text: # Copyright 2017 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.
  # ==============================================================================
  """Python wrappers for Datasets."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import abc
  import functools
  import threading
  import warnings
  
  import numpy as np
  import six
  from six.moves import queue as Queue  # pylint: disable=redefined-builtin
  
  
  from tensorflow.python.compat import compat
  from tensorflow.python.data.experimental.ops import filter_for_shard_ops
  from tensorflow.python.data.experimental.ops import optimization_options
  from tensorflow.python.data.experimental.ops import stats_options
  from tensorflow.python.data.experimental.ops import threading_options
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.data.util import nest
  from tensorflow.python.data.util import options as options_lib
  from tensorflow.python.data.util import random_seed
  from tensorflow.python.data.util import sparse
  from tensorflow.python.data.util import structure as structure_lib
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed as core_random_seed
  from tensorflow.python.framework import smart_cond
  from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
  from tensorflow.python.framework import tensor_shape
  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 gen_dataset_ops
  from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
  from tensorflow.python.ops import gen_io_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import script_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import function_utils
  from tensorflow.python.util.tf_export import tf_export
  
  
  ops.NotDifferentiable("ReduceDataset")
  
  
  @tf_export("data.Dataset", v1=[])
  @six.add_metaclass(abc.ABCMeta)
  class DatasetV2(object):
    """Represents a potentially large set of elements.
  
    A `Dataset` can be used to represent an input pipeline as a
    collection of elements (nested structures of tensors) and a "logical
    plan" of transformations that act on those elements.
    """
  
    def __init__(self):
      self._graph_attr = ops.get_default_graph()
  
    def _as_serialized_graph(self):
      """Produces serialized graph representation of the dataset.
  
      Returns:
        A scalar `tf.Tensor` of `tf.string` type, representing this dataset as a
        serialized graph.
      """
      return gen_dataset_ops.dataset_to_graph(self._as_variant_tensor())
  
    @abc.abstractmethod
    def _as_variant_tensor(self):
      """Creates a scalar `tf.Tensor` of `tf.variant` representing this dataset.
  
      Returns:
        A scalar `tf.Tensor` of `tf.variant` type, which represents this dataset.
      """
      raise NotImplementedError("Dataset._as_variant_tensor")
  
    @abc.abstractmethod
    def _inputs(self):
      """Returns a list of the input datasets of the dataset."""
  
      raise NotImplementedError("Dataset._inputs")
  
    @property
    def _graph(self):
      return self._graph_attr
  
    @_graph.setter
    def _graph(self, _):
      raise ValueError("The _graph property is read-only")
  
    def _has_captured_ref(self):
      """Whether this dataset uses a function that captures ref variables.
  
      Returns:
        A boolean, which if true indicates that the dataset or one of its inputs
        uses a function that captures ref variables.
      """
      if context.executing_eagerly():
        # RefVariables are not supported in eager mode
        return False
  
      def is_tensor_or_parent_ref(tensor):
        if tensor.dtype._is_ref_dtype:  # pylint: disable=protected-access
          return True
        return any([is_tensor_or_parent_ref(x) for x in tensor.op.inputs])
  
      for fn in self._functions():
        if any([is_tensor_or_parent_ref(t) for t in fn.function.captured_inputs]):
          return True
  
      return any(
          [input_dataset._has_captured_ref() for input_dataset in self._inputs()])  # pylint: disable=protected-access
  
    def _functions(self):
      """Returns a list of functions associated with this dataset.
  
      Returns:
        A list of `StructuredFunctionWrapper` objects.
      """
      return []
  
    def options(self):
      """Returns the options for this dataset and its inputs.
  
      Returns:
        A `tf.data.Options` object representing the dataset options.
      """
      options = Options()
      for input_dataset in self._inputs():
        input_options = input_dataset.options()
        if input_options is not None:
          options = options.merge(input_options)
      return options
  
    def _apply_options(self):
      """Apply options, such as optimization configuration, to the dataset."""
  
