# 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.
# ==============================================================================
"""SavedModel builder implementation."""

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

import os

from google.protobuf.any_pb2 import Any

from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import variables
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
from tensorflow.python.util.tf_export import tf_export


@tf_export("saved_model.builder.SavedModelBuilder")
class SavedModelBuilder(object):
  """Builds the `SavedModel` protocol buffer and saves variables and assets.

  The `SavedModelBuilder` class provides functionality to build a `SavedModel`
  protocol buffer. Specifically, this allows multiple meta graphs to be saved as
  part of a single language-neutral `SavedModel`, while sharing variables and
  assets.

  To build a SavedModel, the first meta graph must be saved with variables.
  Subsequent meta graphs will simply be saved with their graph definitions. If
  assets need to be saved and written or copied to disk, they can be provided
  when the meta graph def is added. If multiple meta graph defs are associated
  an asset of the same name, only the first version is retained.

  Each meta graph added to the SavedModel must be annotated with tags. The tags
  provide a means to identify the specific meta graph to load and restore, along
  with the shared set of variables and assets.

  Typical usage for the `SavedModelBuilder`:
  ```python
  ...
  builder = tf.saved_model.builder.SavedModelBuilder(export_dir)

  with tf.Session(graph=tf.Graph()) as sess:
    ...
    builder.add_meta_graph_and_variables(sess,
                                    ["foo-tag"],
                                    signature_def_map=foo_signatures,
                                    assets_collection=foo_assets)
  ...

  with tf.Session(graph=tf.Graph()) as sess:
    ...
    builder.add_meta_graph(["bar-tag", "baz-tag"])
  ...

  builder.save()
  ```
  """

  def __init__(self, export_dir):
    self._saved_model = saved_model_pb2.SavedModel()
    self._saved_model.saved_model_schema_version = (
        constants.SAVED_MODEL_SCHEMA_VERSION)

    self._export_dir = export_dir
    if file_io.file_exists(export_dir):
      raise AssertionError(
          "Export directory already exists. Please specify a different export "
          "directory: %s" % export_dir)

    file_io.recursive_create_dir(self._export_dir)

    # Boolean to track whether variables and assets corresponding to the
    # SavedModel have been saved. Specifically, the first meta graph to be added
    # MUST use the add_meta_graph_and_variables() API. Subsequent add operations
    # on the SavedModel MUST use the add_meta_graph() API which does not save
    # weights.
    self._has_saved_variables = False

  def _save_and_write_assets(self, assets_collection_to_add=None):
    """Saves asset to the meta graph and writes asset files to disk.

    Args:
      assets_collection_to_add: The collection where the asset paths are setup.
    """
    asset_filename_map = _maybe_save_assets(assets_collection_to_add)

    # Return if there are no assets to write.
    if not asset_filename_map:
      tf_logging.info("No assets to write.")
      return

    assets_destination_dir = os.path.join(
        compat.as_bytes(self._export_dir),
        compat.as_bytes(constants.ASSETS_DIRECTORY))

    if not file_io.file_exists(assets_destination_dir):
      file_io.recursive_create_dir(assets_destination_dir)

    # Copy each asset from source path to destination path.
    for asset_basename, asset_source_filepath in asset_filename_map.items():
      asset_destination_filepath = os.path.join(
          compat.as_bytes(assets_destination_dir),
          compat.as_bytes(asset_basename))

      # Only copy the asset file to the destination if it does not already
      # exist. This is to ensure that an asset with the same name defined as
      # part of multiple graphs is only copied the first time.
      if not file_io.file_exists(asset_destination_filepath):
        file_io.copy(asset_source_filepath, asset_destination_filepath)

    tf_logging.info("Assets written to: %s",
                    compat.as_text(assets_destination_dir))

  def _maybe_add_legacy_init_op(self, legacy_init_op=None):
    """Add legacy init op to the SavedModel.

    Args:
      legacy_init_op: Optional legacy init op to support backward compatibility.

