# Copyright 2019 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.
# ==============================================================================
"""Keras model saving code."""

import tensorflow.compat.v2 as tf
from keras.saving import hdf5_format
from keras.saving import saving_utils
from keras.saving.saved_model import load as saved_model_load
from keras.saving.saved_model import load_context
from keras.saving.saved_model import save as saved_model_save
from keras.utils import generic_utils
from keras.utils import traceback_utils
from keras.utils.io_utils import path_to_string
from tensorflow.python.util.tf_export import keras_export

# pylint: disable=g-import-not-at-top
try:
  import h5py
except ImportError:
  h5py = None
# pylint: enable=g-import-not-at-top


@keras_export('keras.models.save_model')
@traceback_utils.filter_traceback
def save_model(model,
               filepath,
               overwrite=True,
               include_optimizer=True,
               save_format=None,
               signatures=None,
               options=None,
               save_traces=True):
  # pylint: disable=line-too-long
  """Saves a model as a TensorFlow SavedModel or HDF5 file.

  See the [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
  for details.

  Usage:

  >>> model = tf.keras.Sequential([
  ...     tf.keras.layers.Dense(5, input_shape=(3,)),
  ...     tf.keras.layers.Softmax()])
  >>> model.save('/tmp/model')
  >>> loaded_model = tf.keras.models.load_model('/tmp/model')
  >>> x = tf.random.uniform((10, 3))
  >>> assert np.allclose(model.predict(x), loaded_model.predict(x))

  The SavedModel and HDF5 file contains:

  - the model's configuration (topology)
  - the model's weights
  - the model's optimizer's state (if any)

  Thus models can be reinstantiated in the exact same state, without any of the
  code used for model definition or training.

  Note that the model weights may have different scoped names after being
  loaded. Scoped names include the model/layer names, such as
  `"dense_1/kernel:0"`. It is recommended that you use the layer properties to
  access specific variables, e.g. `model.get_layer("dense_1").kernel`.

  __SavedModel serialization format__

  Keras SavedModel uses `tf.saved_model.save` to save the model and all
  trackable objects attached to the model (e.g. layers and variables). The model
  config, weights, and optimizer are saved in the SavedModel. Additionally, for
  every Keras layer attached to the model, the SavedModel stores:

    * the config and metadata -- e.g. name, dtype, trainable status
    * traced call and loss functions, which are stored as TensorFlow subgraphs.

  The traced functions allow the SavedModel format to save and load custom
  layers without the original class definition.

  You can choose to not save the traced functions by disabling the `save_traces`
  option. This will decrease the time it takes to save the model and the
  amount of disk space occupied by the output SavedModel. If you enable this
  option, then you _must_ provide all custom class definitions when loading
  the model. See the `custom_objects` argument in `tf.keras.models.load_model`.

  Args:
      model: Keras model instance to be saved.
      filepath: One of the following:
        - String or `pathlib.Path` object, path where to save the model
        - `h5py.File` object where to save the model
      overwrite: Whether we should overwrite any existing model at the target
        location, or instead ask the user with a manual prompt.
      include_optimizer: If True, save optimizer's state together.
      save_format: Either 'tf' or 'h5', indicating whether to save the model
        to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5'
        in TF 1.X.
      signatures: Signatures to save with the SavedModel. Applicable to the 'tf'
        format only. Please see the `signatures` argument in
        `tf.saved_model.save` for details.
      options: (only applies to SavedModel format) `tf.saved_model.SaveOptions`
        object that specifies options for saving to SavedModel.
      save_traces: (only applies to SavedModel format) When enabled, the
        SavedModel will store the function traces for each layer. This
        can be disabled, so that only the configs of each layer are stored.
        Defaults to `True`. Disabling this will decrease serialization time and
        reduce file size, but it requires that all custom layers/models
        implement a `get_config()` method.

