# Copyright 2021 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.
# ==============================================================================
"""API for specifying `tf.data` options."""

import enum

from absl import logging

from tensorflow.core.framework import dataset_options_pb2
from tensorflow.core.framework import model_pb2
from tensorflow.python.data.util import options as options_lib
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export


@tf_export("data.experimental.AutotuneAlgorithm")
class AutotuneAlgorithm(enum.Enum):
  """Represents the type of autotuning algorithm to use.

  DEFAULT: The default behavior is implementation specific and may change over
  time.

  HILL_CLIMB: In each optimization step, this algorithm chooses the optimial
  parameter and increases its value by 1.

  GRADIENT_DESCENT: In each optimization step, this algorithm updates the
  parameter values in the optimal direction.

  MAX_PARALLELISM: Similar to HILL_CLIMB but uses a relaxed stopping condition,
  allowing the optimization to oversubscribe the CPU.

  STAGE_BASED: In each optimization step, this algorithm chooses the worst
  bottleneck parameter and increases its value by 1.
  """
  DEFAULT = 0
  HILL_CLIMB = 1
  GRADIENT_DESCENT = 2
  MAX_PARALLELISM = 3
  STAGE_BASED = 4

  @classmethod
  def _to_proto(cls, obj):
    if obj == cls.DEFAULT:
      return model_pb2.AutotuneAlgorithm.DEFAULT
    if obj == cls.HILL_CLIMB:
      return model_pb2.AutotuneAlgorithm.HILL_CLIMB
    if obj == cls.GRADIENT_DESCENT:
      return model_pb2.AutotuneAlgorithm.GRADIENT_DESCENT
    if obj == cls.MAX_PARALLELISM:
      return model_pb2.AutotuneAlgorithm.MAX_PARALLELISM
    if obj == cls.STAGE_BASED:
      return model_pb2.AutotuneAlgorithm.STAGE_BASED
    raise ValueError(
        f"Invalid `obj.` Supported values include `DEFAULT`, `HILL_CLIMB` "
        f"`GRADIENT_DESCENT`, and `STAGE_BASED`. Got {obj.name}.")

  @classmethod
  def _from_proto(cls, pb):
    if pb == model_pb2.AutotuneAlgorithm.DEFAULT:
      return cls.DEFAULT
    if pb == model_pb2.AutotuneAlgorithm.HILL_CLIMB:
      return cls.HILL_CLIMB
    if pb == model_pb2.AutotuneAlgorithm.GRADIENT_DESCENT:
      return cls.GRADIENT_DESCENT
    if pb == model_pb2.AutotuneAlgorithm.MAX_PARALLELISM:
      return cls.MAX_PARALLELISM
    if pb == model_pb2.AutotuneAlgorithm.STAGE_BASED:
      return cls.STAGE_BASED
    raise ValueError(
        f"Invalid `pb.` Supported values include `DEFAULT`, `HILL_CLIMB`, "
        f"`GRADIENT_DESCENT` and `STAGE_BASED`. Got {pb}.")


@tf_export("data.experimental.AutoShardPolicy")
class AutoShardPolicy(enum.IntEnum):
  """Represents the type of auto-sharding to use.

  OFF: No sharding will be performed.

  AUTO: Attempts FILE-based sharding, falling back to DATA-based sharding.

  FILE: Shards by input files (i.e. each worker will get a set of files to
  process). When this option is selected, make sure that there is at least as
  many files as workers. If there are fewer input files than workers, a runtime
  error will be raised.

  DATA: Shards by elements produced by the dataset. Each worker will process the
  whole dataset and discard the portion that is not for itself. Note that for
  this mode to correctly partitions the dataset elements, the dataset needs to
  produce elements in a deterministic order.

  HINT: Looks for the presence of `shard(SHARD_HINT, ...)` which is treated as a
  placeholder to replace with `shard(num_workers, worker_index)`.
  """

  # LINT.IfChange
  OFF = -1
  AUTO = 0
  FILE = 1
  DATA = 2
  HINT = 3
  # LINT.ThenChange(//tensorflow/python/data/experimental/ops/data_service_ops.py:tf_data_service_sharding_policy)