      dataset = self
      options = self.options()
      if options.experimental_threading is not None:
        t_options = options.experimental_threading
        if t_options.private_threadpool_size is not None:
          dataset = _PrivateThreadPoolDataset(dataset,
                                              t_options.private_threadpool_size)
        if t_options.max_intra_op_parallelism is not None:
          dataset = _MaxIntraOpParallelismDataset(
              dataset, t_options.max_intra_op_parallelism)
      static_optimizations = options._static_optimizations()  # pylint: disable=protected-access
      if static_optimizations:
        if self._has_captured_ref():
          warnings.warn(
              "tf.data static optimizations are not compatible with tf.Variable. "
              "The following optimizations will be disabled: %s. To enable "
              "optimizations, use resource variables instead by calling "
              "`tf.enable_resource_variables()` at the start of the program." %
              ", ".join(static_optimizations))
        else:
          dataset = _OptimizeDataset(dataset, static_optimizations)
  
      if options.experimental_autotune is not False:
        dataset = _ModelDataset(dataset)
      if options.experimental_stats and options.experimental_stats.aggregator:  # pylint: disable=line-too-long
        dataset = _SetStatsAggregatorDataset(  # pylint: disable=protected-access
            dataset, options.experimental_stats.aggregator,
            options.experimental_stats.prefix,
            options.experimental_stats.counter_prefix)
      return dataset
  
    def __iter__(self):
      """Creates an `Iterator` for enumerating the elements of this dataset.
  
      The returned iterator implements the Python iterator protocol and therefore
      can only be used in eager mode.
  
      Returns:
        An `Iterator` over the elements of this dataset.
  
      Raises:
        RuntimeError: If eager execution is not enabled.
      """
      if context.executing_eagerly():
        return iterator_ops.EagerIterator(self)
      else:
        raise RuntimeError("dataset.__iter__() is only supported when eager "
                           "execution is enabled.")
  
    @abc.abstractproperty
    def _element_structure(self):
      """The structure of an element of this dataset.
  
      Returns:
        A `Structure` object representing the structure of an element of this
        dataset.
      """
      raise NotImplementedError("Dataset._element_structure")
  
    @property
    def output_classes(self):
      """Returns the class of each component of an element of this dataset.
  
      The expected values are `tf.Tensor` and `tf.SparseTensor`.
  
      Returns:
        A nested structure of Python `type` objects corresponding to each
        component of an element of this dataset.
      """
      return self._element_structure._to_legacy_output_classes()  # pylint: disable=protected-access
  
    @property
    def output_shapes(self):
      """Returns the shape of each component of an element of this dataset.
  
      Returns:
        A nested structure of `tf.TensorShape` objects corresponding to each
        component of an element of this dataset.
      """
      return self._element_structure._to_legacy_output_shapes()  # pylint: disable=protected-access
  
    @property
    def output_types(self):
      """Returns the type of each component of an element of this dataset.
  
      Returns:
        A nested structure of `tf.DType` objects corresponding to each component
        of an element of this dataset.
      """
      return self._element_structure._to_legacy_output_types()  # pylint: disable=protected-access
  
    def __repr__(self):
      output_shapes = nest.map_structure(str, self.output_shapes)
      output_shapes = str(output_shapes).replace("'", "")
      output_types = nest.map_structure(repr, self.output_types)
      output_types = str(output_types).replace("'", "")
      return ("<%s shapes: %s, types: %s>" % (type(self).__name__, output_shapes,
                                              output_types))
  
    @staticmethod
    def from_tensors(tensors):
      """Creates a `Dataset` with a single element, comprising the given tensors.
  
      Note that if `tensors` contains a NumPy array, and eager execution is not
      enabled, the values will be embedded in the graph as one or more
      `tf.constant` operations. For large datasets (> 1 GB), this can waste
      memory and run into byte limits of graph serialization. If `tensors`
      contains one or more large NumPy arrays, consider the alternative described
      in [this
      guide](https://tensorflow.org/guide/datasets#consuming_numpy_arrays).
  
      Args:
        tensors: A nested structure of tensors.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return TensorDataset(tensors)
  
    @staticmethod
    def from_tensor_slices(tensors):
      """Creates a `Dataset` whose elements are slices of the given tensors.
  
      Note that if `tensors` contains a NumPy array, and eager execution is not
      enabled, the values will be embedded in the graph as one or more
      `tf.constant` operations. For large datasets (> 1 GB), this can waste
      memory and run into byte limits of graph serialization. If `tensors`
      contains one or more large NumPy arrays, consider the alternative described
      in [this guide](
      https://tensorflow.org/guide/datasets#consuming_numpy_arrays).
  
      Args:
        tensors: A nested structure of tensors, each having the same size in the
          0th dimension.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return TensorSliceDataset(tensors)
  
    class _GeneratorState(object):
      """Stores outstanding iterators created from a Python generator.
  