  Raises:
      ImportError: If save format is hdf5, and h5py is not available.
  """
  # pylint: enable=line-too-long
  from keras.engine import sequential  # pylint: disable=g-import-not-at-top

  default_format = 'tf' if tf.__internal__.tf2.enabled() else 'h5'
  save_format = save_format or default_format

  filepath = path_to_string(filepath)

  # If the user has not already called fit or built the underlying metrics, we
  # should do that before saving to ensure the metric names have all
  # appropriate name transformations applied.
  saving_utils.try_build_compiled_arguments(model)

  if (save_format == 'h5' or
      (h5py is not None and isinstance(filepath, h5py.File)) or
      saving_utils.is_hdf5_filepath(filepath)):
    # TODO(b/130258301): add utility method for detecting model type.
    if (not model._is_graph_network and  # pylint:disable=protected-access
        not isinstance(model, sequential.Sequential)):
      raise NotImplementedError(
          'Saving the model to HDF5 format requires the model to be a '
          'Functional model or a Sequential model. It does not work for '
          'subclassed models, because such models are defined via the body of '
          'a Python method, which isn\'t safely serializable. Consider saving '
          'to the Tensorflow SavedModel format (by setting save_format="tf") '
          'or using `save_weights`.')
    hdf5_format.save_model_to_hdf5(
        model, filepath, overwrite, include_optimizer)
  else:
    with generic_utils.SharedObjectSavingScope():
      saved_model_save.save(model, filepath, overwrite, include_optimizer,
                            signatures, options, save_traces)


@keras_export('keras.models.load_model')
@traceback_utils.filter_traceback
def load_model(filepath, custom_objects=None, compile=True, options=None):  # pylint: disable=redefined-builtin
  """Loads a model saved via `model.save()`.

  Usage:

  >>> model = tf.keras.Sequential([
  ...     tf.keras.layers.Dense(5, input_shape=(3,)),
  ...     tf.keras.layers.Softmax()])
  >>> model.save('/tmp/model')
  >>> loaded_model = tf.keras.models.load_model('/tmp/model')
  >>> x = tf.random.uniform((10, 3))
  >>> assert np.allclose(model.predict(x), loaded_model.predict(x))

  Note that the model weights may have different scoped names after being
  loaded. Scoped names include the model/layer names, such as
  `"dense_1/kernel:0"`. It is recommended that you use the layer properties to
  access specific variables, e.g. `model.get_layer("dense_1").kernel`.

  Args:
      filepath: One of the following:
          - String or `pathlib.Path` object, path to the saved model
          - `h5py.File` object from which to load the model
      custom_objects: Optional dictionary mapping names
          (strings) to custom classes or functions to be
          considered during deserialization.
      compile: Boolean, whether to compile the model
          after loading.
      options: Optional `tf.saved_model.LoadOptions` object that specifies
        options for loading from SavedModel.

  Returns:
      A Keras model instance. If the original model was compiled, and saved with
      the optimizer, then the returned model will be compiled. Otherwise, the
      model will be left uncompiled. In the case that an uncompiled model is
      returned, a warning is displayed if the `compile` argument is set to
      `True`.

  Raises:
      ImportError: if loading from an hdf5 file and h5py is not available.
      IOError: In case of an invalid savefile.
  """
  with generic_utils.SharedObjectLoadingScope():
    with generic_utils.CustomObjectScope(custom_objects or {}):
      with load_context.load_context(options):
        if (h5py is not None and
            (isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
          return hdf5_format.load_model_from_hdf5(filepath, custom_objects,
                                                  compile)

        filepath = path_to_string(filepath)
        if isinstance(filepath, str):
          return saved_model_load.load(filepath, compile, options)

  raise IOError(
      'Unable to load model. Filepath is not an hdf5 file (or h5py is not '
      f'available) or SavedModel. Received: filepath={filepath}')

# Inject the load_model function to keras_deps to remove the dependency
# from TFLite to Keras.
tf.__internal__.register_load_model_function(load_model)
