text: # Copyright 2016 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.
  # ==============================================================================
  
  """Base Estimator class."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import copy
  import os
  import tempfile
  
  import numpy as np
  import six
  
  from google.protobuf import message
  from tensorflow.core.framework import summary_pb2
  from tensorflow.python.client import session as tf_session
  from tensorflow.python.eager import context
  from tensorflow.python.estimator import model_fn as model_fn_lib
  from tensorflow.python.estimator import run_config
  from tensorflow.python.estimator import util as estimator_util
  from tensorflow.python.estimator.export import export as export_helpers
  from tensorflow.python.estimator.export import export_output
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import metrics as metrics_lib
  from tensorflow.python.ops import resources
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import gfile
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.saved_model import builder as saved_model_builder
  from tensorflow.python.saved_model import utils_impl as saved_model_utils
  from tensorflow.python.summary import summary
  from tensorflow.python.summary.writer import writer_cache
  from tensorflow.python.training import basic_session_run_hooks
  from tensorflow.python.training import checkpoint_management
  from tensorflow.python.training import device_setter
  from tensorflow.python.training import distribute as distribute_lib
  from tensorflow.python.training import evaluation
  from tensorflow.python.training import monitored_session
  from tensorflow.python.training import saver
  from tensorflow.python.training import training
  from tensorflow.python.training import training_util
  from tensorflow.python.training import warm_starting_util
  from tensorflow.python.util import compat
  from tensorflow.python.util import compat_internal
  from tensorflow.python.util import function_utils
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import estimator_export
  
  
  _VALID_MODEL_FN_ARGS = set(
      ['features', 'labels', 'mode', 'params', 'self', 'config'])
  
  
  @estimator_export('estimator.Estimator')
  class Estimator(object):
    """Estimator class to train and evaluate TensorFlow models.
  
    The `Estimator` object wraps a model which is specified by a `model_fn`,
    which, given inputs and a number of other parameters, returns the ops
    necessary to perform training, evaluation, or predictions.
  
    All outputs (checkpoints, event files, etc.) are written to `model_dir`, or a
    subdirectory thereof. If `model_dir` is not set, a temporary directory is
    used.
  
    The `config` argument can be passed `tf.estimator.RunConfig` object containing
    information about the execution environment. It is passed on to the
    `model_fn`, if the `model_fn` has a parameter named "config" (and input
    functions in the same manner). If the `config` parameter is not passed, it is
    instantiated by the `Estimator`. Not passing config means that defaults useful
    for local execution are used. `Estimator` makes config available to the model
    (for instance, to allow specialization based on the number of workers
    available), and also uses some of its fields to control internals, especially
    regarding checkpointing.
  
    The `params` argument contains hyperparameters. It is passed to the
    `model_fn`, if the `model_fn` has a parameter named "params", and to the input
    functions in the same manner. `Estimator` only passes params along, it does
    not inspect it. The structure of `params` is therefore entirely up to the
    developer.
  
    None of `Estimator`'s methods can be overridden in subclasses (its
    constructor enforces this). Subclasses should use `model_fn` to configure
    the base class, and may add methods implementing specialized functionality.
  
    @compatibility(eager)
    Calling methods of `Estimator` will work while eager execution is enabled.
    However, the `model_fn` and `input_fn` is not executed eagerly, `Estimator`
    will switch to graph model before calling all user-provided functions (incl.
    hooks), so their code has to be compatible with graph mode execution. Note
    that `input_fn` code using `tf.data` generally works in both graph and eager
    modes.
    @end_compatibility
    """
  
    def __init__(self, model_fn, model_dir=None, config=None, params=None,
                 warm_start_from=None):
      """Constructs an `Estimator` instance.
  
      See @{$estimators} for more information. To warm-start an `Estimator`:
  
      ```python
      estimator = tf.estimator.DNNClassifier(
          feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
          hidden_units=[1024, 512, 256],
          warm_start_from="/path/to/checkpoint/dir")
      ```
  
      For more details on warm-start configuration, see
      `tf.estimator.WarmStartSettings`.
  
      Args:
        model_fn: Model function. Follows the signature:
  
          * Args:
  
            * `features`: This is the first item returned from the `input_fn`
                   passed to `train`, `evaluate`, and `predict`. This should be a
                   single `tf.Tensor` or `dict` of same.
            * `labels`: This is the second item returned from the `input_fn`
                   passed to `train`, `evaluate`, and `predict`. This should be a
                   single `tf.Tensor` or `dict` of same (for multi-head models).
                   If mode is @{tf.estimator.ModeKeys.PREDICT}, `labels=None` will
                   be passed. If the `model_fn`'s signature does not accept
                   `mode`, the `model_fn` must still be able to handle
                   `labels=None`.
            * `mode`: Optional. Specifies if this training, evaluation or
                   prediction. See `tf.estimator.ModeKeys`.
            * `params`: Optional `dict` of hyperparameters.  Will receive what
                   is passed to Estimator in `params` parameter. This allows
                   to configure Estimators from hyper parameter tuning.
            * `config`: Optional configuration object. Will receive what is passed
                   to Estimator in `config` parameter, or the default `config`.
                   Allows updating things in your `model_fn` based on
                   configuration such as `num_ps_replicas`, or `model_dir`.
  
          * Returns:
            `tf.estimator.EstimatorSpec`
  
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into an estimator to
          continue training a previously saved model. If `PathLike` object, the
          path will be resolved. If `None`, the model_dir in `config` will be used
          if set. If both are set, they must be same. If both are `None`, a
          temporary directory will be used.
        config: Configuration object.
        params: `dict` of hyper parameters that will be passed into `model_fn`.
                Keys are names of parameters, values are basic python types.
        warm_start_from: Optional string filepath to a checkpoint or SavedModel to
                         warm-start from, or a `tf.estimator.WarmStartSettings`
                         object to fully configure warm-starting.  If the string
                         filepath is provided instead of a
                         `tf.estimator.WarmStartSettings`, then all variables are
                         warm-started, and it is assumed that vocabularies
                         and `tf.Tensor` names are unchanged.
  
      Raises:
        ValueError: parameters of `model_fn` don't match `params`.
        ValueError: if this is called via a subclass and if that class overrides
          a member of `Estimator`.
      """
      Estimator._assert_members_are_not_overridden(self)
  
      config = maybe_overwrite_model_dir_and_session_config(config, model_dir)
      self._config = config
  
      # The distribute field contains an instance of DistributionStrategy.
      self._train_distribution = self._config.train_distribute
      self._eval_distribution = self._config.eval_distribute
      # Model directory.
      self._model_dir = self._config.model_dir
      self._session_config = self._config.session_config
      logging.info('Using config: %s', str(vars(self._config)))
  
      self._device_fn = (
          self._config.device_fn or _get_replica_device_setter(self._config))
  
      if model_fn is None:
        raise ValueError('model_fn must be provided to Estimator.')
      _verify_model_fn_args(model_fn, params)
      self._model_fn = model_fn
      self._params = copy.deepcopy(params or {})
  
      # pylint: disable=protected-access
      self._warm_start_settings = _get_default_warm_start_settings(
          warm_start_from)
      # pylint: enable=protected-access
  
    @property
    def model_dir(self):
      return self._model_dir
  
    @property
    def config(self):
      return copy.deepcopy(self._config)
  
    @property
    def params(self):
      return copy.deepcopy(self._params)
  
    @property
    def model_fn(self):
      """Returns the `model_fn` which is bound to `self.params`.
  
      Returns:
        The `model_fn` with following signature:
          `def model_fn(features, labels, mode, config)`
      """
  
      def public_model_fn(features, labels, mode, config):
        return self._call_model_fn(features, labels, mode, config)
  
      return public_model_fn
  
    # TODO(ispir): support a list of names
    def get_variable_value(self, name):
      """Returns value of the variable given by name.
  
      Args:
        name: string or a list of string, name of the tensor.
  
      Returns:
        Numpy array - value of the tensor.
  
      Raises:
        ValueError: If the `Estimator` has not produced a checkpoint yet.
      """
      _check_checkpoint_available(self.model_dir)
      with context.graph_mode():
        return training.load_variable(self.model_dir, name)
  
    def get_variable_names(self):
      """Returns list of all variable names in this model.
  
      Returns:
        List of names.
  
      Raises:
        ValueError: If the `Estimator` has not produced a checkpoint yet.
      """
      _check_checkpoint_available(self.model_dir)
      with context.graph_mode():
        return [name for name, _ in training.list_variables(self.model_dir)]
  
    def latest_checkpoint(self):
      """Finds the filename of the latest saved checkpoint file in `model_dir`.
  
      Returns:
        The full path to the latest checkpoint or `None` if no checkpoint was
        found.
      """
      with context.graph_mode():
        return checkpoint_management.latest_checkpoint(self.model_dir)
  
    def train(self,
              input_fn,
              hooks=None,
              steps=None,
              max_steps=None,
              saving_listeners=None):
      """Trains a model given training data `input_fn`.
  
      Args:
        input_fn: A function that provides input data for training as minibatches.
          See @{$premade_estimators#create_input_functions} for more information.
          The function should construct and return one of the following:  * A
          `tf.data.Dataset` object: Outputs of `Dataset` object must be a tuple
          `(features, labels)` with same constraints as below. * A tuple
          `(features, labels)`: Where `features` is a `tf.Tensor` or a dictionary
          of string feature name to `Tensor` and `labels` is a `Tensor` or a
          dictionary of string label name to `Tensor`. Both `features` and
          `labels` are consumed by `model_fn`. They should satisfy the expectation
          of `model_fn` from inputs.
        hooks: List of `tf.train.SessionRunHook` subclass instances. Used for
          callbacks inside the training loop.
        steps: Number of steps for which to train the model. If `None`, train
          forever or train until `input_fn` generates the `tf.errors.OutOfRange`
          error or `StopIteration` exception. `steps` works incrementally. If you
          call two times `train(steps=10)` then training occurs in total 20 steps.
          If `OutOfRange` or `StopIteration` occurs in the middle, training stops
          before 20 steps. If you don't want to have incremental behavior please
          set `max_steps` instead. If set, `max_steps` must be `None`.
        max_steps: Number of total steps for which to train model. If `None`,
          train forever or train until `input_fn` generates the
          `tf.errors.OutOfRange` error or `StopIteration` exception. If set,
          `steps` must be `None`. If `OutOfRange` or `StopIteration` occurs in the
          middle, training stops before `max_steps` steps. Two calls to
          `train(steps=100)` means 200 training iterations. On the other hand, two
          calls to `train(max_steps=100)` means that the second call will not do
          any iteration since first call did all 100 steps.
        saving_listeners: list of `CheckpointSaverListener` objects. Used for
          callbacks that run immediately before or after checkpoint savings.
  