      This class keeps track of potentially multiple iterators that may have
      been created from a generator, e.g. in the case that the dataset is
      repeated, or nested within a parallel computation.
      """
  
      def __init__(self, generator):
        self._generator = generator
        self._lock = threading.Lock()
        self._next_id = 0  # GUARDED_BY(self._lock)
        self._args = {}
        self._iterators = {}
  
      def get_next_id(self, *args):
        with self._lock:
          ret = self._next_id
          self._next_id += 1
        self._args[ret] = args
        # NOTE(mrry): Explicitly create an array of `np.int64` because implicit
        # casting in `py_func()` will create an array of `np.int32` on Windows,
        # leading to a runtime error.
        return np.array(ret, dtype=np.int64)
  
      def get_iterator(self, iterator_id):
        try:
          return self._iterators[iterator_id]
        except KeyError:
          iterator = iter(self._generator(*self._args.pop(iterator_id)))
          self._iterators[iterator_id] = iterator
          return iterator
  
      def iterator_completed(self, iterator_id):
        del self._iterators[iterator_id]
  
    @staticmethod
    def from_generator(generator, output_types, output_shapes=None, args=None):
      """Creates a `Dataset` whose elements are generated by `generator`.
  
      The `generator` argument must be a callable object that returns
      an object that support the `iter()` protocol (e.g. a generator function).
      The elements generated by `generator` must be compatible with the given
      `output_types` and (optional) `output_shapes` arguments.
  
      For example:
  
      ```python
      import itertools
      tf.enable_eager_execution()
  
      def gen():
        for i in itertools.count(1):
          yield (i, [1] * i)
  
      ds = tf.data.Dataset.from_generator(
          gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None])))
  
      for value in ds.take(2):
        print value
      # (1, array([1]))
      # (2, array([1, 1]))
      ```
  
      NOTE: The current implementation of `Dataset.from_generator()` uses
      `tf.py_func` and inherits the same constraints. In particular, it
      requires the `Dataset`- and `Iterator`-related operations to be placed
      on a device in the same process as the Python program that called
      `Dataset.from_generator()`. The body of `generator` will not be
      serialized in a `GraphDef`, and you should not use this method if you
      need to serialize your model and restore it in a different environment.
  
      NOTE: If `generator` depends on mutable global variables or other external
      state, be aware that the runtime may invoke `generator` multiple times
      (in order to support repeating the `Dataset`) and at any time
      between the call to `Dataset.from_generator()` and the production of the
      first element from the generator. Mutating global variables or external
      state can cause undefined behavior, and we recommend that you explicitly
      cache any external state in `generator` before calling
      `Dataset.from_generator()`.
  
      Args:
        generator: A callable object that returns an object that supports the
          `iter()` protocol. If `args` is not specified, `generator` must take
          no arguments; otherwise it must take as many arguments as there are
          values in `args`.
        output_types: A nested structure of `tf.DType` objects corresponding to
          each component of an element yielded by `generator`.
        output_shapes: (Optional.) A nested structure of `tf.TensorShape`
          objects corresponding to each component of an element yielded by
          `generator`.
        args: (Optional.) A tuple of `tf.Tensor` objects that will be evaluated
          and passed to `generator` as NumPy-array arguments.
  
      Returns:
        Dataset: A `Dataset`.
      """
      if not callable(generator):
        raise TypeError("`generator` must be callable.")
      if output_shapes is None:
        output_shapes = nest.map_structure(
            lambda _: tensor_shape.TensorShape(None), output_types)
      else:
        output_shapes = nest.map_structure_up_to(
            output_types, tensor_shape.as_shape, output_shapes)
      if args is None:
        args = ()
      else:
        args = tuple(ops.convert_n_to_tensor(args, name="args"))
  
      flattened_types = [dtypes.as_dtype(dt) for dt in nest.flatten(output_types)]
      flattened_shapes = nest.flatten(output_shapes)
  
      generator_state = DatasetV2._GeneratorState(generator)
  
      def get_iterator_id_fn(unused_dummy):
        """Creates a unique `iterator_id` for each pass over the dataset.
  
        The returned `iterator_id` disambiguates between multiple concurrently
        existing iterators.
  
        Args:
          unused_dummy: Ignored value.
  
        Returns:
          A `tf.int64` tensor whose value uniquely identifies an iterator in
          `generator_state`.
        """
        return script_ops.py_func(
            generator_state.get_next_id, args, dtypes.int64, stateful=True)
  
      def generator_next_fn(iterator_id_t):
        """Generates the next element from iterator with ID `iterator_id_t`.
  
