# Copyright 2015 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.
# ==============================================================================
"""Loader implementation for SavedModel with hermetic, language-neutral exports.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from google.protobuf import message
from google.protobuf import text_format

from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.framework import ops
from tensorflow.python.lib.io import file_io
from tensorflow.python.platform import tf_logging
from tensorflow.python.saved_model import constants
from tensorflow.python.training import saver as tf_saver
from tensorflow.python.util import compat


def _parse_saved_model(export_dir):
  """Reads the savedmodel.pb or savedmodel.pbtxt file containing `SavedModel`.

  Args:
    export_dir: Directory containing the SavedModel file.

  Returns:
    A `SavedModel` protocol buffer.

  Raises:
    IOError: If the file does not exist, or cannot be successfully parsed.
  """
  # Build the path to the SavedModel in pbtxt format.
  path_to_pbtxt = os.path.join(
      compat.as_bytes(export_dir),
      compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
  # Build the path to the SavedModel in pb format.
  path_to_pb = os.path.join(
      compat.as_bytes(export_dir),
      compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))

  # Parse the SavedModel protocol buffer.
  saved_model = saved_model_pb2.SavedModel()
  if file_io.file_exists(path_to_pb):
    try:
      file_content = file_io.FileIO(path_to_pb, "rb").read()
      saved_model.ParseFromString(file_content)
      return saved_model
    except message.DecodeError as e:
      raise IOError("Cannot parse file %s: %s." % (path_to_pb, str(e)))
  elif file_io.file_exists(path_to_pbtxt):
    try:
      file_content = file_io.FileIO(path_to_pbtxt, "rb").read()
      text_format.Merge(file_content.decode("utf-8"), saved_model)
      return saved_model
    except text_format.ParseError as e:
      raise IOError("Cannot parse file %s: %s." % (path_to_pbtxt, str(e)))
  else:
    raise IOError("SavedModel file does not exist at: %s/{%s|%s}" %
                  (export_dir,
                   constants.SAVED_MODEL_FILENAME_PBTXT,
                   constants.SAVED_MODEL_FILENAME_PB))


def _get_asset_tensors(export_dir, meta_graph_def_to_load):
  """Gets the asset tensors, if defined in the meta graph def to load.

  Args:
    export_dir: Directory where the SavedModel is located.
    meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded.

  Returns:
    A dictionary of asset tensors, keyed by the name of the asset tensor. The
    value in the map corresponds to the absolute path of the asset file.
  """
  # Collection-def that may contain the assets key.
  collection_def = meta_graph_def_to_load.collection_def

  asset_tensor_dict = {}
  if constants.ASSETS_KEY in collection_def:
    # Location of the assets for SavedModel.
    assets_directory = os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes(constants.ASSETS_DIRECTORY))
    assets_any_proto = collection_def[constants.ASSETS_KEY].any_list.value
    # Process each asset and add it to the asset tensor dictionary.
    for asset_any_proto in assets_any_proto:
      asset_proto = meta_graph_pb2.AssetFileDef()
      asset_any_proto.Unpack(asset_proto)
      asset_tensor_dict[asset_proto.tensor_info.name] = os.path.join(
          compat.as_bytes(assets_directory),
          compat.as_bytes(asset_proto.filename))
  return asset_tensor_dict


def _get_main_op_tensor(meta_graph_def_to_load):
  """Gets the main op tensor, if one exists.

  Args:
    meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded.

  Returns:
    The main op tensor, if it exists and `None` otherwise.

  Raises:
    RuntimeError: If the collection def corresponding to the main op key has
        other than exactly one tensor.
  """
  collection_def = meta_graph_def_to_load.collection_def
  main_op_tensor = None
  if constants.MAIN_OP_KEY in collection_def:
    main_ops = collection_def[constants.MAIN_OP_KEY].node_list.value
    if len(main_ops) != 1:
      raise RuntimeError("Expected exactly one SavedModel main op.")
    main_op_tensor = ops.get_collection(constants.MAIN_OP_KEY)[0]
  return main_op_tensor


def _get_legacy_init_op_tensor(meta_graph_def_to_load):
  """Gets the legacy init op tensor, if one exists.