      Returns:
        `self`, for chaining.
  
      Raises:
        ValueError: If both `steps` and `max_steps` are not `None`.
        ValueError: If either `steps` or `max_steps <= 0`.
      """
      with context.graph_mode():
        if (steps is not None) and (max_steps is not None):
          raise ValueError('Can not provide both steps and max_steps.')
        if steps is not None and steps <= 0:
          raise ValueError('Must specify steps > 0, given: {}'.format(steps))
        if max_steps is not None and max_steps <= 0:
          raise ValueError(
              'Must specify max_steps > 0, given: {}'.format(max_steps))
  
        if max_steps is not None:
          start_step = _load_global_step_from_checkpoint_dir(self._model_dir)
          if max_steps <= start_step:
            logging.info('Skipping training since max_steps has already saved.')
            return self
  
        hooks = _check_hooks_type(hooks)
        hooks.extend(self._convert_train_steps_to_hooks(steps, max_steps))
  
        saving_listeners = _check_listeners_type(saving_listeners)
        loss = self._train_model(input_fn, hooks, saving_listeners)
        logging.info('Loss for final step: %s.', loss)
        return self
  
    def _convert_train_steps_to_hooks(self, steps, max_steps):
      """Create hooks to run correct number of steps in training.
  
      Args:
        steps: number of steps to run during training.
        max_steps: maximum number of steps to be run during training. It'll be
          the maximum number of steps the model will train to after restoring
          from checkpoint even across multiple estimator.train calls.
  
      Returns:
        List of hooks to be passed to the estimator.
      """
      if steps is not None or max_steps is not None:
        if self._train_distribution:
          steps_per_run = getattr(self._train_distribution, 'steps_per_run', 1)
          if steps_per_run > 1:
            return [basic_session_run_hooks._MultiStepStopAtStepHook(  # pylint: disable=protected-access
                steps, max_steps, steps_per_run)]
        return [training.StopAtStepHook(steps, max_steps)]
      else:
        return []
  
    def eval_dir(self, name=None):
      """Shows the directory name where evaluation metrics are dumped.
  
      Args:
        name: Name of the evaluation if user needs to run multiple evaluations on
          different data sets, such as on training data vs test data. Metrics for
          different evaluations are saved in separate folders, and appear
          separately in tensorboard.
  
      Returns:
        A string which is the path of directory contains evaluation metrics.
      """
      return os.path.join(self._model_dir, 'eval' if not name else
                          'eval_' + name)
  
    def evaluate(self, input_fn, steps=None, hooks=None, checkpoint_path=None,
                 name=None):
      """Evaluates the model given evaluation data `input_fn`.
  
      For each step, calls `input_fn`, which returns one batch of data.
      Evaluates until:
      - `steps` batches are processed, or
      - `input_fn` raises an end-of-input exception (`tf.errors.OutOfRangeError`
      or
      `StopIteration`).
  
      Args:
        input_fn: A function that constructs the input data for evaluation. See
          @{$premade_estimators#create_input_functions} for more information. The
          function should construct and return one of the following:  * A
          `tf.data.Dataset` object: Outputs of `Dataset` object must be a tuple
          `(features, labels)` with same constraints as below. * A tuple
          `(features, labels)`: Where `features` is a `tf.Tensor` or a dictionary
          of string feature name to `Tensor` and `labels` is a `Tensor` or a
          dictionary of string label name to `Tensor`. Both `features` and
          `labels` are consumed by `model_fn`. They should satisfy the expectation
          of `model_fn` from inputs.
        steps: Number of steps for which to evaluate model. If `None`, evaluates
          until `input_fn` raises an end-of-input exception.
        hooks: List of `tf.train.SessionRunHook` subclass instances. Used for
          callbacks inside the evaluation call.
        checkpoint_path: Path of a specific checkpoint to evaluate. If `None`, the
          latest checkpoint in `model_dir` is used.  If there are no checkpoints
          in `model_dir`, evaluation is run with newly initialized `Variables`
          instead of ones restored from checkpoint.
        name: Name of the evaluation if user needs to run multiple evaluations on
          different data sets, such as on training data vs test data. Metrics for
          different evaluations are saved in separate folders, and appear
          separately in tensorboard.
  
      Returns:
        A dict containing the evaluation metrics specified in `model_fn` keyed by
        name, as well as an entry `global_step` which contains the value of the
        global step for which this evaluation was performed.
  
      Raises:
        ValueError: If `steps <= 0`.
        ValueError: If no model has been trained, namely `model_dir`, or the
          given `checkpoint_path` is empty.
      """
      with context.graph_mode():
        hooks = _check_hooks_type(hooks)
        hooks.extend(self._convert_eval_steps_to_hooks(steps))
  
        # Check that model has been trained (if nothing has been set explicitly).
        if not checkpoint_path:
          latest_path = checkpoint_management.latest_checkpoint(self._model_dir)
          if not latest_path:
            logging.info('Could not find trained model in model_dir: {}, running '
                         'initialization to evaluate.'.format(self._model_dir))
          checkpoint_path = latest_path
  
        def _evaluate():
          (scaffold, update_op, eval_dict, all_hooks) = (
              self._evaluate_build_graph(input_fn, hooks, checkpoint_path))
          return self._evaluate_run(
              checkpoint_path=checkpoint_path,
              scaffold=scaffold,
              update_op=update_op,
              eval_dict=eval_dict,
              all_hooks=all_hooks,
              output_dir=self.eval_dir(name))
  
        with ops.Graph().as_default():
          # TODO(priyag): Support distributed eval on TPUs.
          if (self._eval_distribution
              and self._eval_distribution.__class__.__name__ != 'TPUStrategy'):
            with self._eval_distribution.scope():
              return _evaluate()
          else:
            return _evaluate()
  
    def _convert_eval_steps_to_hooks(self, steps):
      if steps is None:
        return []
  
      if steps <= 0:
        raise ValueError('Must specify steps > 0, given: {}'.format(steps))
      return [evaluation._StopAfterNEvalsHook(num_evals=steps)]  # pylint: disable=protected-access
  
    def predict(self,
                input_fn,
                predict_keys=None,
                hooks=None,
                checkpoint_path=None,
                yield_single_examples=True):
      """Yields predictions for given features.
  
      Args:
        input_fn: A function that constructs the features. Prediction continues
          until `input_fn` raises an end-of-input exception
          (`tf.errors.OutOfRangeError` or `StopIteration`).
          See @{$premade_estimators#create_input_functions} for more
          information. The function should construct and return one of
          the following:
  
            * A `tf.data.Dataset` object: Outputs of `Dataset` object must have
              same constraints as below.
            * features: A `tf.Tensor` or a dictionary of string feature name to
              `Tensor`. features are consumed by `model_fn`. They should satisfy
              the expectation of `model_fn` from inputs.
            * A tuple, in which case the first item is extracted as features.
  
        predict_keys: list of `str`, name of the keys to predict. It is used if
          the `tf.estimator.EstimatorSpec.predictions` is a `dict`. If
          `predict_keys` is used then rest of the predictions will be filtered
          from the dictionary. If `None`, returns all.
        hooks: List of `tf.train.SessionRunHook` subclass instances. Used for
          callbacks inside the prediction call.
        checkpoint_path: Path of a specific checkpoint to predict. If `None`, the
          latest checkpoint in `model_dir` is used.  If there are no checkpoints
          in `model_dir`, prediction is run with newly initialized `Variables`
          instead of ones restored from checkpoint.
        yield_single_examples: If `False`, yields the whole batch as returned by
          the `model_fn` instead of decomposing the batch into individual
          elements. This is useful if `model_fn` returns some tensors whose first
          dimension is not equal to the batch size.
  
      Yields:
        Evaluated values of `predictions` tensors.
  