  @classmethod
  def _to_proto(cls, obj):
    """Convert enum to proto."""
    if obj == cls.OFF:
      return dataset_options_pb2.AutoShardPolicy.OFF
    if obj == cls.FILE:
      return dataset_options_pb2.AutoShardPolicy.FILE
    if obj == cls.DATA:
      return dataset_options_pb2.AutoShardPolicy.DATA
    if obj == cls.AUTO:
      return dataset_options_pb2.AutoShardPolicy.AUTO
    if obj == cls.HINT:
      return dataset_options_pb2.AutoShardPolicy.HINT
    raise ValueError(
        f"Invalid `obj.` Supported values include `OFF`, `FILE`, `DATA`,"
        f"`AUTO`, and `HINT`. Got {obj.name}."
    )

  @classmethod
  def _from_proto(cls, pb):
    """Convert proto to enum."""
    if pb == dataset_options_pb2.AutoShardPolicy.OFF:
      return cls.OFF
    if pb == dataset_options_pb2.AutoShardPolicy.FILE:
      return cls.FILE
    if pb == dataset_options_pb2.AutoShardPolicy.DATA:
      return cls.DATA
    if pb == dataset_options_pb2.AutoShardPolicy.AUTO:
      return cls.AUTO
    if pb == dataset_options_pb2.AutoShardPolicy.HINT:
      return cls.HINT
    raise ValueError(
        f"Invalid `pb.` Supported values include `OFF`, `FILE`, `DATA`,"
        f"`AUTO`, and `HINT`. Got {pb}."
    )


@tf_export("data.experimental.ExternalStatePolicy")
class ExternalStatePolicy(enum.Enum):
  """Represents how to handle external state during serialization.

  See the `tf.data.Options.experimental_external_state_policy` documentation
  for more information.
  """
  WARN = 0
  IGNORE = 1
  FAIL = 2

  @classmethod
  def _to_proto(cls, obj):
    """Convert enum to proto."""
    if obj == cls.IGNORE:
      return dataset_options_pb2.ExternalStatePolicy.POLICY_IGNORE
    if obj == cls.FAIL:
      return dataset_options_pb2.ExternalStatePolicy.POLICY_FAIL
    if obj == cls.WARN:
      return dataset_options_pb2.ExternalStatePolicy.POLICY_WARN
    raise ValueError(
        f"Invalid `obj.` Supported values include `POLICY_IGNORE`,"
        f"`POLICY_FAIL`, `POLICY_WARN`. Got {obj.name}.")

  @classmethod
  def _from_proto(cls, pb):
    """Convert proto to enum."""
    if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_IGNORE:
      return cls.IGNORE
    if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_FAIL:
      return cls.FAIL
    if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_WARN:
      return cls.WARN
    raise ValueError(
        f"Invalid `pb.` Supported values include `POLICY_IGNORE`,"
        f"`POLICY_FAIL`, `POLICY_WARN`. Got {pb}.")


@tf_export("data.experimental.AutotuneOptions")
class AutotuneOptions(options_lib.OptionsBase):
  """Represents options for autotuning dataset performance.

  ```python
  options = tf.data.Options()
  options.autotune.enabled = False
  dataset = dataset.with_options(options)
  ```
  """

  enabled = options_lib.create_option(
      name="enabled",
      ty=bool,
      docstring="Whether to automatically tune performance knobs. If None, "
      "defaults to True.")

  cpu_budget = options_lib.create_option(
      name="cpu_budget",
      ty=int,
      docstring="When autotuning is enabled (through `autotune`), determines "
      "the CPU budget to use. Values greater than the number of schedulable "
      "CPU cores are allowed but may result in CPU contention. If None, "
      "defaults to the number of schedulable CPU cores.")

  ram_budget = options_lib.create_option(
      name="ram_budget",
      ty=int,
      docstring="When autotuning is enabled (through `autotune`), determines "
      "the RAM budget to use. Values greater than the available RAM in bytes "
      "may result in OOM. If None, defaults to half of the available RAM in "
      "bytes.")

  autotune_algorithm = options_lib.create_option(
      name="autotune_algorithm",
      ty=AutotuneAlgorithm,
      docstring="When autotuning is enabled (through `autotune`), determines "
      "the algorithm to use.")