        We map this function across an infinite repetition of the
        `iterator_id_t`, and raise `StopIteration` to terminate the iteration.
  
        Args:
          iterator_id_t: A `tf.int64` tensor whose value uniquely identifies
            the iterator in `generator_state` from which to generate an element.
  
        Returns:
          A nested structure of tensors representing an element from the iterator.
        """
  
        def generator_py_func(iterator_id):
          """A `py_func` that will be called to invoke the iterator."""
          # `next()` raises `StopIteration` when there are no more
          # elements remaining to be generated.
          values = next(generator_state.get_iterator(iterator_id))
  
          # Use the same _convert function from the py_func() implementation to
          # convert the returned values to arrays early, so that we can inspect
          # their values.
          try:
            flattened_values = nest.flatten_up_to(output_types, values)
          except (TypeError, ValueError):
            raise TypeError(
                "`generator` yielded an element that did not match the expected "
                "structure. The expected structure was %s, but the yielded "
                "element was %s." % (output_types, values))
          ret_arrays = []
          for ret, dtype in zip(flattened_values, flattened_types):
            try:
              ret_arrays.append(script_ops.FuncRegistry._convert(  # pylint: disable=protected-access
                  ret, dtype=dtype.as_numpy_dtype))
            except (TypeError, ValueError):
              raise TypeError(
                  "`generator` yielded an element that could not be converted to "
                  "the expected type. The expected type was %s, but the yielded "
                  "element was %s." % (dtype.name, ret))
  
          # Additional type and shape checking to ensure that the components
          # of the generated element match the `output_types` and `output_shapes`
          # arguments.
          for (ret_array, expected_dtype, expected_shape) in zip(
              ret_arrays, flattened_types, flattened_shapes):
            if ret_array.dtype != expected_dtype.as_numpy_dtype:
              raise TypeError(
                  "`generator` yielded an element of type %s where an element "
                  "of type %s was expected." % (ret_array.dtype,
                                                expected_dtype.as_numpy_dtype))
            if not expected_shape.is_compatible_with(ret_array.shape):
              raise ValueError(
                  "`generator` yielded an element of shape %s where an element "
                  "of shape %s was expected." % (ret_array.shape, expected_shape))
  
          return ret_arrays
  
        flat_values = script_ops.py_func(
            generator_py_func, [iterator_id_t], flattened_types, stateful=True)
  
        # The `py_func()` op drops the inferred shapes, so we add them back in
        # here.
        if output_shapes is not None:
          for ret_t, shape in zip(flat_values, flattened_shapes):
            ret_t.set_shape(shape)
  
        return nest.pack_sequence_as(output_types, flat_values)
  
      def finalize_fn(iterator_id_t):
        """Releases host-side state for the iterator with ID `iterator_id_t`."""
  
        def finalize_py_func(iterator_id):
          generator_state.iterator_completed(iterator_id)
          # We return a dummy value so that the `finalize_fn` has a valid
          # signature.
          # NOTE(mrry): Explicitly create an array of `np.int64` because implicit
          # casting in `py_func()` will create an array of `np.int32` on Windows,
          # leading to a runtime error.
          return np.array(0, dtype=np.int64)
  
        return script_ops.py_func(
            finalize_py_func, [iterator_id_t], dtypes.int64, stateful=True)
  
      # This function associates each traversal of `generator` with a unique
      # iterator ID.
      def flat_map_fn(dummy_arg):
        # The `get_iterator_id_fn` gets a unique ID for the current instance of
        # of the generator.
        # The `generator_next_fn` gets the next element from the iterator with the
        # given ID, and raises StopIteration when that iterator contains no
        # more elements.
        return _GeneratorDataset(dummy_arg, get_iterator_id_fn, generator_next_fn,
                                 finalize_fn)
  
      # A single-element dataset that, each time it is evaluated, contains a
      # freshly-generated and unique (for the returned dataset) int64
      # ID that will be used to identify the appropriate Python state, which
      # is encapsulated in `generator_state`, and captured in
      # `get_iterator_id_map_fn`.
      dummy = 0
      id_dataset = Dataset.from_tensors(dummy)
  
      # A dataset that contains all of the elements generated by a
      # single iterator created from `generator`, identified by the
      # iterator ID contained in `id_dataset`. Lifting the iteration
      # into a flat_map here enables multiple repetitions and/or nested
      # versions of the returned dataset to be created, because it forces
      # the generation of a new ID for each version.
      return id_dataset.flat_map(flat_map_fn)
  
    @staticmethod
    def range(*args):
      """Creates a `Dataset` of a step-separated range of values.
  