      Raises:
        ValueError: Could not find a trained model in `model_dir`.
        ValueError: If batch length of predictions is not the same and
          `yield_single_examples` is `True`.
        ValueError: If there is a conflict between `predict_keys` and
          `predictions`. For example if `predict_keys` is not `None` but
          `tf.estimator.EstimatorSpec.predictions` is not a `dict`.
      """
      with context.graph_mode():
        hooks = _check_hooks_type(hooks)
        # Check that model has been trained.
        if not checkpoint_path:
          checkpoint_path = checkpoint_management.latest_checkpoint(
              self._model_dir)
        if not checkpoint_path:
          logging.info('Could not find trained model in model_dir: {}, running '
                       'initialization to predict.'.format(self._model_dir))
        with ops.Graph().as_default() as g:
          random_seed.set_random_seed(self._config.tf_random_seed)
          self._create_and_assert_global_step(g)
          features, input_hooks = self._get_features_from_input_fn(
              input_fn, model_fn_lib.ModeKeys.PREDICT)
          estimator_spec = self._call_model_fn(
              features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
  
          # Call to warm_start has to be after model_fn is called.
          self._maybe_warm_start(checkpoint_path)
  
          predictions = self._extract_keys(
              estimator_spec.predictions, predict_keys)
          all_hooks = list(input_hooks)
          all_hooks.extend(hooks)
          all_hooks.extend(list(estimator_spec.prediction_hooks or []))
          with training.MonitoredSession(
              session_creator=training.ChiefSessionCreator(
                  checkpoint_filename_with_path=checkpoint_path,
                  master=self._config.master,
                  scaffold=estimator_spec.scaffold,
                  config=self._session_config),
              hooks=all_hooks) as mon_sess:
            while not mon_sess.should_stop():
              preds_evaluated = mon_sess.run(predictions)
              if not yield_single_examples:
                yield preds_evaluated
              elif not isinstance(predictions, dict):
                for pred in preds_evaluated:
                  yield pred
              else:
                for i in range(self._extract_batch_length(preds_evaluated)):
                  yield {
                      key: value[i]
                      for key, value in six.iteritems(preds_evaluated)
                  }
  
    def _assert_members_are_not_overridden(self):
      """Asserts members of `Estimator` are not overridden."""
      # TPUEstimator is special cased (owned by TF).
      if self.__class__.__name__ == 'TPUEstimator':
        return
  
      allowed_overrides = set([
          '_create_and_assert_global_step',
          '_tf_api_names', '_tf_api_names_v1', '_estimator_api_names',
          '_estimator_api_names_v1', '_estimator_api_constants',
          '_estimator_api_constants_v1',
      ])
      estimator_members = set([m for m in Estimator.__dict__.keys()
                               if not m.startswith('__')])
      subclass_members = set(self.__class__.__dict__.keys())
      common_members = estimator_members & subclass_members - allowed_overrides
      overridden_members = [
          m for m in common_members
          if Estimator.__dict__[m] != self.__class__.__dict__[m]]
      if overridden_members:
        raise ValueError(
            'Subclasses of Estimator cannot override members of Estimator. '
            '{} does override {}'.format(self.__class__, overridden_members))
  
    def export_savedmodel(
        self, export_dir_base, serving_input_receiver_fn,
        assets_extra=None,
        as_text=False,
        checkpoint_path=None,
        strip_default_attrs=False):
      # pylint: disable=line-too-long
      """Exports inference graph as a `SavedModel` into the given dir.
  
      For a detailed guide, see
      @{$saved_model#using_savedmodel_with_estimators$Using SavedModel with
      Estimators}.
  
      This method builds a new graph by first calling the
      `serving_input_receiver_fn` to obtain feature `Tensor`s, and then calling
      this `Estimator`'s `model_fn` to generate the model graph based on those
      features. It restores the given checkpoint (or, lacking that, the most
      recent checkpoint) into this graph in a fresh session.  Finally it creates
      a timestamped export directory below the given `export_dir_base`, and writes
      a `SavedModel` into it containing a single `tf.MetaGraphDef` saved from this
      session.
  
      The exported `MetaGraphDef` will provide one `SignatureDef` for each
      element of the `export_outputs` dict returned from the `model_fn`, named
      using
      the same keys.  One of these keys is always
      `tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`,
      indicating which
      signature will be served when a serving request does not specify one.
      For each signature, the outputs are provided by the corresponding
      `tf.estimator.export.ExportOutput`s, and the inputs are always the input
      receivers provided by
      the `serving_input_receiver_fn`.
  
      Extra assets may be written into the `SavedModel` via the `assets_extra`
      argument.  This should be a dict, where each key gives a destination path
      (including the filename) relative to the assets.extra directory.  The
      corresponding value gives the full path of the source file to be copied.
      For example, the simple case of copying a single file without renaming it
      is specified as `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
  
      Args:
        export_dir_base: A string containing a directory in which to create
          timestamped subdirectories containing exported `SavedModel`s.
        serving_input_receiver_fn: A function that takes no argument and returns a
          `tf.estimator.export.ServingInputReceiver` or
          `tf.estimator.export.TensorServingInputReceiver`.
        assets_extra: A dict specifying how to populate the assets.extra directory
          within the exported `SavedModel`, or `None` if no extra assets are
          needed.
        as_text: whether to write the `SavedModel` proto in text format.
        checkpoint_path: The checkpoint path to export.  If `None` (the default),
          the most recent checkpoint found within the model directory is chosen.
        strip_default_attrs: Boolean. If `True`, default-valued attributes will be
          removed from the `NodeDef`s. 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).
  
      Returns:
        The string path to the exported directory.
  
      Raises:
        ValueError: if no `serving_input_receiver_fn` is provided, no
        `export_outputs`
            are provided, or no checkpoint can be found.
      """
      # pylint: enable=line-too-long
      return self._export_saved_model_for_mode(
          export_dir_base,
          serving_input_receiver_fn,
          assets_extra=assets_extra,
          as_text=as_text,
          checkpoint_path=checkpoint_path,
          strip_default_attrs=strip_default_attrs,
          mode=model_fn_lib.ModeKeys.PREDICT)
  
    def _export_saved_model_for_mode(
        self, export_dir_base, input_receiver_fn,
        assets_extra=None,
        as_text=False,
        checkpoint_path=None,
        strip_default_attrs=False,
        mode=model_fn_lib.ModeKeys.PREDICT):
      # pylint: disable=line-too-long
      """Exports a single train/eval/predict graph as a `SavedModel`.
  
      This method is a wrapper for `_export_all_saved_models`, and wraps a raw
      `input_receiver_fn` in a dictionary to pass in to that function.
      See `_export_all_saved_models` for full docs.
  
      See `tf.contrib.estimator.export_saved_model_for_mode` for the currently
      exposed version of this function.
  
      Args:
        export_dir_base: A string containing a directory in which to create
          timestamped subdirectories containing exported `SavedModel`s.
        input_receiver_fn: a function that takes no argument and returns the
          appropriate subclass of `InputReceiver`.
        assets_extra: A dict specifying how to populate the assets.extra directory
          within the exported `SavedModel`, or `None` if no extra assets are
          needed.
        as_text: whether to write the `SavedModel` proto in text format.
        checkpoint_path: The checkpoint path to export.  If `None` (the default),
          the most recent checkpoint found within the model directory is chosen.
        strip_default_attrs: Boolean. If `True`, default-valued attributes will be
          removed from the `NodeDef`s. 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).
        mode: `tf.estimator.ModeKeys` value indicating with mode will be exported.
  
      Returns:
        The string path to the exported directory.
  
      Raises:
        ValueError: if `input_receiver_fn` is `None`, no `export_outputs`
          are provided, or no checkpoint can be found.
      """
      # pylint: enable=line-too-long
      if not input_receiver_fn:
        raise ValueError('An input_receiver_fn must be defined.')
  
      input_receiver_fn_map = {mode: input_receiver_fn}
  
      return self._export_all_saved_models(
          export_dir_base,
          input_receiver_fn_map,
          assets_extra=assets_extra,
          as_text=as_text,
          checkpoint_path=checkpoint_path,
          strip_default_attrs=strip_default_attrs)
  
    def _export_all_saved_models(
        self, export_dir_base, input_receiver_fn_map,
        assets_extra=None,
        as_text=False,
        checkpoint_path=None,
        strip_default_attrs=False):
      # pylint: disable=line-too-long
      """Exports a `SavedModel` containing `tf.MetaGraphDefs` for each requested mode.
  
      See `tf.contrib.estimator.export_all_saved_models` for the currently
      exposed version of this function.
  
      For each mode passed in via the `input_receiver_fn_map`,
      this method builds a new graph by calling the `input_receiver_fn` to obtain
      feature and label `Tensor`s. Next, this method calls the `Estimator`'s
      `model_fn` in the passed mode to generate the model graph based on
      those features and labels, and restores the given checkpoint
      (or, lacking that, the most recent checkpoint) into the graph.
      Only one of the modes is used for saving variables to the `SavedModel`
      (order of preference: @{tf.estimator.ModeKeys#TRAIN$TRAIN},
      @{tf.estimator.ModeKeys#EVAL$EVAL}, then
      @{tf.estimator.ModeKeys#PREDICT$PREDICT}), such that up to three
      `tf.MetaGraphDefs` are saved with a single set of variables in a single
      `SavedModel` directory.
  
      For the variables and `tf.MetaGraphDefs`, a timestamped export directory
      below
      `export_dir_base`, and writes a `SavedModel` into it containing
      the `tf.MetaGraphDef` for the given mode and its associated signatures.
  
      For prediction, the exported `MetaGraphDef` will provide one `SignatureDef`
      for each element of the `export_outputs` dict returned from the `model_fn`,
      named using the same keys.  One of these keys is always
      `tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`,
      indicating which
      signature will be served when a serving request does not specify one.
      For each signature, the outputs are provided by the corresponding
      `tf.estimator.export.ExportOutput`s, and the inputs are always the input
      receivers provided by
      the `serving_input_receiver_fn`.
  
      For training and evaluation, the `train_op` is stored in an extra
      collection,
      and loss, metrics, and predictions are included in a `SignatureDef` for the
      mode in question.
  
      Extra assets may be written into the `SavedModel` via the `assets_extra`
      argument.  This should be a dict, where each key gives a destination path
      (including the filename) relative to the assets.extra directory.  The
      corresponding value gives the full path of the source file to be copied.
      For example, the simple case of copying a single file without renaming it
      is specified as `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
  
      Args:
        export_dir_base: A string containing a directory in which to create
          timestamped subdirectories containing exported `SavedModel`s.
        input_receiver_fn_map: dict of `tf.estimator.ModeKeys` to
          `input_receiver_fn` mappings, where the `input_receiver_fn` is a
          function that takes no arguments and returns the appropriate subclass of
          `InputReceiver`.
        assets_extra: A dict specifying how to populate the assets.extra directory
          within the exported `SavedModel`, or `None` if no extra assets are
          needed.
        as_text: whether to write the `SavedModel` proto in text format.
        checkpoint_path: The checkpoint path to export.  If `None` (the default),
          the most recent checkpoint found within the model directory is chosen.
        strip_default_attrs: Boolean. If `True`, default-valued attributes will be
          removed from the `NodeDef`s. 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).
  
      Returns:
        A dict of `tf.estimator.ModeKeys` value to string path for each exported
        directory.
  