  def _to_proto(self):
    pb = dataset_options_pb2.AutotuneOptions()
    if self.enabled is not None:
      pb.enabled = self.enabled
    if self.cpu_budget is not None:
      pb.cpu_budget = self.cpu_budget
    if self.ram_budget is not None:
      pb.ram_budget = self.ram_budget
    if self.autotune_algorithm is not None:
      pb.autotune_algorithm = AutotuneAlgorithm._to_proto(  # pylint: disable=protected-access
          self.autotune_algorithm)
    return pb

  def _from_proto(self, pb):
    if pb.WhichOneof("optional_enabled") is not None:
      self.enabled = pb.enabled
    if pb.WhichOneof("optional_cpu_budget") is not None:
      self.cpu_budget = pb.cpu_budget
    if pb.WhichOneof("optional_ram_budget") is not None:
      self.ram_budget = pb.ram_budget
    if pb.WhichOneof("optional_autotune_algorithm") is not None:
      self.autotune_algorithm = AutotuneAlgorithm._from_proto(  # pylint: disable=protected-access
          pb.autotune_algorithm)

  def _set_mutable(self, mutable):
    """Change the mutability value to `mutable` on this options and children."""
    # pylint: disable=protected-access
    object.__setattr__(self, "_mutable", mutable)


@tf_export("data.experimental.DistributeOptions")
class DistributeOptions(options_lib.OptionsBase):
  """Represents options for distributed data processing.

  You can set the distribution options of a dataset through the
  `experimental_distribute` property of `tf.data.Options`; the property is
  an instance of `tf.data.experimental.DistributeOptions`.

  ```python
  options = tf.data.Options()
  options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
  dataset = dataset.with_options(options)
  ```
  """

  auto_shard_policy = options_lib.create_option(
      name="auto_shard_policy",
      ty=AutoShardPolicy,
      docstring="The type of sharding to use. See "
      "`tf.data.experimental.AutoShardPolicy` for additional information.",
      default_factory=lambda: AutoShardPolicy.AUTO)

  num_devices = options_lib.create_option(
      name="num_devices",
      ty=int,
      docstring=
      "The number of devices attached to this input pipeline. This will be "
      "automatically set by `MultiDeviceIterator`.")

  def _to_proto(self):
    pb = dataset_options_pb2.DistributeOptions()
    pb.auto_shard_policy = AutoShardPolicy._to_proto(self.auto_shard_policy)  # pylint: disable=protected-access
    if self.num_devices is not None:
      pb.num_devices = self.num_devices
    return pb

  def _from_proto(self, pb):
    self.auto_shard_policy = AutoShardPolicy._from_proto(pb.auto_shard_policy)  # pylint: disable=protected-access
    if pb.WhichOneof("optional_num_devices") is not None:
      self.num_devices = pb.num_devices


@tf_export("data.experimental.OptimizationOptions")
class OptimizationOptions(options_lib.OptionsBase):
  """Represents options for dataset optimizations.

  You can set the optimization options of a dataset through the
  `experimental_optimization` property of `tf.data.Options`; the property is
  an instance of `tf.data.experimental.OptimizationOptions`.

  ```python
  options = tf.data.Options()
  options.experimental_optimization.noop_elimination = True
  options.experimental_optimization.apply_default_optimizations = False
  dataset = dataset.with_options(options)
  ```
  """
  apply_default_optimizations = options_lib.create_option(
      name="apply_default_optimizations",
      ty=bool,
      docstring=
      "Whether to apply default graph optimizations. If False, only graph "
      "optimizations that have been explicitly enabled will be applied.")

  filter_fusion = options_lib.create_option(
      name="filter_fusion",
      ty=bool,
      docstring=
      "Whether to fuse filter transformations. If None, defaults to False.")

  filter_parallelization = options_lib.create_option(
      name="filter_parallelization",
      ty=bool,
      docstring=
      "Whether to parallelize stateless filter transformations. If None, "
      "defaults to False.")

  inject_prefetch = options_lib.create_option(
      name="inject_prefetch",
      ty=bool,
      docstring=
      "Whether to inject prefetch transformation as the last transformation "
      "when the last transformation is a synchronous transformation. If None, "
      "defaults to True.")

  map_and_batch_fusion = options_lib.create_option(
      name="map_and_batch_fusion",
      ty=bool,
      docstring=
      "Whether to fuse map and batch transformations. If None, defaults to "
      "True.")

  map_and_filter_fusion = options_lib.create_option(
      name="map_and_filter_fusion",
      ty=bool,
      docstring=
      "Whether to fuse map and filter transformations. If None, defaults to "
      "False.")

  map_fusion = options_lib.create_option(
      name="map_fusion",
      ty=bool,
      docstring="Whether to fuse map transformations. If None, defaults to "
      "False.")