    Raises:
      TypeError if legacy init op is not of type `Operation`.
      AssertionError if the graph already contains one or more legacy init ops.
    """
    if legacy_init_op is not None:
      if not isinstance(legacy_init_op, ops.Operation):
        raise TypeError("legacy_init_op needs to be an Operation: %r" %
                        legacy_init_op)
      if ops.get_collection(constants.LEGACY_INIT_OP_KEY):
        raise AssertionError(
            "graph already contains one or more legacy init ops under the "
            "collection {}.".format(constants.LEGACY_INIT_OP_KEY))
      ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, legacy_init_op)

  def _add_main_op(self, main_op):
    """Add main op to the SavedModel.

    Args:
      main_op: Main op to run as part of graph initialization.

    Raises:
      TypeError if main op is not of type `Operation`.
    """
    if main_op is not None:
      if not isinstance(main_op, ops.Operation):
        raise TypeError("main_op needs to be an Operation: %r" % main_op)
      ops.add_to_collection(constants.MAIN_OP_KEY, main_op)

  def _add_train_op(self, train_op):
    """Add train op to the SavedModel.

    Note that this functionality is in development, and liable to be
    moved elsewhere.

    Args:
      train_op: Op or group of ops that are used for training. These are
        stored as a collection with key TRAIN_OP_KEY, but not executed.

    Raises:
      TypeError if Train op is not of type `Operation`.
    """
    if train_op is not None:
      if (not isinstance(train_op, ops.Tensor) and
          not isinstance(train_op, ops.Operation)):
        raise TypeError("train_op needs to be a Tensor or Op: %r" % train_op)
      ops.add_to_collection(constants.TRAIN_OP_KEY, train_op)

  def _tag_and_add_meta_graph(self, meta_graph_def, tags, signature_def_map):
    """Tags the meta graph def and adds it to the SavedModel.

    Tags the meta graph def with the supplied tags, adds signature defs to it if
    provided and appends the meta graph def to the SavedModel proto.

    Args:
      meta_graph_def: The meta graph def to add to the SavedModel.
      tags: The set of tags to annotate the meta graph def with.
      signature_def_map: The map of signature defs to be added to the meta graph
          def.
    """
    for tag in tags:
      meta_graph_def.meta_info_def.tags.append(tag)

    if signature_def_map is not None:
      for key in signature_def_map:
        meta_graph_def.signature_def[key].CopyFrom(signature_def_map[key])

    proto_meta_graph_def = self._saved_model.meta_graphs.add()
    proto_meta_graph_def.CopyFrom(meta_graph_def)

  def _validate_tensor_info(self, tensor_info):
    """Validates the `TensorInfo` proto.

    Checks if the `encoding` (`name` or `coo_sparse`) and `dtype` fields exist
    and are non-empty.

    Args:
      tensor_info: `TensorInfo` protocol buffer to validate.

    Raises:
      AssertionError: If the `name` or `dtype` fields of the supplied
          `TensorInfo` proto are not populated.
    """
    if tensor_info is None:
      raise AssertionError(
          "All TensorInfo protos used in the SignatureDefs must have the name "
          "and dtype fields set.")
    if tensor_info.WhichOneof("encoding") is None:
      # TODO(soergel) validate each of the fields of coo_sparse
      raise AssertionError(
          "All TensorInfo protos used in the SignatureDefs must have one of "
          "the 'encoding' fields (e.g., name or coo_sparse) set: %s"
          % tensor_info)
    if tensor_info.dtype is types_pb2.DT_INVALID:
      raise AssertionError(
          "All TensorInfo protos used in the SignatureDefs must have the dtype "
          "field set: %s" % tensor_info)

  def _validate_signature_def_map(self, signature_def_map):
    """Validates the `SignatureDef` entries in the signature def map.

    Validation of entries in the signature def map includes ensuring that the
    `name` and `dtype` fields of the TensorInfo protos of the `inputs` and
    `outputs` of each `SignatureDef` are populated.