      Raises:
        ValueError: if any `input_receiver_fn` is `None`, no `export_outputs`
          are provided, or no checkpoint can be found.
      """
      # pylint: enable=line-too-long
      # TODO(b/65561022): Consider allowing multiple input_receiver_fns per mode.
      with context.graph_mode():
        if not checkpoint_path:
          # Locate the latest checkpoint
          checkpoint_path = checkpoint_management.latest_checkpoint(
              self._model_dir)
        if not checkpoint_path:
          raise ValueError("Couldn't find trained model at %s." % self._model_dir)
  
        export_dir = export_helpers.get_timestamped_export_dir(export_dir_base)
        temp_export_dir = export_helpers.get_temp_export_dir(export_dir)
  
        builder = saved_model_builder.SavedModelBuilder(temp_export_dir)
  
        save_variables = True
        # Note that the order in which we run here matters, as the first
        # mode we pass through will be used to save the variables. We run TRAIN
        # first, as that is also the mode used for checkpoints, and therefore
        # we are not likely to have vars in PREDICT that are not in the checkpoint
        # created by TRAIN.
        if input_receiver_fn_map.get(model_fn_lib.ModeKeys.TRAIN):
          self._add_meta_graph_for_mode(
              builder, input_receiver_fn_map, checkpoint_path,
              strip_default_attrs, save_variables,
              mode=model_fn_lib.ModeKeys.TRAIN)
          save_variables = False
        if input_receiver_fn_map.get(model_fn_lib.ModeKeys.EVAL):
          self._add_meta_graph_for_mode(
              builder, input_receiver_fn_map, checkpoint_path,
              strip_default_attrs, save_variables,
              mode=model_fn_lib.ModeKeys.EVAL)
          save_variables = False
        if input_receiver_fn_map.get(model_fn_lib.ModeKeys.PREDICT):
          self._add_meta_graph_for_mode(
              builder, input_receiver_fn_map, checkpoint_path,
              strip_default_attrs, save_variables,
              mode=model_fn_lib.ModeKeys.PREDICT)
          save_variables = False
  
        if save_variables:
          raise ValueError('No valid modes for exporting found. Got {}.'.format(
              input_receiver_fn_map.keys()))
  
        builder.save(as_text)
  
        # Add the extra assets
        if assets_extra:
          assets_extra_path = os.path.join(compat.as_bytes(temp_export_dir),
                                           compat.as_bytes('assets.extra'))
          for dest_relative, source in assets_extra.items():
            dest_absolute = os.path.join(compat.as_bytes(assets_extra_path),
                                         compat.as_bytes(dest_relative))
            dest_path = os.path.dirname(dest_absolute)
            gfile.MakeDirs(dest_path)
            gfile.Copy(source, dest_absolute)
  
        gfile.Rename(temp_export_dir, export_dir)
        return export_dir
  
    def _add_meta_graph_for_mode(self,
                                 builder,
                                 input_receiver_fn_map,
                                 checkpoint_path,
                                 strip_default_attrs,
                                 save_variables=True,
                                 mode=model_fn_lib.ModeKeys.PREDICT,
                                 export_tags=None,
                                 check_variables=True):
      # pylint: disable=line-too-long
      """Loads variables and adds them along with a `tf.MetaGraphDef` for saving.
  
      Args:
        builder: instance of `tf.saved_modle.builder.SavedModelBuilder` that will
          be used for saving.
        input_receiver_fn_map: dict of `tf.estimator.ModeKeys` to
          `input_receiver_fn` mappings, where the `input_receiver_fn` is a
          function that takes no argument and returns the appropriate subclass of
          `InputReceiver`.
        checkpoint_path: The checkpoint path to export.  If `None` (the default),
          the most recent checkpoint found within the model directory is chosen.
        strip_default_attrs: Boolean. If `True`, default-valued attributes will be
          removed from the `NodeDef`s. 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).
        save_variables: bool, whether variables should be saved. If `False`, just
          the `tf.MetaGraphDef` will be saved. Note that `save_variables` should
          only be `True` for the first call to this function, and the
          `SavedModelBuilder` will raise an error if that is not the case.
        mode: `tf.estimator.ModeKeys` value indicating which mode will be
          exported.
        export_tags: The set of tags with which to save `tf.MetaGraphDef`. If
          `None`, a default set will be selected to matched the passed mode.
        check_variables: bool, whether to check the checkpoint has all variables.
  
      Raises:
        ValueError: if `save_variables` is `True` and `check_variable` is `False`.
      """
      # pylint: enable=line-too-long
      if export_tags is None:
        export_tags = model_fn_lib.EXPORT_TAG_MAP[mode]
      input_receiver_fn = input_receiver_fn_map[mode]
  
      with ops.Graph().as_default() as g:
        self._create_and_assert_global_step(g)
        random_seed.set_random_seed(self._config.tf_random_seed)
  
        input_receiver = input_receiver_fn()
  
        # Call the model_fn and collect the export_outputs.
        estimator_spec = self._call_model_fn(
            features=input_receiver.features,
            labels=getattr(input_receiver, 'labels', None),
            mode=mode,
            config=self.config)
  
        export_outputs = self._get_export_outputs_for_spec(estimator_spec)
  
        # Build the SignatureDefs from receivers and all outputs
        signature_def_map = export_helpers.build_all_signature_defs(
            input_receiver.receiver_tensors,
            export_outputs,
            getattr(input_receiver, 'receiver_tensors_alternatives', None),
            serving_only=(mode == model_fn_lib.ModeKeys.PREDICT))
  
        with tf_session.Session(config=self._session_config) as session:
  
          if estimator_spec.scaffold.local_init_op is not None:
            local_init_op = estimator_spec.scaffold.local_init_op
          else:
            local_init_op = monitored_session.Scaffold.default_local_init_op()
  
          # This saver will be used both for restoring variables now,
          # and in saving out the metagraph below. This ensures that any
          # Custom Savers stored with the Scaffold are passed through to the
          # SavedModel for restore later.
          graph_saver = estimator_spec.scaffold.saver or saver.Saver(sharded=True)
  
          if save_variables and not check_variables:
            raise ValueError('If `save_variables` is `True, `check_variables`'
                             'must not be `False`.')
          if check_variables:
            try:
              graph_saver.restore(session, checkpoint_path)
            except errors.NotFoundError as e:
              msg = ('Could not load all requested variables from checkpoint. '
                     'Please make sure your model_fn does not expect variables '
                     'that were not saved in the checkpoint.\n\n'
                     'Encountered error with mode `{}` while restoring '
                     'checkpoint from: `{}`. Full Traceback:\n\n{}').format(
                         mode, checkpoint_path, e)
              raise ValueError(msg)
  
          # We add the train op explicitly for now, so that we don't have to
          # change the Builder public interface. Note that this is a no-op
          # for prediction, where train_op is None.
          builder._add_train_op(estimator_spec.train_op)  # pylint: disable=protected-access
  
          meta_graph_kwargs = dict(
              tags=export_tags,
              signature_def_map=signature_def_map,
              assets_collection=ops.get_collection(
                  ops.GraphKeys.ASSET_FILEPATHS),
              strip_default_attrs=strip_default_attrs,
              legacy_init_op=local_init_op,
              saver=graph_saver)
  
          if save_variables:
            builder.add_meta_graph_and_variables(
                session, **meta_graph_kwargs)
          else:
            builder.add_meta_graph(**meta_graph_kwargs)
  
    def _get_export_outputs_for_spec(self, estimator_spec):
      """Given an `EstimatorSpec`, determine what our export outputs should be.
  
      `EstimatorSpecs` contains `export_outputs` that are used for serving, but
      for
      training and eval graphs, we must wrap the tensors of interest in
      appropriate `tf.estimator.export.ExportOutput` objects.
  
      Args:
        estimator_spec: `tf.estimator.EstimatorSpec` object that will be exported.
  
      Returns:
        a dict mapping `export_output_name` to `tf.estimator.export.ExportOutput`
        object.
  
      Raises:
        ValueError: if an appropriate `ExportOutput` cannot be found for the
          passed `EstimatorSpec.mode`
      """
      mode = estimator_spec.mode
      if mode == model_fn_lib.ModeKeys.PREDICT:
        outputs = estimator_spec.export_outputs
      else:
        if mode == model_fn_lib.ModeKeys.TRAIN:
          output_class = export_output.TrainOutput
        elif mode == model_fn_lib.ModeKeys.EVAL:
          output_class = export_output.EvalOutput
        else:
          raise ValueError(
              'Export output type not found for mode: {}'.format(mode))
  
        export_out = output_class(
            loss=estimator_spec.loss,
            predictions=estimator_spec.predictions,
            metrics=estimator_spec.eval_metric_ops)
        outputs = {mode: export_out}
  
      return outputs
  
    def _get_features_from_input_fn(self, input_fn, mode):
      """Extracts the `features` from return values of `input_fn`."""
      result = self._call_input_fn(input_fn, mode)
      result, _, hooks = estimator_util.parse_input_fn_result(result)
      self._validate_features_in_predict_input(result)
      return result, hooks
  
    def _validate_features_in_predict_input(self, result):
      if not _has_dataset_or_queue_runner(result):
        logging.warning('Input graph does not use tf.data.Dataset or contain a '
                        'QueueRunner. That means predict yields forever. '
                        'This is probably a mistake.')
  
    def _get_features_and_labels_from_input_fn(self, input_fn, mode,
                                               distribution=None):
      """Extracts the `features` and labels from return values of `input_fn`."""
      if distribution is not None:
        result = distribution.distribute_dataset(
            lambda: self._call_input_fn(input_fn, mode))
      else:
        result = self._call_input_fn(input_fn, mode)
  
      return estimator_util.parse_input_fn_result(result)
  
    def _extract_batch_length(self, preds_evaluated):
      """Extracts batch length of predictions."""
      batch_length = None
      for key, value in six.iteritems(preds_evaluated):
        batch_length = batch_length or value.shape[0]
        if value.shape[0] != batch_length:
          raise ValueError('Batch length of predictions should be same. %s has '
                           'different batch length than others.' % key)
      return batch_length
  
    def _extract_keys(self, predictions, predict_keys):
      """Extracts `predict_keys` from `predictions`."""
      if not predict_keys:
        return predictions
      if not isinstance(predictions, dict):
        raise ValueError(
            'predict_keys argument is not valid in case of non-dict predictions.')
      existing_keys = predictions.keys()
      predictions = {
          key: value
          for key, value in six.iteritems(predictions) if key in predict_keys
      }
      if not predictions:
        raise ValueError('Expected to run at least one output from %s, '
                         'provided %s.' % (existing_keys, predict_keys))
      return predictions
  
    def _create_global_step(self, graph):
      """Creates the global step tensor in graph.
  
