text: # Copyright 2018 The TensorFlow 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.
  # ==============================================================================
  # pylint: disable=protected-access
  """Utilities related to layer/model functionality.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import six
  
  from tensorflow.python.keras import backend as K
  from tensorflow.python.keras.utils.conv_utils import convert_kernel
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import nest
  from tensorflow.python.util import object_identity
  from tensorflow.python.util.tf_export import keras_export
  
  
  @keras_export('keras.utils.get_source_inputs')
  def get_source_inputs(tensor, layer=None, node_index=None):
    """Returns the list of input tensors necessary to compute `tensor`.
  
    Output will always be a list of tensors
    (potentially with 1 element).
  
    Arguments:
        tensor: The tensor to start from.
        layer: Origin layer of the tensor. Will be
            determined via tensor._keras_history if not provided.
        node_index: Origin node index of the tensor.
  
    Returns:
        List of input tensors.
    """
    if not hasattr(tensor, '_keras_history'):
      return tensor
  
    if layer is None or node_index:
      layer, node_index, _ = tensor._keras_history
    if not layer._inbound_nodes:
      return [tensor]
    else:
      node = layer._inbound_nodes[node_index]
      if node.is_input:
        # Reached an Input layer, stop recursion.
        return nest.flatten(node.input_tensors)
      else:
        source_tensors = []
        for layer, node_index, _, tensor in node.iterate_inbound():
          previous_sources = get_source_inputs(tensor, layer, node_index)
          # Avoid input redundancy.
          for x in previous_sources:
            if all(x is not t for t in source_tensors):
              source_tensors.append(x)
        return source_tensors
  
  
  def validate_string_arg(input_data,
                          allowable_strings,
                          layer_name,
                          arg_name,
                          allow_none=False,
                          allow_callables=False):
    """Validates the correctness of a string-based arg."""
    if allow_none and input_data is None:
      return
    elif allow_callables and callable(input_data):
      return
    elif isinstance(input_data,
                    six.string_types) and input_data in allowable_strings:
      return
    else:
      allowed_args = '`None`, ' if allow_none else ''
      allowed_args += 'a `Callable`, ' if allow_callables else ''
      allowed_args += 'or one of the following values: %s' % (allowable_strings,)
      raise ValueError(("%s's %s arg received an invalid value %s. " +
                        'Allowed values are %s.') %
                       (layer_name, arg_name, input_data, allowed_args))
  
  
  def count_params(weights):
    """Count the total number of scalars composing the weights.
  
    Arguments:
        weights: An iterable containing the weights on which to compute params
  
    Returns:
        The total number of scalars composing the weights
    """
    unique_weights = object_identity.ObjectIdentitySet(weights)
    weight_shapes = [w.shape.as_list() for w in unique_weights]
    standardized_weight_shapes = [
        [0 if w_i is None else w_i for w_i in w] for w in weight_shapes
    ]
    return int(sum(np.prod(p) for p in standardized_weight_shapes))
  
  
  def print_summary(model, line_length=None, positions=None, print_fn=None):
    """Prints a summary of a model.
  
    Arguments:
        model: Keras model instance.
        line_length: Total length of printed lines
            (e.g. set this to adapt the display to different
            terminal window sizes).
        positions: Relative or absolute positions of log elements in each line.
            If not provided, defaults to `[.33, .55, .67, 1.]`.
        print_fn: Print function to use.
            It will be called on each line of the summary.
            You can set it to a custom function
            in order to capture the string summary.
            It defaults to `print` (prints to stdout).
    """
    if print_fn is None:
      print_fn = print
  
    if model.__class__.__name__ == 'Sequential':
      sequential_like = True
    elif not model._is_graph_network:
      # We treat subclassed models as a simple sequence of layers, for logging
      # purposes.
      sequential_like = True
    else:
      sequential_like = True
      nodes_by_depth = model._nodes_by_depth.values()
      nodes = []
      for v in nodes_by_depth:
        if (len(v) > 1) or (len(v) == 1 and
                            len(nest.flatten(v[0].keras_inputs)) > 1):
          # if the model has multiple nodes
          # or if the nodes have multiple inbound_layers
          # the model is no longer sequential
          sequential_like = False
          break
        nodes += v
      if sequential_like:
        # search for shared layers
        for layer in model.layers:
          flag = False
          for node in layer._inbound_nodes:
            if node in nodes:
              if flag:
                sequential_like = False
                break
              else:
                flag = True
          if not sequential_like:
            break
  
