# 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
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# ==============================================================================
# pylint: disable=protected-access
"""Functions that save the model's config into different formats."""

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.models.model_from_config')
def model_from_config(config, custom_objects=None):
  """Instantiates a Keras model from its config.

  Usage:
  ```
  # for a Functional API model
  tf.keras.Model().from_config(model.get_config())

  # for a Sequential model
  tf.keras.Sequential().from_config(model.get_config())
  ```

  Args:
      config: Configuration dictionary.
      custom_objects: Optional dictionary mapping names
          (strings) to custom classes or functions to be
          considered during deserialization.

  Returns:
      A Keras model instance (uncompiled).

  Raises:
      TypeError: if `config` is not a dictionary.
  """
  if isinstance(config, list):
    raise TypeError('`model_from_config` expects a dictionary, not a list. '
                    f'Received: config={config}. Did you meant to use '
                    '`Sequential.from_config(config)`?')
  from keras.layers import deserialize  # pylint: disable=g-import-not-at-top
  return deserialize(config, custom_objects=custom_objects)


@keras_export('keras.models.model_from_yaml')
def model_from_yaml(yaml_string, custom_objects=None):
  """Parses a yaml model configuration file and returns a model instance.

  Note: Since TF 2.6, this method is no longer supported and will raise a
  RuntimeError.

  Args:
      yaml_string: YAML string or open file encoding a model configuration.
      custom_objects: Optional dictionary mapping names
          (strings) to custom classes or functions to be
          considered during deserialization.

  Returns:
      A Keras model instance (uncompiled).

  Raises:
      RuntimeError: announces that the method poses a security risk
  """
  raise RuntimeError(
      'Method `model_from_yaml()` has been removed due to security risk of '
      'arbitrary code execution. Please use `Model.to_json()` and '
      '`model_from_json()` instead.'
  )


@keras_export('keras.models.model_from_json')
def model_from_json(json_string, custom_objects=None):
  """Parses a JSON model configuration string and returns a model instance.

  Usage:

  >>> model = tf.keras.Sequential([
  ...     tf.keras.layers.Dense(5, input_shape=(3,)),
  ...     tf.keras.layers.Softmax()])
  >>> config = model.to_json()
  >>> loaded_model = tf.keras.models.model_from_json(config)

  Args:
      json_string: JSON string encoding a model configuration.
      custom_objects: Optional dictionary mapping names
          (strings) to custom classes or functions to be
          considered during deserialization.

  Returns:
      A Keras model instance (uncompiled).
  """
  from keras.layers import deserialize_from_json  # pylint: disable=g-import-not-at-top
  return deserialize_from_json(json_string, custom_objects=custom_objects)