      The global step tensor must be an integer type with name 'global_step' and
      be added to the collection @{tf.GraphKeys#GLOBAL_STEP$GLOBAL_STEP}.
  
      Args:
        graph: The graph in which to create the global step tensor.
  
      Returns:
        The global step `tf.Tensor`.
      """
      return training.create_global_step(graph)
  
    def _create_and_assert_global_step(self, graph):
      """Creates and asserts properties of the global step.
  
      Args:
        graph: The graph in which to create the global step tensor.
  
      Returns:
        The global step `tf.Tensor`.
      """
      step = self._create_global_step(graph)
      assert step == training.get_global_step()
      assert step.dtype.is_integer
      return step
  
    def _call_input_fn(self, input_fn, mode):
      """Calls the input function.
  
      Args:
        input_fn: The input function.
        mode: `tf.estimator.ModeKeys`
  
      Returns:
        The return value of the passed `input_fn`, which should be one of:
  
          * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a
              tuple `(features, labels)` with same constraints as below.
          * A tuple `(features, labels)`: Where `features` is a `Tensor` or a
            dictionary of string feature name to `Tensor` and `labels` is a
            `Tensor` or a dictionary of string label name to `Tensor`. Both
            `features` and `labels` are consumed by `model_fn`. They should
            satisfy the expectation of `model_fn` from inputs.
  
      Raises:
        ValueError: if `input_fn` takes invalid arguments.
      """
      input_fn_args = function_utils.fn_args(input_fn)
      kwargs = {}
      if 'mode' in input_fn_args:
        kwargs['mode'] = mode
      if 'params' in input_fn_args:
        kwargs['params'] = self.params
      if 'config' in input_fn_args:
        kwargs['config'] = self.config
      with ops.device('/cpu:0'):
        return input_fn(**kwargs)
  
    def _call_model_fn(self, features, labels, mode, config):
      """Calls model function.
  
      Args:
        features: features dict.
        labels: labels dict.
        mode: `tf.estimator.ModeKeys`
        config: `tf.estimator.RunConfig`
  
      Returns:
        An `tf.estimator.EstimatorSpec` object.
  
      Raises:
        ValueError: if `model_fn` returns invalid objects.
      """
      model_fn_args = function_utils.fn_args(self._model_fn)
      kwargs = {}
      if 'labels' in model_fn_args:
        kwargs['labels'] = labels
      else:
        if labels is not None:
          raise ValueError(
              'model_fn does not take labels, but input_fn returns labels.')
      if 'mode' in model_fn_args:
        kwargs['mode'] = mode
      if 'params' in model_fn_args:
        kwargs['params'] = self.params
      if 'config' in model_fn_args:
        kwargs['config'] = config
  
      logging.info('Calling model_fn.')
      model_fn_results = self._model_fn(features=features, **kwargs)
      logging.info('Done calling model_fn.')
  
      if not isinstance(model_fn_results, model_fn_lib.EstimatorSpec):
        raise ValueError('model_fn should return an EstimatorSpec.')
  
      return model_fn_results
  
    def _train_model(self, input_fn, hooks, saving_listeners):
      if self._train_distribution:
        return self._train_model_distributed(input_fn, hooks, saving_listeners)
      else:
        return self._train_model_default(input_fn, hooks, saving_listeners)
  
    def _train_model_default(self, input_fn, hooks, saving_listeners):
      """Initiate training with `input_fn`, without `DistributionStrategies`.
  
      Args:
        input_fn: A function that provides input data for training as minibatches.
        hooks: List of `tf.train.SessionRunHook` subclass instances. Used for
          callbacks inside the training loop.
        saving_listeners: list of `tf.train.CheckpointSaverListener` objects. Used
          for callbacks that run immediately before or after checkpoint savings.
  
      Returns:
        Loss from training
      """
      worker_hooks = []
      with ops.Graph().as_default() as g, g.device(self._device_fn):
        random_seed.set_random_seed(self._config.tf_random_seed)
        global_step_tensor = self._create_and_assert_global_step(g)
  
        # Skip creating a read variable if _create_and_assert_global_step
        # returns None (e.g. tf.contrib.estimator.SavedModelEstimator).
        if global_step_tensor is not None:
          training_util._get_or_create_global_step_read(g)  # pylint: disable=protected-access
  
        features, labels, input_hooks = (
            self._get_features_and_labels_from_input_fn(
                input_fn, model_fn_lib.ModeKeys.TRAIN))
        worker_hooks.extend(input_hooks)
        estimator_spec = self._call_model_fn(
            features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
        global_step_tensor = training_util.get_global_step(g)
        return self._train_with_estimator_spec(estimator_spec, worker_hooks,
                                               hooks, global_step_tensor,
                                               saving_listeners)
  
    def _train_model_distributed(self, input_fn, hooks, saving_listeners):
      """Initiate training with `input_fn`, using `DistributionStrategies`.
  
      Args:
        input_fn: A function that provides input data for training as minibatches.
        hooks: List of `tf.train.SessionRunHook` subclass instances. Used for
          callbacks inside the training loop.
        saving_listeners: list of `tf.train.CheckpointSaverListener` objects. Used
          for callbacks that run immediately before or after checkpoint savings.
  
      Returns:
        Loss from training
      """
      self._train_distribution.configure(self._session_config)
  
      # TODO(sourabhbajaj): Remove this hack once we migrate the other strategies
      # to use the new API
      is_tpu_strategy = (
          self._train_distribution.__class__.__name__ == 'TPUStrategy')
  
      worker_hooks = []
      with ops.Graph().as_default() as g:
        # We want to create the iterations variable outside the distribution scope
        # as that is just stored on the host and mainly used to drive the loop
        # and doesn't need to be a Mirrored/Device variable.
        steps_per_run_variable = training.get_or_create_steps_per_run_variable()
        with self._train_distribution.scope():
          random_seed.set_random_seed(self._config.tf_random_seed)
  
          if is_tpu_strategy:
            # Create the iterator for run_on_dataset function
            # TODO(sourabhbajaj): refactor this out to call a function on the
            # strategy
            dataset = self._train_distribution.distribute_dataset(
                lambda: self._call_input_fn(input_fn,  # pylint: disable=g-long-lambda
                                            model_fn_lib.ModeKeys.TRAIN))
            iterator = dataset.make_initializable_iterator()
            worker_hooks.append(
                estimator_util._DatasetInitializerHook(iterator))  # pylint: disable=protected-access
  
            global_step_tensor = self._create_and_assert_global_step(g)
            # we want to add to the global collection in the main thread not the
            # tower threads.
            ops.add_to_collection(
                training_util.GLOBAL_STEP_READ_KEY,
                self._train_distribution.read_var(global_step_tensor))
  
            # Create a step_fn from the train_op of grouped_estimator_spec
            def step_fn(ctx, features, labels):
              """A single step that is passed to run_on_dataset."""
              estimator_spec = self._train_distribution.call_for_each_tower(
                  self._call_model_fn,
                  features,
                  labels,
                  model_fn_lib.ModeKeys.TRAIN,
                  self.config)
              ctx.set_last_step_output(
                  name='loss',
                  output=estimator_spec.loss,
                  aggregation=distribute_lib.get_loss_reduction())
              ctx.set_non_tensor_output(
                  name='estimator_spec', output=estimator_spec)
              return estimator_spec.train_op
  
            # Create new train_op post graph rewrites
            initial_training_loss = constant_op.constant(1e7)
            ctx = self._train_distribution.run_steps_on_dataset(
                step_fn, iterator, iterations=steps_per_run_variable,
                initial_loop_values={'loss': initial_training_loss})
            distributed_train_op = ctx.run_op
            tpu_result = ctx.last_step_outputs
            grouped_estimator_spec = ctx.non_tensor_outputs['estimator_spec']
          else:
            features, labels, input_hooks = (
                self._get_features_and_labels_from_input_fn(
                    input_fn, model_fn_lib.ModeKeys.TRAIN,
                    self._train_distribution))
            worker_hooks.extend(input_hooks)
            global_step_tensor = self._create_and_assert_global_step(g)
            # we want to add to the global collection in the main thread not the
            # tower threads.
            ops.add_to_collection(
                training_util.GLOBAL_STEP_READ_KEY,
                self._train_distribution.read_var(global_step_tensor))
            grouped_estimator_spec = self._train_distribution.call_for_each_tower(
                self._call_model_fn,
                features,
                labels,  # although this will be None it seems
                model_fn_lib.ModeKeys.TRAIN,
                self.config)
  
          scaffold = _combine_distributed_scaffold(
              grouped_estimator_spec.scaffold, self._train_distribution)
  
          def get_hooks_from_the_first_device(per_device_hooks):
            hooks_list = self._train_distribution.unwrap(per_device_hooks)
            assert hooks_list
            return hooks_list[0]
  
          training_hooks = get_hooks_from_the_first_device(
              grouped_estimator_spec.training_hooks)
          training_chief_hooks = get_hooks_from_the_first_device(
              grouped_estimator_spec.training_chief_hooks)
  