    if sequential_like:
      line_length = line_length or 65
      positions = positions or [.45, .85, 1.]
      if positions[-1] <= 1:
        positions = [int(line_length * p) for p in positions]
      # header names for the different log elements
      to_display = ['Layer (type)', 'Output Shape', 'Param #']
    else:
      line_length = line_length or 98
      positions = positions or [.33, .55, .67, 1.]
      if positions[-1] <= 1:
        positions = [int(line_length * p) for p in positions]
      # header names for the different log elements
      to_display = ['Layer (type)', 'Output Shape', 'Param #', 'Connected to']
      relevant_nodes = []
      for v in model._nodes_by_depth.values():
        relevant_nodes += v
  
    def print_row(fields, positions):
      line = ''
      for i in range(len(fields)):
        if i > 0:
          line = line[:-1] + ' '
        line += str(fields[i])
        line = line[:positions[i]]
        line += ' ' * (positions[i] - len(line))
      print_fn(line)
  
    print_fn('Model: "{}"'.format(model.name))
    print_fn('_' * line_length)
    print_row(to_display, positions)
    print_fn('=' * line_length)
  
    def print_layer_summary(layer):
      """Prints a summary for a single layer.
  
      Arguments:
          layer: target layer.
      """
      try:
        output_shape = layer.output_shape
      except AttributeError:
        output_shape = 'multiple'
      except RuntimeError:  # output_shape unknown in Eager mode.
        output_shape = '?'
      name = layer.name
      cls_name = layer.__class__.__name__
      fields = [name + ' (' + cls_name + ')', output_shape, layer.count_params()]
      print_row(fields, positions)
  
    def print_layer_summary_with_connections(layer):
      """Prints a summary for a single layer (including topological connections).
  
      Arguments:
          layer: target layer.
      """
      try:
        output_shape = layer.output_shape
      except AttributeError:
        output_shape = 'multiple'
      connections = []
      for node in layer._inbound_nodes:
        if relevant_nodes and node not in relevant_nodes:
          # node is not part of the current network
          continue
  
        for inbound_layer, node_index, tensor_index, _ in node.iterate_inbound():
          connections.append('{}[{}][{}]'.format(inbound_layer.name, node_index,
                                                 tensor_index))
  
      name = layer.name
      cls_name = layer.__class__.__name__
      if not connections:
        first_connection = ''
      else:
        first_connection = connections[0]
      fields = [
          name + ' (' + cls_name + ')', output_shape,
          layer.count_params(), first_connection
      ]
      print_row(fields, positions)
      if len(connections) > 1:
        for i in range(1, len(connections)):
          fields = ['', '', '', connections[i]]
          print_row(fields, positions)
  
    layers = model.layers
    for i in range(len(layers)):
      if sequential_like:
        print_layer_summary(layers[i])
      else:
        print_layer_summary_with_connections(layers[i])
      if i == len(layers) - 1:
        print_fn('=' * line_length)
      else:
        print_fn('_' * line_length)
  
    if hasattr(model, '_collected_trainable_weights'):
      trainable_count = count_params(model._collected_trainable_weights)
    else:
      trainable_count = count_params(model.trainable_weights)
  
    non_trainable_count = count_params(model.non_trainable_weights)
  
    print_fn('Total params: {:,}'.format(trainable_count + non_trainable_count))
    print_fn('Trainable params: {:,}'.format(trainable_count))
    print_fn('Non-trainable params: {:,}'.format(non_trainable_count))
    print_fn('_' * line_length)
  
  
  def gather_trainable_weights(trainable, sub_layers, extra_variables):
    """Lists the trainable weights for an object with sub-layers.
  
    Args:
      trainable: Whether the object collecting the variables is trainable.
      sub_layers: A flat list of Layer objects owned by this object, to collect
        variables from.
      extra_variables: Any extra variables to include. Their `.trainable` property
        is used to categorize them.
  