  map_parallelization = options_lib.create_option(
      name="map_parallelization",
      ty=bool,
      docstring=
      "Whether to parallelize stateless map transformations. If None, defaults "
      "to True.")

  noop_elimination = options_lib.create_option(
      name="noop_elimination",
      ty=bool,
      docstring=
      "Whether to eliminate no-op transformations. If None, defaults to True.")

  parallel_batch = options_lib.create_option(
      name="parallel_batch",
      ty=bool,
      docstring="Whether to parallelize copying of batch elements. If None, "
      "defaults to True.")

  shuffle_and_repeat_fusion = options_lib.create_option(
      name="shuffle_and_repeat_fusion",
      ty=bool,
      docstring="Whether to fuse shuffle and repeat transformations. If None, "
      "defaults to True.")

  def _to_proto(self):
    pb = dataset_options_pb2.OptimizationOptions()
    if self.apply_default_optimizations is not None:
      pb.apply_default_optimizations = self.apply_default_optimizations
    if self.filter_fusion is not None:
      pb.filter_fusion = self.filter_fusion
    if self.filter_parallelization is not None:
      pb.filter_parallelization = self.filter_parallelization
    if self.inject_prefetch is not None:
      pb.inject_prefetch = self.inject_prefetch
    if self.map_and_batch_fusion is not None:
      pb.map_and_batch_fusion = self.map_and_batch_fusion
    if self.map_and_filter_fusion is not None:
      pb.map_and_filter_fusion = self.map_and_filter_fusion
    if self.map_fusion is not None:
      pb.map_fusion = self.map_fusion
    if self.map_parallelization is not None:
      pb.map_parallelization = self.map_parallelization
    if self.noop_elimination is not None:
      pb.noop_elimination = self.noop_elimination
    if self.parallel_batch is not None:
      pb.parallel_batch = self.parallel_batch
    if self.shuffle_and_repeat_fusion is not None:
      pb.shuffle_and_repeat_fusion = self.shuffle_and_repeat_fusion
    return pb

  def _from_proto(self, pb):
    if pb.WhichOneof("optional_apply_default_optimizations") is not None:
      self.apply_default_optimizations = pb.apply_default_optimizations
    if pb.WhichOneof("optional_filter_fusion") is not None:
      self.filter_fusion = pb.filter_fusion
    if pb.WhichOneof("optional_filter_parallelization") is not None:
      self.filter_parallelization = pb.filter_parallelization
    if pb.WhichOneof("optional_inject_prefetch") is not None:
      self.inject_prefetch = pb.inject_prefetch
    if pb.WhichOneof("optional_map_and_batch_fusion") is not None:
      self.map_and_batch_fusion = pb.map_and_batch_fusion
    if pb.WhichOneof("optional_map_and_filter_fusion") is not None:
      self.map_and_filter_fusion = pb.map_and_filter_fusion
    if pb.WhichOneof("optional_map_fusion") is not None:
      self.map_fusion = pb.map_fusion
    if pb.WhichOneof("optional_map_parallelization") is not None:
      self.map_parallelization = pb.map_parallelization
    if pb.WhichOneof("optional_noop_elimination") is not None:
      self.noop_elimination = pb.noop_elimination
    if pb.WhichOneof("optional_parallel_batch") is not None:
      self.parallel_batch = pb.parallel_batch
    if pb.WhichOneof("optional_shuffle_and_repeat_fusion") is not None:
      self.shuffle_and_repeat_fusion = pb.shuffle_and_repeat_fusion

  def _set_mutable(self, mutable):
    """Change the mutability value to `mutable` on this options and children."""
    # pylint: disable=protected-access
    object.__setattr__(self, "_mutable", mutable)


@deprecation.deprecated_endpoints("data.experimental.ThreadingOptions")
@tf_export("data.experimental.ThreadingOptions", "data.ThreadingOptions")
class ThreadingOptions(options_lib.OptionsBase):
  """Represents options for dataset threading.