          # TODO(sourabhbajaj): Merge the two code paths and clean up the code
          if is_tpu_strategy:
            loss = tpu_result['loss']
            worker_hooks.append(
                estimator_util.StrategyInitFinalizeHook(
                    self._train_distribution.initialize,
                    self._train_distribution.finalize))
          else:
            loss = self._train_distribution.unwrap(
                self._train_distribution.reduce(
                    distribute_lib.get_loss_reduction(),
                    grouped_estimator_spec.loss,
                    destinations='/device:CPU:0'))[0]
            distributed_train_op = grouped_estimator_spec.train_op
  
          estimator_spec = model_fn_lib.EstimatorSpec(
              mode=grouped_estimator_spec.mode,
              loss=loss,
              train_op=self._train_distribution.group(distributed_train_op),
              training_hooks=training_hooks,
              training_chief_hooks=training_chief_hooks,
              scaffold=scaffold)
          return self._train_with_estimator_spec(estimator_spec, worker_hooks,
                                                 hooks, global_step_tensor,
                                                 saving_listeners)
  
    def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks,
                                   global_step_tensor, saving_listeners):
      """Train a model with the given Estimator Spec."""
      if self._warm_start_settings:
        logging.info('Warm-starting with WarmStartSettings: %s' %
                     (self._warm_start_settings,))
        warm_starting_util.warm_start(*self._warm_start_settings)
      # Check if the user created a loss summary, and add one if they didn't.
      # We assume here that the summary is called 'loss'. If it is not, we will
      # make another one with the name 'loss' to ensure it shows up in the right
      # graph in TensorBoard.
      if not any([x.op.name == 'loss'
                  for x in ops.get_collection(ops.GraphKeys.SUMMARIES)]):
        summary.scalar('loss', estimator_spec.loss)
      ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
      worker_hooks.extend(hooks)
      worker_hooks.append(
          training.NanTensorHook(estimator_spec.loss)
      )
      if self._config.log_step_count_steps is not None:
        worker_hooks.append(
            training.LoggingTensorHook(
                {
                    'loss': estimator_spec.loss,
                    'step': global_step_tensor
                },
                every_n_iter=self._config.log_step_count_steps)
        )
      worker_hooks.extend(estimator_spec.training_hooks)
  
      if not (estimator_spec.scaffold.saver or
              ops.get_collection(ops.GraphKeys.SAVERS)):
        ops.add_to_collection(
            ops.GraphKeys.SAVERS,
            training.Saver(
                sharded=True,
                max_to_keep=self._config.keep_checkpoint_max,
                keep_checkpoint_every_n_hours=(
                    self._config.keep_checkpoint_every_n_hours),
                defer_build=True,
                save_relative_paths=True))
  
      chief_hooks = []
      all_hooks = worker_hooks + list(estimator_spec.training_chief_hooks)
      saver_hooks = [
          h for h in all_hooks if isinstance(h, training.CheckpointSaverHook)]
      if (self._config.save_checkpoints_secs or
          self._config.save_checkpoints_steps):
        if not saver_hooks:
          chief_hooks = [
              training.CheckpointSaverHook(
                  self._model_dir,
                  save_secs=self._config.save_checkpoints_secs,
                  save_steps=self._config.save_checkpoints_steps,
                  scaffold=estimator_spec.scaffold)
          ]
          saver_hooks = [chief_hooks[0]]
      if saving_listeners:
        if not saver_hooks:
          raise ValueError(
              'There should be a CheckpointSaverHook to use saving_listeners. '
              'Please set one of the RunConfig.save_checkpoints_steps or '
              'RunConfig.save_checkpoints_secs.')
        else:
          # It is expected to have one CheckpointSaverHook. If multiple, we pick
          # up the first one to add listener.
          saver_hooks[0]._listeners.extend(saving_listeners)  # pylint: disable=protected-access
      with training.MonitoredTrainingSession(
          master=self._config.master,
          is_chief=self._config.is_chief,
          checkpoint_dir=self._model_dir,
          scaffold=estimator_spec.scaffold,
          hooks=worker_hooks,
          chief_only_hooks=(
              tuple(chief_hooks) + tuple(estimator_spec.training_chief_hooks)),
          save_checkpoint_secs=0,  # Saving is handled by a hook.
          save_summaries_steps=self._config.save_summary_steps,
          config=self._session_config,
          log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
        loss = None
        while not mon_sess.should_stop():
          _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
      return loss
  
    def _evaluate_build_graph(self, input_fn, hooks=None, checkpoint_path=None):
      """Builds the graph and related hooks to run evaluation."""
      random_seed.set_random_seed(self._config.tf_random_seed)
      self._create_and_assert_global_step(ops.get_default_graph())
      features, labels, input_hooks = (
          self._get_features_and_labels_from_input_fn(
              input_fn, model_fn_lib.ModeKeys.EVAL, self._eval_distribution))
  
      if self._eval_distribution:
        (loss_metric, scaffold, evaluation_hooks, eval_metric_ops) = (
            self._call_model_fn_eval_distributed(features, labels, self.config))
      else:
        (loss_metric, scaffold, evaluation_hooks, eval_metric_ops) = (
            self._call_model_fn_eval(features, labels, self.config))
  
      global_step_tensor = training_util.get_global_step(ops.get_default_graph())
      # Call to warm_start has to be after model_fn is called.
      self._maybe_warm_start(checkpoint_path)
  
      if model_fn_lib.LOSS_METRIC_KEY in eval_metric_ops:
        raise ValueError(
            'Metric with name "%s" is not allowed, because Estimator ' %
            (model_fn_lib.LOSS_METRIC_KEY) +
            'already defines a default metric with the same name.')
      eval_metric_ops[model_fn_lib.LOSS_METRIC_KEY] = loss_metric
  
      update_op, eval_dict = _extract_metric_update_ops(eval_metric_ops,
                                                        self._eval_distribution)
  
      if ops.GraphKeys.GLOBAL_STEP in eval_dict:
        raise ValueError(
            'Metric with name `global_step` is not allowed, because Estimator '
            'already defines a default metric with the same name.')
      eval_dict[ops.GraphKeys.GLOBAL_STEP] = global_step_tensor
  
      all_hooks = list(input_hooks)
      all_hooks.extend(hooks)
      all_hooks.extend(list(evaluation_hooks or []))
      # New local variables have been added, so update the estimator spec's
      # local init op if it was defined.
      if scaffold and scaffold.local_init_op:
        # Ensure that eval step has been created before updating local init op.
        evaluation._get_or_create_eval_step()  # pylint: disable=protected-access
  
        scaffold = monitored_session.Scaffold(
            local_init_op=control_flow_ops.group(
                scaffold.local_init_op,
                monitored_session.Scaffold.default_local_init_op()),
            copy_from_scaffold=scaffold
        )
  
      return scaffold, update_op, eval_dict, all_hooks
  
    def _call_model_fn_eval(self, features, labels, config):
      estimator_spec = self._call_model_fn(
          features, labels, model_fn_lib.ModeKeys.EVAL, config)
      loss_metric = metrics_lib.mean(estimator_spec.loss)
      return (loss_metric, estimator_spec.scaffold,
              estimator_spec.evaluation_hooks, estimator_spec.eval_metric_ops)
  
    def _call_model_fn_eval_distributed(self, features, labels, config):
      """Call model_fn in distribution mode and handle return values."""
      grouped_estimator_spec = self._eval_distribution.call_for_each_tower(
          self._call_model_fn, features, labels,
          model_fn_lib.ModeKeys.EVAL, config)
      scaffold = _combine_distributed_scaffold(
          grouped_estimator_spec.scaffold, self._eval_distribution)
      evaluation_hooks = self._eval_distribution.unwrap(
          grouped_estimator_spec.evaluation_hooks)[0]
      loss_metric = self._eval_distribution.call_for_each_tower(
          metrics_lib.mean, grouped_estimator_spec.loss)
      return (loss_metric, scaffold,
              evaluation_hooks, grouped_estimator_spec.eval_metric_ops)
  
    def _evaluate_run(self, checkpoint_path, scaffold, update_op, eval_dict,
                      all_hooks, output_dir):
      """Run evaluation."""
      eval_results = evaluation._evaluate_once(  # pylint: disable=protected-access
          checkpoint_path=checkpoint_path,
          master=self._config.evaluation_master,
          scaffold=scaffold,
          eval_ops=update_op,
          final_ops=eval_dict,
          hooks=all_hooks,
          config=self._session_config)
  
      current_global_step = eval_results[ops.GraphKeys.GLOBAL_STEP]
  
      _write_dict_to_summary(
          output_dir=output_dir,
          dictionary=eval_results,
          current_global_step=current_global_step)
  
      if checkpoint_path:
        _write_checkpoint_path_to_summary(
            output_dir=output_dir,
            checkpoint_path=checkpoint_path,
            current_global_step=current_global_step)
  
      return eval_results
  
    def _maybe_warm_start(self, checkpoint_path):
      if not checkpoint_path and self._warm_start_settings:
        logging.info('Warm-starting with WarmStartSettings: %s' %
                     (self._warm_start_settings,))
        warm_starting_util.warm_start(*self._warm_start_settings)
  
  
  def maybe_overwrite_model_dir_and_session_config(config, model_dir):
    """Overwrite estimator config by `model_dir` and `session_config` if needed.
  
    Args:
      config: Original estimator config.
      model_dir: Estimator model checkpoint directory.
  
    Returns:
      Overwritten estimator config.
  