    Returns:
      A list of collected trainable weights/variables.
    """
    if not trainable:
      return []
    weights = []
    for layer in sub_layers:
      weights += layer.trainable_weights
    trainable_extra_variables = [
        v for v in extra_variables if v.trainable]
    return weights + trainable_extra_variables
  
  
  def gather_non_trainable_weights(trainable, sub_layers, extra_variables):
    """Lists the non-trainable weights for an object with sub-layers.
  
    Args:
      trainable: Whether the object collecting the variables is trainable.
      sub_layers: A flat list of Layer objects owned by this object, to collect
        variables from.
      extra_variables: Any extra variables to include. Their `.trainable` property
        is used to categorize them.
  
    Returns:
      A list of collected non-trainable weights/variables.
    """
    trainable_extra_variables = []
    non_trainable_extra_variables = []
    for v in extra_variables:
      if v.trainable:
        trainable_extra_variables.append(v)
      else:
        non_trainable_extra_variables.append(v)
    weights = []
    for layer in sub_layers:
      weights += layer.non_trainable_weights
    if not trainable:
      trainable_weights = []
      for layer in sub_layers:
        trainable_weights += layer.trainable_weights
      return (trainable_weights + trainable_extra_variables
              + weights + non_trainable_extra_variables)
    return weights + non_trainable_extra_variables
  
  
  @deprecation.deprecated('2020-06-23',
                          'The Theano kernel format is legacy; '
                          'this utility will be removed.')
  @keras_export('keras.utils.convert_all_kernels_in_model')
  def convert_all_kernels_in_model(model):
    """Converts all convolution kernels in a model from Theano to TensorFlow.
  
    Also works from TensorFlow to Theano.
  
    This is used for converting legacy Theano-saved model files.
  
    Arguments:
        model: target model for the conversion.
    """
    # Note: SeparableConvolution not included
    # since only supported by TF.
    conv_classes = {
        'Conv1D',
        'Conv2D',
        'Conv3D',
        'Conv2DTranspose',
    }
    to_assign = []
    for layer in model.layers:
      if layer.__class__.__name__ in conv_classes:
        original_kernel = K.get_value(layer.kernel)
        converted_kernel = convert_kernel(original_kernel)
        to_assign.append((layer.kernel, converted_kernel))
    K.batch_set_value(to_assign)
  
  
  def convert_dense_weights_data_format(dense,
                                        previous_feature_map_shape,
                                        target_data_format='channels_first'):
    """Utility useful when changing a convnet's `data_format`.
  
    When porting the weights of a convnet from one data format to the other,
    if the convnet includes a `Flatten` layer
    (applied to the last convolutional feature map)
    followed by a `Dense` layer, the weights of that `Dense` layer
    should be updated to reflect the new dimension ordering.
  
    Arguments:
        dense: The target `Dense` layer.
        previous_feature_map_shape: A shape tuple of 3 integers,
            e.g. `(512, 7, 7)`. The shape of the convolutional
            feature map right before the `Flatten` layer that
            came before the target `Dense` layer.
        target_data_format: One of "channels_last", "channels_first".
            Set it "channels_last"
            if converting a "channels_first" model to "channels_last",
            or reciprocally.
    """
    assert target_data_format in {'channels_last', 'channels_first'}
    kernel, bias = dense.get_weights()
    for i in range(kernel.shape[1]):
      if target_data_format == 'channels_first':
        c, h, w = previous_feature_map_shape
        original_fm_shape = (h, w, c)
        ki = kernel[:, i].reshape(original_fm_shape)
        ki = np.transpose(ki, (2, 0, 1))  # last -> first
      else:
        h, w, c = previous_feature_map_shape
        original_fm_shape = (c, h, w)
        ki = kernel[:, i].reshape(original_fm_shape)
        ki = np.transpose(ki, (1, 2, 0))  # first -> last
      kernel[:, i] = np.reshape(ki, (np.prod(previous_feature_map_shape),))
    dense.set_weights([kernel, bias])
  
  
  def is_builtin_layer(layer):
    if not getattr(layer, '_keras_api_names', None):
      return False
  
    # Subclasses of `Layer` that are not exported inherit the export name
    # of the base layer class.
    return (layer._keras_api_names != ('keras.layers.Layer',) and
            layer._keras_api_names_v1 != ('keras.layers.Layer',))
  