  You can set the threading options of a dataset through the
  `experimental_threading` property of `tf.data.Options`; the property is
  an instance of `tf.data.ThreadingOptions`.

  ```python
  options = tf.data.Options()
  options.threading.private_threadpool_size = 10
  dataset = dataset.with_options(options)
  ```
  """

  max_intra_op_parallelism = options_lib.create_option(
      name="max_intra_op_parallelism",
      ty=int,
      docstring=
      "If set, it overrides the maximum degree of intra-op parallelism.")

  private_threadpool_size = options_lib.create_option(
      name="private_threadpool_size",
      ty=int,
      docstring=
      "If set, the dataset will use a private threadpool of the given size. "
      "The value 0 can be used to indicate that the threadpool size should be "
      "determined at runtime based on the number of available CPU cores.")

  def _to_proto(self):
    pb = dataset_options_pb2.ThreadingOptions()
    if self.max_intra_op_parallelism is not None:
      pb.max_intra_op_parallelism = self.max_intra_op_parallelism
    if self.private_threadpool_size is not None:
      pb.private_threadpool_size = self.private_threadpool_size
    return pb

  def _from_proto(self, pb):
    if pb.WhichOneof("optional_max_intra_op_parallelism") is not None:
      self.max_intra_op_parallelism = pb.max_intra_op_parallelism
    if pb.WhichOneof("optional_private_threadpool_size") is not None:
      self.private_threadpool_size = pb.private_threadpool_size


@tf_export("data.Options")
class Options(options_lib.OptionsBase):
  """Represents options for `tf.data.Dataset`.

  A `tf.data.Options` object can be, for instance, used to control which static
  optimizations to apply to the input pipeline graph or whether to use
  performance modeling to dynamically tune the parallelism of operations such as
  `tf.data.Dataset.map` or `tf.data.Dataset.interleave`.

  The options are set for the entire dataset and are carried over to datasets
  created through tf.data transformations.

  The options can be set by constructing an `Options` object and using the
  `tf.data.Dataset.with_options(options)` transformation, which returns a
  dataset with the options set.

  >>> dataset = tf.data.Dataset.range(42)
  >>> options = tf.data.Options()
  >>> options.deterministic = False
  >>> dataset = dataset.with_options(options)
  >>> print(dataset.options().deterministic)
  False

  Note: A known limitation of the `tf.data.Options` implementation is that the
  options are not preserved across tf.function boundaries. In particular, to
  set options for a dataset that is iterated within a tf.function, the options
  need to be set within the same tf.function.
  """

  autotune = options_lib.create_option(
      name="autotune",
      ty=AutotuneOptions,
      docstring="The autotuning options associated with the dataset. See "
      "`tf.data.experimental.AutotuneOptions` for more details.",
      default_factory=AutotuneOptions)

  deterministic = options_lib.create_option(
      name="deterministic",
      ty=bool,
      docstring=
      "Whether the outputs need to be produced in deterministic order. If None,"
      " defaults to True.")

  experimental_deterministic = options_lib.create_option(
      name="experimental_deterministic",
      ty=bool,
      docstring="DEPRECATED. Use `deterministic` instead.")

  experimental_distribute = options_lib.create_option(
      name="experimental_distribute",
      ty=DistributeOptions,
      docstring=
      "The distribution strategy options associated with the dataset. See "
      "`tf.data.experimental.DistributeOptions` for more details.",
      default_factory=DistributeOptions)

  experimental_external_state_policy = options_lib.create_option(
      name="experimental_external_state_policy",
      ty=ExternalStatePolicy,
      docstring="This option can be used to override the default policy for "
      "how to handle external state when serializing a dataset or "
      "checkpointing its iterator. There are three settings available - "
      "IGNORE: External state is ignored without a warning; WARN: External "
      "state is ignored and a warning is logged; FAIL: External state results "
      "in an error.")

  experimental_optimization = options_lib.create_option(
      name="experimental_optimization",
      ty=OptimizationOptions,
      docstring=
      "The optimization options associated with the dataset. See "
      "`tf.data.experimental.OptimizationOptions` for more details.",
      default_factory=OptimizationOptions)

  experimental_slack = options_lib.create_option(
      name="experimental_slack",
      ty=bool,
      docstring="Whether to introduce 'slack' in the last `prefetch` of the "
      "input pipeline, if it exists. This may reduce CPU contention with "
      "accelerator host-side activity at the start of a step. The slack "
      "frequency is determined by the number of devices attached to this "
      "input pipeline. If None, defaults to False.")

  experimental_symbolic_checkpoint = options_lib.create_option(
      name="experimental_symbolic_checkpoint",
      ty=bool,
      docstring="Whether to checkpoint internal input pipeline state "
      "maintaining cursors into data sources that identify last "
      "element(s) produced as output to the tf.data consumer. This "
      "is alternative to the default 'explicit' checkpointing which "
      "stores the internal input pipeline state in the checkpoint. "
      "Note that symbolic checkpointing is not supported for "
      "transformations that can reorder elements.")