    Raises:
      ValueError: Model directory inconsistent between `model_dir` and `config`.
    """
  
    if config is None:
      config = run_config.RunConfig()
      logging.info('Using default config.')
    if not isinstance(config, run_config.RunConfig):
      raise ValueError(
          'config must be an instance of `RunConfig`, but provided %s.' % config)
  
    if config.session_config is None:
      session_config = run_config.get_default_session_config()
      config = run_config.RunConfig.replace(config, session_config=session_config)
  
    model_dir = compat_internal.path_to_str(model_dir)
    if model_dir is not None:
      if (getattr(config, 'model_dir', None) is not None and
          config.model_dir != model_dir):
        raise ValueError(
            "`model_dir` are set both in constructor and `RunConfig`, but with "
            "different values. In constructor: '{}', in `RunConfig`: "
            "'{}' ".format(model_dir, config.model_dir))
    if model_dir:
      config = run_config.RunConfig.replace(config, model_dir=model_dir)
    elif getattr(config, 'model_dir', None) is None:
      model_dir = tempfile.mkdtemp()
      logging.warning('Using temporary folder as model directory: %s', model_dir)
      config = run_config.RunConfig.replace(config, model_dir=model_dir)
  
    return config
  
  
  def create_per_tower_ready_op(scaffold):
    """Create a `tf.train.Scaffold.ready_op` inside a tower."""
    if scaffold.ready_op:
      return scaffold.ready_op
  
    def default_ready_op():
      return array_ops.concat([
          variables.report_uninitialized_variables(),
          resources.report_uninitialized_resources()
      ], 0)
  
    return monitored_session.Scaffold.get_or_default(
        'ready_op', ops.GraphKeys.READY_OP, default_ready_op)
  
  
  def create_per_tower_ready_for_local_init_op(scaffold):
    """Create a `tf.train.Scaffold.ready_for_local_init_op` inside a tower."""
    if scaffold.ready_for_local_init_op:
      return scaffold.ready_for_local_init_op
  
    def default_ready_for_local_init_op():
      return variables.report_uninitialized_variables(
          variables.global_variables())
  
    return monitored_session.Scaffold.get_or_default(
        'ready_for_local_init_op', ops.GraphKeys.READY_FOR_LOCAL_INIT_OP,
        default_ready_for_local_init_op)
  
  
  def _combine_distributed_scaffold(grouped_scaffold, distribution):
    """Combines scaffold(s) returned from `distribution.call_for_each_tower`."""
  
    # TODO(anjalisridhar): Figure out how to resolve the following scaffold
    # parameters: init_feed_dict, init_fn.
    scaffold_list = distribution.unwrap(grouped_scaffold)
    init_feed_dict = [
        s.init_feed_dict
        for s in scaffold_list
        if s.init_feed_dict is not None
    ]
    if init_feed_dict:
      init_feed_dict = distribution.group(init_feed_dict)
    else:
      init_feed_dict = None
  
    init_fn = [s.init_fn for s in scaffold_list if s.init_fn is not None]
    if init_fn:
      init_fn = distribution.group(init_fn)
    else:
      init_fn = None
  
    init_op = [s.init_op for s in scaffold_list if s.init_op is not None]
    if init_op:
      init_op = distribution.group(init_op)
    else:
      init_op = None
  
    def _unwrap_and_concat(value):
      value = nest.flatten(distribution.unwrap(value))
      if len(value) != 1:
        return array_ops.concat(value)
      return value[0]
  
    ready_op = distribution.call_for_each_tower(
        create_per_tower_ready_op, grouped_scaffold)
    if ready_op is not None:
      ready_op = _unwrap_and_concat(ready_op)
    else:
      ready_op = None
  
    ready_for_local_init_op = distribution.call_for_each_tower(
        create_per_tower_ready_for_local_init_op, grouped_scaffold)
    if ready_for_local_init_op is not None:
      ready_for_local_init_op = _unwrap_and_concat(ready_for_local_init_op)
    else:
      ready_for_local_init_op = None
  
    local_init_op = [
        s.local_init_op
        for s in scaffold_list
        if s.local_init_op is not None
    ]
    if local_init_op:
      local_init_op = distribution.group(local_init_op)
    else:
      local_init_op = None
  
    summary_op = [
        s.summary_op for s in scaffold_list if s.summary_op is not None
    ]
    if summary_op:
      summary_op = distribution.group(summary_op)
    else:
      summary_op = None
  
    scaffold = monitored_session.Scaffold(
        init_op=init_op,
        ready_op=ready_op,
        ready_for_local_init_op=ready_for_local_init_op,
        local_init_op=local_init_op,
        summary_op=summary_op,
        init_feed_dict=init_feed_dict,
        init_fn=init_fn)
    return scaffold
  
  
  def _check_checkpoint_available(model_dir):
    latest_path = checkpoint_management.latest_checkpoint(model_dir)
    if not latest_path:
      raise ValueError(
          'Could not find trained model in model_dir: {}.'.format(model_dir))
  
  
  def _check_hooks_type(hooks):
    """Returns hooks if all are `SessionRunHook`, raises TypeError otherwise."""
    hooks = list(hooks or [])
    for h in hooks:
      if not isinstance(h, training.SessionRunHook):
        raise TypeError('Hooks must be a SessionRunHook, given: {}'.format(h))
    return hooks
  
  
  def _check_listeners_type(saving_listeners):
    """Check listeners type."""
    listeners = list(saving_listeners or [])
    for l in listeners:
      if not isinstance(l, training.CheckpointSaverListener):
        raise TypeError(
            'saving_listeners must be a list of CheckpointSaverListener, '
            'given: {}'.format(l))
    return listeners
  
  
  def _get_replica_device_setter(config):
    """Creates a replica device setter if required as a default `device_fn`.
  
    `Estimator` uses `tf.train.ReplicaDeviceSetter` as a default device placer. It
    sets the
    distributed related arguments such as number of `ps_replicas` based on given
    `config`.
  
    Args:
      config: A `tf.estimator.RunConfig` instance.
  
    Returns:
      A replica device setter, or `None`.
    """
    if config.task_type:
      worker_device = '/job:%s/task:%d' % (config.task_type, config.task_id)
    else:
      worker_device = '/job:worker'
  
    if config.num_ps_replicas > 0:
      return training.replica_device_setter(
          ps_tasks=config.num_ps_replicas,
          worker_device=worker_device,
          merge_devices=True,
          ps_ops=list(device_setter.STANDARD_PS_OPS),
          cluster=config.cluster_spec)
    else:
      return None
  
  
  def _verify_model_fn_args(model_fn, params):
    """Verifies `model_fn` arguments."""
    args = set(function_utils.fn_args(model_fn))
    if 'features' not in args:
      raise ValueError('model_fn (%s) must include features argument.' % model_fn)
    if params is not None and 'params' not in args:
      raise ValueError('model_fn (%s) does not include params argument, '
                       'but params (%s) is passed to Estimator.' % (model_fn,
                                                                    params))
    if params is None and 'params' in args:
      logging.warning('Estimator\'s model_fn (%s) includes params '
                      'argument, but params are not passed to Estimator.',
                      model_fn)
    non_valid_args = list(args - _VALID_MODEL_FN_ARGS)
    if non_valid_args:
      raise ValueError('model_fn (%s) has following not expected args: %s' %
                       (model_fn, non_valid_args))
  
  
  def _load_global_step_from_checkpoint_dir(checkpoint_dir):
    try:
      checkpoint_reader = training.NewCheckpointReader(
          training.latest_checkpoint(checkpoint_dir))
      return checkpoint_reader.get_tensor(ops.GraphKeys.GLOBAL_STEP)
    except:  # pylint: disable=bare-except
      return 0
  
  
  def _extract_metric_update_ops(eval_dict, distribution=None):
    """Separate update operations from metric value operations."""
    update_ops = []
    value_ops = {}
    # Sort metrics lexicographically so graph is identical every time.
    for name, metric_ops in sorted(six.iteritems(eval_dict)):
      value_ops[name] = metric_ops[0]
      if distribution:
        update_op = distribution.group(metric_ops[1])
      else:
        update_op = metric_ops[1]
      update_ops.append(update_op)
  
    if update_ops:
      update_op = control_flow_ops.group(*update_ops)
    else:
      update_op = None
  
    return update_op, value_ops
  
  
  def _dict_to_str(dictionary):
    """Get a `str` representation of a `dict`.
  
    Args:
      dictionary: The `dict` to be represented as `str`.
  
    Returns:
      A `str` representing the `dictionary`.
    """
    return ', '.join('%s = %s' % (k, v)
                     for k, v in sorted(six.iteritems(dictionary))
                     if not isinstance(v, six.binary_type))
  
  
  def _write_dict_to_summary(output_dir,
                             dictionary,
                             current_global_step):
    """Writes a `dict` into summary file in given output directory.
  
    Args:
      output_dir: `str`, directory to write the summary file in.
      dictionary: the `dict` to be written to summary file.
      current_global_step: `int`, the current global step.
    """
    logging.info('Saving dict for global step %d: %s', current_global_step,
                 _dict_to_str(dictionary))
    summary_writer = writer_cache.FileWriterCache.get(output_dir)
    summary_proto = summary_pb2.Summary()
    for key in dictionary:
      if dictionary[key] is None:
        continue
      if key == 'global_step':
        continue
      if (isinstance(dictionary[key], np.float32) or
          isinstance(dictionary[key], float)):
        summary_proto.value.add(tag=key, simple_value=float(dictionary[key]))
      elif (isinstance(dictionary[key], np.int64) or
            isinstance(dictionary[key], np.int32) or
            isinstance(dictionary[key], int)):
        summary_proto.value.add(tag=key, simple_value=int(dictionary[key]))
      elif isinstance(dictionary[key], six.binary_type):
        try:
          summ = summary_pb2.Summary.FromString(dictionary[key])
          for i, _ in enumerate(summ.value):
            summ.value[i].tag = '%s/%d' % (key, i)
          summary_proto.value.extend(summ.value)
        except message.DecodeError:
          logging.warn('Skipping summary for %s, cannot parse string to Summary.',
                       key)
          continue
      elif isinstance(dictionary[key], np.ndarray):
        value = summary_proto.value.add()
        value.tag = key
        value.node_name = key
        tensor_proto = tensor_util.make_tensor_proto(dictionary[key])
        value.tensor.CopyFrom(tensor_proto)
        # pylint: disable=line-too-long
        logging.info(
            'Summary for np.ndarray is not visible in Tensorboard by default. '
            'Consider using a Tensorboard plugin for visualization (see '
            'https://github.com/tensorflow/tensorboard-plugin-example/blob/master/README.md'
            ' for more information).')
        # pylint: enable=line-too-long
      else:
        logging.warn(
            'Skipping summary for %s, must be a float, np.float32, np.int64, '
            'np.int32 or int or np.ndarray or a serialized string of Summary.',
            key)
    summary_writer.add_summary(summary_proto, current_global_step)
    summary_writer.flush()
  
  
  def _write_checkpoint_path_to_summary(output_dir, checkpoint_path,
                                        current_global_step):
    """Writes `checkpoint_path` into summary file in the given output directory.
  