  Args:
    meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded.

  Returns:
    The legacy init op tensor, if it exists and `None` otherwise.

  Raises:
    RuntimeError: If the collection def corresponding to the legacy init op key
        has other than exactly one tensor.
  """
  collection_def = meta_graph_def_to_load.collection_def
  legacy_init_op_tensor = None
  if constants.LEGACY_INIT_OP_KEY in collection_def:
    legacy_init_ops = collection_def[
        constants.LEGACY_INIT_OP_KEY].node_list.value
    if len(legacy_init_ops) != 1:
      raise RuntimeError("Expected exactly one legacy serving init op.")
    legacy_init_op_tensor = ops.get_collection(constants.LEGACY_INIT_OP_KEY)[0]
  return legacy_init_op_tensor


def maybe_saved_model_directory(export_dir):
  """Checks whether the provided export directory could contain a SavedModel.

  Note that the method does not load any data by itself. If the method returns
  `false`, the export directory definitely does not contain a SavedModel. If the
  method returns `true`, the export directory may contain a SavedModel but
  provides no guarantee that it can be loaded.

  Args:
    export_dir: Absolute string path to possible export location. For example,
                '/my/foo/model'.

  Returns:
    True if the export directory contains SavedModel files, False otherwise.
  """
  txt_path = os.path.join(export_dir, constants.SAVED_MODEL_FILENAME_PBTXT)
  pb_path = os.path.join(export_dir, constants.SAVED_MODEL_FILENAME_PB)
  return file_io.file_exists(txt_path) or file_io.file_exists(pb_path)


def load(sess, tags, export_dir, **saver_kwargs):
  """Loads the model from a SavedModel as specified by tags.

  Args:
    sess: The TensorFlow session to restore the variables.
    tags: Set of string tags to identify the required MetaGraphDef. These should
        correspond to the tags used when saving the variables using the
        SavedModel `save()` API.
    export_dir: Directory in which the SavedModel protocol buffer and variables
        to be loaded are located.
    **saver_kwargs: Optional keyword arguments passed through to Saver.

  Returns:
    The `MetaGraphDef` protocol buffer loaded in the provided session. This
    can be used to further extract signature-defs, collection-defs, etc.

  Raises:
    RuntimeError: MetaGraphDef associated with the tags cannot be found.
  """
  with sess.graph.as_default():
    # Build the SavedModel protocol buffer and find requested meta graph def.
    saved_model = _parse_saved_model(export_dir)
    found_match = False
    for meta_graph_def in saved_model.meta_graphs:
      if set(meta_graph_def.meta_info_def.tags) == set(tags):
        meta_graph_def_to_load = meta_graph_def
        found_match = True
        break

    if not found_match:
      raise RuntimeError(
          "MetaGraphDef associated with tags " + str(tags).strip("[]") +
          " could not be found in SavedModel. To inspect available tag-sets in"
          " the SavedModel, please use the SavedModel CLI: `saved_model_cli`"
      )

    # Build a saver by importing the meta graph def to load.
    saver = tf_saver.import_meta_graph(meta_graph_def_to_load, **saver_kwargs)

    if saver:
      # Build the checkpoint path where the variables are located.
      variables_path = os.path.join(
          compat.as_bytes(export_dir),
          compat.as_bytes(constants.VARIABLES_DIRECTORY),
          compat.as_bytes(constants.VARIABLES_FILENAME))

      # Restore the variables using the built saver in the provided session.
      saver.restore(sess, variables_path)
    else:
      tf_logging.info("The specified SavedModel has no variables; no "
                      "checkpoints were restored.")

    # Get asset tensors, if any.
    asset_tensors_dictionary = _get_asset_tensors(export_dir,
                                                  meta_graph_def_to_load)

    main_op_tensor = _get_main_op_tensor(meta_graph_def_to_load)
    if main_op_tensor is not None:
      sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary)
    else:
      legacy_init_op_tensor = _get_legacy_init_op_tensor(meta_graph_def_to_load)
      if legacy_init_op_tensor is not None:
        sess.run(
            fetches=[legacy_init_op_tensor], feed_dict=asset_tensors_dictionary)

    return meta_graph_def_to_load