      For example:
  
      ```python
      Dataset.range(5) == [0, 1, 2, 3, 4]
      Dataset.range(2, 5) == [2, 3, 4]
      Dataset.range(1, 5, 2) == [1, 3]
      Dataset.range(1, 5, -2) == []
      Dataset.range(5, 1) == []
      Dataset.range(5, 1, -2) == [5, 3]
      ```
  
      Args:
        *args: follows the same semantics as python's xrange.
          len(args) == 1 -> start = 0, stop = args[0], step = 1
          len(args) == 2 -> start = args[0], stop = args[1], step = 1
          len(args) == 3 -> start = args[0], stop = args[1, stop = args[2]
  
      Returns:
        Dataset: A `RangeDataset`.
  
      Raises:
        ValueError: if len(args) == 0.
      """
      return RangeDataset(*args)
  
    @staticmethod
    def zip(datasets):
      """Creates a `Dataset` by zipping together the given datasets.
  
      This method has similar semantics to the built-in `zip()` function
      in Python, with the main difference being that the `datasets`
      argument can be an arbitrary nested structure of `Dataset` objects.
      For example:
  
      ```python
      # NOTE: The following examples use `{ ... }` to represent the
      # contents of a dataset.
      a = { 1, 2, 3 }
      b = { 4, 5, 6 }
      c = { (7, 8), (9, 10), (11, 12) }
      d = { 13, 14 }
  
      # The nested structure of the `datasets` argument determines the
      # structure of elements in the resulting dataset.
      Dataset.zip((a, b)) == { (1, 4), (2, 5), (3, 6) }
      Dataset.zip((b, a)) == { (4, 1), (5, 2), (6, 3) }
  
      # The `datasets` argument may contain an arbitrary number of
      # datasets.
      Dataset.zip((a, b, c)) == { (1, 4, (7, 8)),
                                  (2, 5, (9, 10)),
                                  (3, 6, (11, 12)) }
  
      # The number of elements in the resulting dataset is the same as
      # the size of the smallest dataset in `datasets`.
      Dataset.zip((a, d)) == { (1, 13), (2, 14) }
      ```
  
      Args:
        datasets: A nested structure of datasets.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return ZipDataset(datasets)
  
    def concatenate(self, dataset):
      """Creates a `Dataset` by concatenating given dataset with this dataset.
  
      ```python
      # NOTE: The following examples use `{ ... }` to represent the
      # contents of a dataset.
      a = { 1, 2, 3 }
      b = { 4, 5, 6, 7 }
  
      # Input dataset and dataset to be concatenated should have same
      # nested structures and output types.
      # c = { (8, 9), (10, 11), (12, 13) }
      # d = { 14.0, 15.0, 16.0 }
      # a.concatenate(c) and a.concatenate(d) would result in error.
  
      a.concatenate(b) == { 1, 2, 3, 4, 5, 6, 7 }
      ```
  
      Args:
        dataset: `Dataset` to be concatenated.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return ConcatenateDataset(self, dataset)
  
    def prefetch(self, buffer_size):
      """Creates a `Dataset` that prefetches elements from this dataset.
  
      Args:
        buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
          maximum number of elements that will be buffered when prefetching.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return PrefetchDataset(self, buffer_size)
  
    @staticmethod
    def list_files(file_pattern, shuffle=None, seed=None):
      """A dataset of all files matching one or more glob patterns.
  
      NOTE: The default behavior of this method is to return filenames in
      a non-deterministic random shuffled order. Pass a `seed` or `shuffle=False`
      to get results in a deterministic order.
  
      Example:
        If we had the following files on our filesystem:
          - /path/to/dir/a.txt
          - /path/to/dir/b.py
          - /path/to/dir/c.py
        If we pass "/path/to/dir/*.py" as the directory, the dataset would
        produce:
          - /path/to/dir/b.py
          - /path/to/dir/c.py
  
      Args:
        file_pattern: A string, a list of strings, or a `tf.Tensor` of string type
          (scalar or vector), representing the filename glob (i.e. shell wildcard)
          pattern(s) that will be matched.
        shuffle: (Optional.) If `True`, the file names will be shuffled randomly.
          Defaults to `True`.
        seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random
          seed that will be used to create the distribution. See
          `tf.set_random_seed` for behavior.
  