    Args:
      output_dir: `str`, directory to write the summary file in.
      checkpoint_path: `str`, checkpoint file path to be written to summary file.
      current_global_step: `int`, the current global step.
    """
  
    checkpoint_path_tag = 'checkpoint_path'
  
    logging.info('Saving \'%s\' summary for global step %d: %s',
                 checkpoint_path_tag, current_global_step, checkpoint_path)
    summary_proto = summary_pb2.Summary()
    summary_proto.value.add(
        tag=checkpoint_path_tag,
        tensor=tensor_util.make_tensor_proto(
            checkpoint_path, dtype=dtypes.string))
    summary_writer = writer_cache.FileWriterCache.get(output_dir)
    summary_writer.add_summary(summary_proto, current_global_step)
    summary_writer.flush()
  
  
  def _has_dataset_or_queue_runner(maybe_tensor):
    """Returns `True` if `Dataset` or `QueueRunner` has been used."""
    # Check TF dataset first. Here, we use a simple algorithm to check the top
    # level Tensors only, which should be sufficient for most users.
    tensors = [x for x in nest.flatten(maybe_tensor) if isinstance(x, ops.Tensor)]
    if any([t.op.type == 'IteratorGetNext' for t in tensors]):
      return True
  
    # Now, check queue.
    return ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS)
  
  
  VocabInfo = warm_starting_util.VocabInfo  # pylint: disable=invalid-name
  estimator_export('estimator.VocabInfo')(VocabInfo)
  
  
  @estimator_export('estimator.WarmStartSettings')
  class WarmStartSettings(
      collections.namedtuple('WarmStartSettings', [
          'ckpt_to_initialize_from',
          'vars_to_warm_start',
          'var_name_to_vocab_info',
          'var_name_to_prev_var_name',
      ])):
    """Settings for warm-starting in `tf.estimator.Estimators`.
  
    Example Use with canned `tf.estimator.DNNEstimator`:
  
    ```
    emb_vocab_file = tf.feature_column.embedding_column(
        tf.feature_column.categorical_column_with_vocabulary_file(
            "sc_vocab_file", "new_vocab.txt", vocab_size=100),
        dimension=8)
    emb_vocab_list = tf.feature_column.embedding_column(
        tf.feature_column.categorical_column_with_vocabulary_list(
            "sc_vocab_list", vocabulary_list=["a", "b"]),
        dimension=8)
    estimator = tf.estimator.DNNClassifier(
      hidden_units=[128, 64], feature_columns=[emb_vocab_file, emb_vocab_list],
      warm_start_from=ws)
    ```
  
    where `ws` could be defined as:
  
    Warm-start all weights in the model (input layer and hidden weights).
    Either the directory or a specific checkpoint can be provided (in the case
    of the former, the latest checkpoint will be used):
  
    ```
    ws = WarmStartSettings(ckpt_to_initialize_from="/tmp")
    ws = WarmStartSettings(ckpt_to_initialize_from="/tmp/model-1000")
    ```
  
    Warm-start only the embeddings (input layer):
  
    ```
    ws = WarmStartSettings(ckpt_to_initialize_from="/tmp",
                           vars_to_warm_start=".*input_layer.*")
    ```
  
    Warm-start all weights but the embedding parameters corresponding to
    `sc_vocab_file` have a different vocab from the one used in the current
    model:
  
    ```
    vocab_info = tf.estimator.VocabInfo(
        new_vocab=sc_vocab_file.vocabulary_file,
        new_vocab_size=sc_vocab_file.vocabulary_size,
        num_oov_buckets=sc_vocab_file.num_oov_buckets,
        old_vocab="old_vocab.txt"
    )
    ws = WarmStartSettings(
        ckpt_to_initialize_from="/tmp",
        var_name_to_vocab_info={
            "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
        })
    ```
  
    Warm-start only `sc_vocab_file` embeddings (and no other variables), which
    have a different vocab from the one used in the current model:
  
    ```
    vocab_info = tf.estimator.VocabInfo(
        new_vocab=sc_vocab_file.vocabulary_file,
        new_vocab_size=sc_vocab_file.vocabulary_size,
        num_oov_buckets=sc_vocab_file.num_oov_buckets,
        old_vocab="old_vocab.txt"
    )
    ws = WarmStartSettings(
        ckpt_to_initialize_from="/tmp",
        vars_to_warm_start=None,
        var_name_to_vocab_info={
            "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
        })
    ```
  
    Warm-start all weights but the parameters corresponding to `sc_vocab_file`
    have a different vocab from the one used in current checkpoint, and only
    100 of those entries were used:
  
    ```
    vocab_info = tf.estimator.VocabInfo(
        new_vocab=sc_vocab_file.vocabulary_file,
        new_vocab_size=sc_vocab_file.vocabulary_size,
        num_oov_buckets=sc_vocab_file.num_oov_buckets,
        old_vocab="old_vocab.txt",
        old_vocab_size=100
    )
    ws = WarmStartSettings(
        ckpt_to_initialize_from="/tmp",
        var_name_to_vocab_info={
            "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
        })
    ```
  
    Warm-start all weights but the parameters corresponding to `sc_vocab_file`
    have a different vocab from the one used in current checkpoint and the
    parameters corresponding to `sc_vocab_list` have a different name from the
    current checkpoint:
  
    ```
    vocab_info = tf.estimator.VocabInfo(
        new_vocab=sc_vocab_file.vocabulary_file,
        new_vocab_size=sc_vocab_file.vocabulary_size,
        num_oov_buckets=sc_vocab_file.num_oov_buckets,
        old_vocab="old_vocab.txt",
        old_vocab_size=100
    )
    ws = WarmStartSettings(
        ckpt_to_initialize_from="/tmp",
        var_name_to_vocab_info={
            "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info
        },
        var_name_to_prev_var_name={
            "input_layer/sc_vocab_list_embedding/embedding_weights":
                "old_tensor_name"
        })
    ```
  
    Attributes:
      ckpt_to_initialize_from: [Required] A string specifying the directory with
        checkpoint file(s) or path to checkpoint from which to warm-start the
        model parameters.
      vars_to_warm_start: [Optional] One of the following:  - A regular expression
        (string) that captures which variables to warm-start (see
        `tf.get_collection`).  This expression will only consider variables in the
        `TRAINABLE_VARIABLES` collection. - A list of Variables to warm-start. - A
        list of strings, each representing a full variable name to warm-start. -
        `None`, in which case only variables specified in `var_name_to_vocab_info`
        will be warm-started.  Defaults to `'.*'`, which warm-starts all variables
        in the `TRAINABLE_VARIABLES` collection.  Note that this excludes
        variables such as accumulators and moving statistics from batch norm.
      var_name_to_vocab_info: [Optional] Dict of variable names (strings) to
        `tf.estimator.VocabInfo`. The variable names should be "full" variables,
        not the names of the partitions.  If not explicitly provided, the variable
        is assumed to have no vocabulary.
      var_name_to_prev_var_name: [Optional] Dict of variable names (strings) to
        name of the previously-trained variable in `ckpt_to_initialize_from`. If
        not explicitly provided, the name of the variable is assumed to be same
        between previous checkpoint and current model.
    """
  
    def __new__(cls,
                ckpt_to_initialize_from,
                vars_to_warm_start='.*',
                var_name_to_vocab_info=None,
                var_name_to_prev_var_name=None):
      if not ckpt_to_initialize_from:
        raise ValueError(
            '`ckpt_to_initialize_from` MUST be set in WarmStartSettings')
      return super(WarmStartSettings, cls).__new__(
          cls,
          ckpt_to_initialize_from,
          vars_to_warm_start,
          var_name_to_vocab_info or {},
          var_name_to_prev_var_name or {},
      )
  
  
  def _get_saved_model_ckpt(saved_model_dir):
    """Return path to variables checkpoint in a `SavedModel` directory."""
    if not gfile.Exists(
        os.path.join(saved_model_utils.get_variables_dir(saved_model_dir),
                     compat.as_text('variables.index'))):
      raise ValueError('Directory provided has an invalid SavedModel format: %s'
                       % saved_model_dir)
    return saved_model_utils.get_variables_path(saved_model_dir)
  
  
  def _get_default_warm_start_settings(warm_start_from):
    """Returns default `tf.estimator.WarmStartSettings`.
  
    Args:
      warm_start_from: Either a string representing the filepath of a checkpoint
        or `SavedModel` to initialize from, or an instance of
        `tf.estimator.WarmStartSettings`.
  
    Returns:
      Either None or an instance of `WarmStartSettings`.
  
    Raises:
      ValueError: If `warm_start_from` is not `None` but is neither a string nor
      an
        instance of `WarmStartSettings`.
    """
    if warm_start_from is None:
      return None
    if isinstance(warm_start_from, (six.string_types, six.binary_type)):
      # Infer that this is a SavedModel if export_path +
      # 'variables/variables.index' exists, and if so, construct the
      # WarmStartSettings pointing to the variables path
      # (export_path + 'variables/variables').
      if gfile.Exists(os.path.join(
          saved_model_utils.get_variables_dir(warm_start_from),
          compat.as_text('variables.index'))):
        logging.info('Warm-starting from a SavedModel')
        return WarmStartSettings(
            ckpt_to_initialize_from=saved_model_utils.get_variables_path(
                warm_start_from))
      return WarmStartSettings(ckpt_to_initialize_from=warm_start_from)
    elif isinstance(warm_start_from, WarmStartSettings):
      return warm_start_from
    else:
      raise ValueError('warm_start_from must be a string or a WarmStartSettings, '
                       'instead got {}'.format(type(warm_start_from)))
  