    Args:
      signature_def_map: The map of signature defs to be validated.
    """
    if signature_def_map is not None:
      for signature_def_key in signature_def_map:
        signature_def = signature_def_map[signature_def_key]
        inputs = signature_def.inputs
        outputs = signature_def.outputs
        for inputs_key in inputs:
          self._validate_tensor_info(inputs[inputs_key])
        for outputs_key in outputs:
          self._validate_tensor_info(outputs[outputs_key])

  def _add_collections(
      self, assets_collection, legacy_init_op, main_op, train_op):
    """Add asset and op collections to be saved."""
    # Save asset files and write them to disk, if any.
    self._save_and_write_assets(assets_collection)

    if main_op is None:
      # Add legacy init op to the SavedModel.
      self._maybe_add_legacy_init_op(legacy_init_op)
    else:
      self._add_main_op(main_op)

    self._add_train_op(train_op)

  def add_meta_graph(self,
                     tags,
                     signature_def_map=None,
                     assets_collection=None,
                     legacy_init_op=None,
                     clear_devices=False,
                     main_op=None,
                     strip_default_attrs=False):
    # pylint: disable=line-too-long
    """Adds the current meta graph to the SavedModel.

    Creates a Saver in the current scope and uses the Saver to export the meta
    graph def. Invoking this API requires the `add_meta_graph_and_variables()`
    API to have been invoked before.

    Args:
      tags: The set of tags to annotate the meta graph def with.
      signature_def_map: The map of signature defs to be added to the meta graph
          def.
      assets_collection: Assets collection to be saved with SavedModel. Note
          that this collection should be a subset of the assets saved as part of
          the first meta graph in the SavedModel.
      legacy_init_op: Legacy support for op or group of ops to execute after the
          restore op upon a load.
      clear_devices: Set to true if the device info on the default graph should
          be cleared.
      main_op: Op or group of ops to execute when the graph is loaded. Note
          that when the main_op is specified it is run after the restore op at
          load-time.
      strip_default_attrs: Boolean. If `True`, default-valued attributes will be
        removed from the NodeDefs. For a detailed guide, see
        [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).

    Raises:
      AssertionError: If the variables for the SavedModel have not been saved
          yet, or if the graph already contains one or more legacy init ops.
    """
    # pylint: enable=line-too-long
    if not self._has_saved_variables:
      raise AssertionError(
          "Graph state including variables and assets has not been saved yet. "
          "Please invoke `add_meta_graph_and_variables()` first.")

    # Validate the signature def map to ensure all included TensorInfos are
    # properly populated.
    self._validate_signature_def_map(signature_def_map)

    # Add assets and ops
    self._add_collections(assets_collection, legacy_init_op, main_op, None)

    # Initialize a saver to generate a sharded output for all saveables in the
    # current scope.
    saver = tf_saver.Saver(
        variables._all_saveable_objects(),  # pylint: disable=protected-access
        sharded=True,
        write_version=saver_pb2.SaverDef.V2,
        allow_empty=True)

    # The graph almost certainly previously contained at least one Saver, and
    # possibly several (e.g. one for loading a pretrained embedding, and another
    # for the model weights).  However, a *new* Saver was just created that
    # includes all of the variables.  Removing the preexisting ones was the
    # motivation for the clear_extraneous_savers option, but it turns out that
    # there are edge cases where that option breaks the graph.  Until that is
    # resolved, we just leave the option set to False for now.
    # TODO(soergel): Reinstate clear_extraneous_savers=True when possible.
    meta_graph_def = saver.export_meta_graph(
        clear_devices=clear_devices, strip_default_attrs=strip_default_attrs)

    # Tag the meta graph def and add it to the SavedModel.
    self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map)

  def add_meta_graph_and_variables(self,
                                   sess,
                                   tags,
                                   signature_def_map=None,
                                   assets_collection=None,
                                   legacy_init_op=None,
                                   clear_devices=False,
                                   main_op=None,
                                   strip_default_attrs=False):
    # pylint: disable=line-too-long
    """Adds the current meta graph to the SavedModel and saves variables.

    Creates a Saver to save the variables from the provided session. Exports the
    corresponding meta graph def. This function assumes that the variables to be
    saved have been initialized. For a given `SavedModelBuilder`, this API must
    be called exactly once and for the first meta graph to save. For subsequent
    meta graph defs to be added, the `add_meta_graph()` API must be used.

