text: # Copyright 2018, The TensorFlow Federated Authors.
  #
  # 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.
  """Utility methods for working with TensorFlow Federated Model objects."""
  
  import collections
  
  import attr
  import tensorflow as tf
  
  from tensorflow_federated.python.common_libs import anonymous_tuple
  from tensorflow_federated.python.common_libs import py_typecheck
  from tensorflow_federated.python.learning import model as model_lib
  
  
  def model_initializer(model, name=None):
    """Creates an initializer op for all of the model's variables."""
    py_typecheck.check_type(model, model_lib.Model)
    return tf.compat.v1.initializers.variables(
        model.trainable_variables + model.non_trainable_variables +
        model.local_variables,
        name=(name or 'model_initializer'))
  
  
  @attr.s(eq=False, frozen=True, slots=True)
  class ModelWeights(object):
    """A container for the trainable and non-trainable variables of a `Model`.
  
    Note this does not include the model's local variables.
  
    It may also be used to hold other values that are parallel to these variables,
    e.g., tensors corresponding to variable values, or updates to model variables.
    """
    trainable = attr.ib()
    non_trainable = attr.ib()
  
    @classmethod
    def from_model(cls, model):
      py_typecheck.check_type(model, (model_lib.Model, tf.keras.Model))
      return cls(model.trainable_variables, model.non_trainable_variables)
  
    @classmethod
    def from_tff_result(cls, anon_tuple):
      py_typecheck.check_type(anon_tuple, anonymous_tuple.AnonymousTuple)
      return cls([
          value
          for _, value in anonymous_tuple.iter_elements(anon_tuple.trainable)
      ], [
          value
          for _, value in anonymous_tuple.iter_elements(anon_tuple.non_trainable)
      ])
  
    def assign_weights_to(self, model):
      """Assign these TFF model weights to the weights of a model.
  
      Args:
        model: a `tf.keras.Model` or `tff.learning.Model` instance to assign the
          weights to.
      """
      py_typecheck.check_type(model, (model_lib.Model, tf.keras.Model))
      if isinstance(model, tf.keras.Model):
        tf.nest.map_structure(lambda var, t: var.assign(t),
                              model.trainable_weights, self.trainable)
        tf.nest.map_structure(lambda var, t: var.assign(t),
                              model.non_trainable_weights, self.non_trainable)
      else:
        tf.nest.map_structure(lambda var, t: var.assign(t),
                              model.trainable_variables, self.trainable)
        tf.nest.map_structure(lambda var, t: var.assign(t),
                              model.non_trainable_variables, self.non_trainable)
  
  
  def enhance(model):
    """Wraps a `tff.learning.Model` as an `EnhancedModel`.
  
    Args:
      model: A `tff.learning.Model`.
  
    Returns:
      An `EnhancedModel`. If `model` has already been wrapped as such, this is a
      no-op.
    """
    py_typecheck.check_type(model, model_lib.Model)
    if isinstance(model, EnhancedModel):
      return model
    elif isinstance(model, model_lib.Model):
      return EnhancedModel(model)
    raise TypeError('Do not know how to wrap object of type [{t}]. Expected a '
                    'tff.learning.Model'.format(t=type(model_lib.Model)))
  
  
  def _check_iterable_of_variables(variables):
    py_typecheck.check_type(variables, collections.Iterable)
    for v in variables:
      py_typecheck.check_type(v, tf.Variable)
    return variables
  
  
  class EnhancedModel(model_lib.Model):
    """A wrapper around a Model that adds sanity checking and metadata helpers."""
  
    def __init__(self, model):
      super().__init__()
      py_typecheck.check_type(model, model_lib.Model)
      if isinstance(model, EnhancedModel):
        raise ValueError(
            'Attempting to wrap an EnhancedModel in another EnhancedModel')
      self._model = model
  
    #
    # Methods offering additional functionality and metadata:
    #
  
    @property
    def weights(self):
      """Returns a `tff.learning.ModelWeights`."""
      return ModelWeights.from_model(self)
  
    #
    # The following delegate to the Model interface:
    #
  
    @property
    def trainable_variables(self):
      return _check_iterable_of_variables(self._model.trainable_variables)
  
    @property
    def non_trainable_variables(self):
      return _check_iterable_of_variables(self._model.non_trainable_variables)
  
    @property
    def local_variables(self):
      return _check_iterable_of_variables(self._model.local_variables)
  
    @property
    def input_spec(self):
      return self._model.input_spec
  
    def forward_pass(self, batch_input, training=True):
      return py_typecheck.check_type(
          self._model.forward_pass(batch_input, training), model_lib.BatchOutput)
  
    def report_local_outputs(self):
      return self._model.report_local_outputs()
  
    @property
    def federated_output_computation(self):
      return self._model.federated_output_computation
  