  experimental_threading = options_lib.create_option(
      name="experimental_threading",
      ty=ThreadingOptions,
      docstring="DEPRECATED. Use `threading` instead.")

  threading = options_lib.create_option(
      name="threading",
      ty=ThreadingOptions,
      docstring="The threading options associated with the dataset. See "
      "`tf.data.ThreadingOptions` for more details.",
      default_factory=ThreadingOptions)

  def __getattribute__(self, name):
    if name == "experimental_threading":
      logging.warning("options.experimental_threading is deprecated. "
                      "Use options.threading instead.")
      return getattr(self, "threading")
    if name == "experimental_deterministic":
      # TODO(aaudibert): Uncomment after internal uses have been updated.
      # logging.warning("options.experimental_deterministic is deprecated. "
      #                 "Use options.deterministic instead.")
      return getattr(self, "deterministic")
    return super(Options, self).__getattribute__(name)

  def __setattr__(self, name, value):
    if name == "experimental_threading":
      logging.warning("options.experimental_threading is deprecated. "
                      "Use options.threading instead.")
      super(Options, self).__setattr__("threading", value)
      return
    if name == "experimental_deterministic":
      # TODO(aaudibert): Uncomment after internal uses have been updated.
      # logging.warning("options.experimental_deterministic is deprecated. "
      #                 "Use options.deterministic instead.")
      super(Options, self).__setattr__("deterministic", value)
      return
    super(Options, self).__setattr__(name, value)

  def _to_proto(self):
    pb = dataset_options_pb2.Options()
    if self.deterministic is not None:
      pb.deterministic = self.deterministic
    pb.autotune_options.CopyFrom(self.autotune._to_proto())  # pylint: disable=protected-access
    pb.distribute_options.CopyFrom(self.experimental_distribute._to_proto())  # pylint: disable=protected-access
    if self.experimental_external_state_policy is not None:
      pb.external_state_policy = (
          ExternalStatePolicy._to_proto(  # pylint: disable=protected-access
              self.experimental_external_state_policy))
    pb.optimization_options.CopyFrom(self.experimental_optimization._to_proto())  # pylint: disable=protected-access
    if self.experimental_slack is not None:
      pb.slack = self.experimental_slack
    if self.experimental_symbolic_checkpoint is not None:
      pb.symbolic_checkpoint = self.experimental_symbolic_checkpoint
    pb.threading_options.CopyFrom(self.threading._to_proto())  # pylint: disable=protected-access
    return pb

  def _from_proto(self, pb):
    if pb.WhichOneof("optional_deterministic") is not None:
      self.deterministic = pb.deterministic
    self.autotune._from_proto(pb.autotune_options)  # pylint: disable=protected-access
    self.experimental_distribute._from_proto(pb.distribute_options)  # pylint: disable=protected-access
    if pb.WhichOneof("optional_external_state_policy") is not None:
      self.experimental_external_state_policy = (
          ExternalStatePolicy._from_proto(  # pylint: disable=protected-access
              pb.external_state_policy))
    self.experimental_optimization._from_proto(pb.optimization_options)  # pylint: disable=protected-access
    if pb.WhichOneof("optional_slack") is not None:
      self.experimental_slack = pb.slack
    if pb.WhichOneof("optional_symbolic_checkpoint") is not None:
      self.experimental_symbolic_checkpoint = pb.symbolic_checkpoint
    self.threading._from_proto(pb.threading_options)  # pylint: disable=protected-access

  def _set_mutable(self, mutable):
    """Change the mutability value to `mutable` on this options and children."""
    # pylint: disable=protected-access
    object.__setattr__(self, "_mutable", mutable)
    self.autotune._set_mutable(mutable)
    self.experimental_distribute._set_mutable(mutable)
    self.experimental_optimization._set_mutable(mutable)
    self.threading._set_mutable(mutable)

  def merge(self, options):
    """Merges itself with the given `tf.data.Options`.

    If this object and the `options` to merge set an option differently, a
    warning is generated and this object's value is updated with the `options`
    object's value.

    Args:
      options: The `tf.data.Options` to merge with.

    Returns:
      New `tf.data.Options` object which is the result of merging self with
      the input `tf.data.Options`.
    """
    return options_lib.merge_options(self, options)