    Args:
      sess: The TensorFlow session from which to save the meta graph and
        variables.
      tags: The set of tags with which to save the meta graph.
      signature_def_map: The map of signature def map to add to the meta graph
        def.
      assets_collection: Assets collection to be saved with SavedModel.
      legacy_init_op: Legacy support for op or group of ops to execute after the
          restore op upon a load.
      clear_devices: Set to true if the device info on the default graph should
          be cleared.
      main_op: Op or group of ops to execute when the graph is loaded. Note
          that when the main_op is specified it is run after the restore op at
          load-time.
      strip_default_attrs: Boolean. If `True`, default-valued attributes will be
        removed from the NodeDefs. For a detailed guide, see
        [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).

    """
    # pylint: enable=line-too-long
    if self._has_saved_variables:
      raise AssertionError("Graph state including variables and assets has "
                           "already been saved. Please invoke "
                           "`add_meta_graph()` instead.")

    # Validate the signature def map to ensure all included TensorInfos are
    # properly populated.
    self._validate_signature_def_map(signature_def_map)

    # Add assets and ops
    self._add_collections(assets_collection, legacy_init_op, main_op, None)

    # Create the variables sub-directory, if it does not exist.
    variables_dir = os.path.join(
        compat.as_text(self._export_dir),
        compat.as_text(constants.VARIABLES_DIRECTORY))
    if not file_io.file_exists(variables_dir):
      file_io.recursive_create_dir(variables_dir)

    variables_path = os.path.join(
        compat.as_text(variables_dir),
        compat.as_text(constants.VARIABLES_FILENAME))

    # Initialize a saver to generate a sharded output for all saveables in the
    # current scope.
    saver = tf_saver.Saver(
        variables._all_saveable_objects(),  # pylint: disable=protected-access
        sharded=True,
        write_version=saver_pb2.SaverDef.V2,
        allow_empty=True)

    # Save the variables. Also, disable writing the checkpoint state proto. The
    # file is not used during SavedModel loading. In addition, since a
    # SavedModel can be copied or moved, this avoids the checkpoint state to
    # become outdated.
    saver.save(sess, variables_path, write_meta_graph=False, write_state=False)

    # Export the meta graph def.

    # The graph almost certainly previously contained at least one Saver, and
    # possibly several (e.g. one for loading a pretrained embedding, and another
    # for the model weights).  However, a *new* Saver was just created that
    # includes all of the variables.  Removing the preexisting ones was the
    # motivation for the clear_extraneous_savers option, but it turns out that
    # there are edge cases where that option breaks the graph.  Until that is
    # resolved, we just leave the option set to False for now.
    # TODO(soergel): Reinstate clear_extraneous_savers=True when possible.
    meta_graph_def = saver.export_meta_graph(
        clear_devices=clear_devices, strip_default_attrs=strip_default_attrs)

    # Tag the meta graph def and add it to the SavedModel.
    self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map)

    # Mark this instance of SavedModel as having saved variables, such that
    # subsequent attempts to save variables will fail.
    self._has_saved_variables = True

  def save(self, as_text=False):
    """Writes a `SavedModel` protocol buffer to disk.

    The function writes the SavedModel protocol buffer to the export directory
    in serialized format.

    Args:
      as_text: Writes the SavedModel protocol buffer in text format to disk.

    Returns:
      The path to which the SavedModel protocol buffer was written.
    """
    if not file_io.file_exists(self._export_dir):
      file_io.recursive_create_dir(self._export_dir)

    if as_text:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
      file_io.write_string_to_file(path, str(self._saved_model))
    else:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
      file_io.write_string_to_file(path, self._saved_model.SerializeToString())
    tf_logging.info("SavedModel written to: %s", compat.as_text(path))

    return path


def _maybe_save_assets(assets_collection_to_add=None):
  """Saves assets to the meta graph.

  Args:
    assets_collection_to_add: The collection where the asset paths are setup.

  Returns:
    A dict of asset basenames for saving to the original full path to the asset.

