimizer = dict(
                  type='optimizing_step', optimizer=optimizer,
                  ls_max_iterations=optimizing_iterations
              )
          if clipping_threshold is not None:
              optimizer = dict(
                  type='clipping_step', optimizer=optimizer, threshold=clipping_threshold
              )
          if subsampling_fraction != 1.0:
              optimizer = dict(
                  type='subsampling_step', optimizer=optimizer, fraction=subsampling_fraction
              )
          if multi_step > 1:
              optimizer = dict(type='multi_step', optimizer=optimizer, num_steps=multi_step)
  
          super().__init__(name=name, optimizer=optimizer, summary_labels=summary_labels)
  
      def tf_step(self, variables, **kwargs):
          return self.optimizer.step(variables=variables, **kwargs)
  