  Raises:
    ValueError: Indicating an invalid filepath tensor.
  """
  # Map of target file names to original filenames
  asset_filename_map = {}

  if assets_collection_to_add is None:
    tf_logging.info("No assets to save.")
    return asset_filename_map

  # Iterate over the supplied asset collection, build the `AssetFile` proto
  # and add them to the collection with key `constants.ASSETS_KEY`, in the
  # graph.
  for asset_tensor in assets_collection_to_add:
    asset_source_filepath = _asset_path_from_tensor(asset_tensor)
    if not asset_source_filepath:
      raise ValueError("Invalid asset filepath tensor %s" % asset_tensor)

    asset_filename = _get_asset_filename_to_add(
        asset_source_filepath, asset_filename_map)

    # Build `AssetFile` proto and add it to the asset collection in the graph.
    # Note that this should be done even when the file is a duplicate of an
    # already-added file, as the tensor reference should still exist.
    _add_asset_to_collection(asset_filename, asset_tensor)

    # In the cases where we are adding a duplicate, this will result in the
    # last of the filepaths being the one used for copying the file to the
    # SavedModel. Since the files in question are the same, it doesn't matter
    # either way.
    asset_filename_map[asset_filename] = asset_source_filepath

  tf_logging.info("Assets added to graph.")
  return asset_filename_map


def _get_asset_filename_to_add(asset_filepath, asset_filename_map):
  """Get a unique basename to add to the SavedModel if this file is unseen.

  Assets come from users as full paths, and we save them out to the
  SavedModel as basenames. In some cases, the basenames collide. Here,
  we dedupe asset basenames by first checking if the file is the same,
  and, if different, generate and return an index-suffixed basename
  that can be used to add the asset to the SavedModel.

  Args:
    asset_filepath: the full path to the asset that is being saved
    asset_filename_map: a dict of filenames used for saving the asset in
      the SavedModel to full paths from which the filenames were derived.

  Returns:
    Uniquified filename string if the file is not a duplicate, or the original
    filename if the file has already been seen and saved.
  """
  asset_filename = os.path.basename(asset_filepath)

  if asset_filename not in asset_filename_map:
    # This is an unseen asset. Safe to add.
    return asset_filename

  other_asset_filepath = asset_filename_map[asset_filename]
  if other_asset_filepath == asset_filepath:
    # This is the same file, stored twice in the collection list. No need
    # to make unique.
    return asset_filename

  # Else, asset_filename is in the map, and the filepath is different. Dedupe.
  if not file_io.filecmp(asset_filepath, other_asset_filepath):
    # Files are different; dedupe filenames.
    return _get_unique_asset_filename(asset_filename, asset_filename_map)

  # Files are the same; don't make unique.
  return asset_filename


def _get_unique_asset_filename(asset_filename, asset_filename_map):
  i = 1
  unique_filename = asset_filename
  while unique_filename in asset_filename_map:
    unique_filename = compat.as_bytes("_").join(
        [compat.as_bytes(asset_filename), compat.as_bytes(str(i))])
    i += 1
  return unique_filename


def _asset_path_from_tensor(path_tensor):
  """Returns the filepath value stored in constant `path_tensor`.

  Args:
    path_tensor: Tensor of a file-path.

  Returns:
    The string value i.e. path of the tensor, if valid.

  Raises:
    TypeError if tensor does not match expected op type, dtype or value.
  """
  if not isinstance(path_tensor, ops.Tensor):
    raise TypeError("Asset path tensor must be a Tensor.")
  if path_tensor.op.type != "Const":
    raise TypeError("Asset path tensor must be of type constant.")
  if path_tensor.dtype != dtypes.string:
    raise TypeError("Asset path tensor must be of dtype string.")
  str_values = path_tensor.op.get_attr("value").string_val
  if len(str_values) != 1:
    raise TypeError("Asset path tensor must be a scalar.")
  return str_values[0]


def _add_asset_to_collection(asset_filename, asset_tensor):
  """Builds an asset proto and adds it to the asset collection of the graph.

  Args:
    asset_filename: The filename of the asset to be added.
    asset_tensor: The asset tensor used to populate the tensor info of the
        asset proto.
  """
  asset_proto = meta_graph_pb2.AssetFileDef()
  asset_proto.filename = asset_filename
  asset_proto.tensor_info.name = asset_tensor.name

  asset_any_proto = Any()
  asset_any_proto.Pack(asset_proto)
  ops.add_to_collection(constants.ASSETS_KEY, asset_any_proto)