      Returns:
       Dataset: A `Dataset` of strings corresponding to file names.
      """
      with ops.name_scope("list_files"):
        if shuffle is None:
          shuffle = True
        file_pattern = ops.convert_to_tensor(
            file_pattern, dtype=dtypes.string, name="file_pattern")
        matching_files = gen_io_ops.matching_files(file_pattern)
  
        # Raise an exception if `file_pattern` does not match any files.
        condition = math_ops.greater(array_ops.shape(matching_files)[0], 0,
                                     name="match_not_empty")
  
        message = math_ops.add(
            "No files matched pattern: ",
            string_ops.reduce_join(file_pattern, separator=", "), name="message")
  
        assert_not_empty = control_flow_ops.Assert(
            condition, [message], summarize=1, name="assert_not_empty")
        with ops.control_dependencies([assert_not_empty]):
          matching_files = array_ops.identity(matching_files)
  
        dataset = Dataset.from_tensor_slices(matching_files)
        if shuffle:
          # NOTE(mrry): The shuffle buffer size must be greater than zero, but the
          # list of files might be empty.
          buffer_size = math_ops.maximum(
              array_ops.shape(matching_files, out_type=dtypes.int64)[0], 1)
          dataset = dataset.shuffle(buffer_size, seed=seed)
        return dataset
  
    def repeat(self, count=None):
      """Repeats this dataset `count` times.
  
      NOTE: If this dataset is a function of global state (e.g. a random number
      generator), then different repetitions may produce different elements.
  
      Args:
        count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
          number of times the dataset should be repeated. The default behavior
          (if `count` is `None` or `-1`) is for the dataset be repeated
          indefinitely.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return RepeatDataset(self, count)
  
    def _enumerate(self, start=0):
  
      max_value = np.iinfo(dtypes.int64.as_numpy_dtype).max
      return Dataset.zip((Dataset.range(start, max_value), self))
  
    def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None):
      """Randomly shuffles the elements of this dataset.
  
      This dataset fills a buffer with `buffer_size` elements, then randomly
      samples elements from this buffer, replacing the selected elements with new
      elements. For perfect shuffling, a buffer size greater than or equal to the
      full size of the dataset is required.
  
      Args:
        buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
          number of elements from this dataset from which the new
          dataset will sample.
        seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
          random seed that will be used to create the distribution. See
          `tf.set_random_seed` for behavior.
        reshuffle_each_iteration: (Optional.) A boolean, which if true indicates
          that the dataset should be pseudorandomly reshuffled each time it is
          iterated over. (Defaults to `True`.)
  
      Returns:
        Dataset: A `Dataset`.
      """
      return ShuffleDataset(self, buffer_size, seed, reshuffle_each_iteration)
  
    def cache(self, filename=""):
      """Caches the elements in this dataset.
  
      Args:
        filename: A `tf.string` scalar `tf.Tensor`, representing the name of a
          directory on the filesystem to use for caching tensors in this Dataset.
          If a filename is not provided, the dataset will be cached in memory.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return CacheDataset(self, filename)
  
    def take(self, count):
      """Creates a `Dataset` with at most `count` elements from this dataset.
  
      Args:
        count: A `tf.int64` scalar `tf.Tensor`, representing the number of
          elements of this dataset that should be taken to form the new dataset.
          If `count` is -1, or if `count` is greater than the size of this
          dataset, the new dataset will contain all elements of this dataset.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return TakeDataset(self, count)
  
    def skip(self, count):
      """Creates a `Dataset` that skips `count` elements from this dataset.
  
      Args:
        count: A `tf.int64` scalar `tf.Tensor`, representing the number
          of elements of this dataset that should be skipped to form the
          new dataset.  If `count` is greater than the size of this
          dataset, the new dataset will contain no elements.  If `count`
          is -1, skips the entire dataset.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return SkipDataset(self, count)
  
    def batch(self, batch_size, drop_remainder=False):
      """Combines consecutive elements of this dataset into batches.
  
      The tensors in the resulting element will have an additional outer
      dimension, which will be `batch_size` (or `N % batch_size` for the last
      element if `batch_size` does not divide the number of input elements `N`
      evenly and `drop_remainder` is `False`). If your program depends on the
      batches having the same outer dimension, you should set the `drop_remainder`
      argument to `True` to prevent the smaller batch from being produced.
  
      Args:
        batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
          consecutive elements of this dataset to combine in a single batch.
        drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
          whether the last batch should be dropped in the case it has fewer than
          `batch_size` elements; the default behavior is not to drop the smaller
          batch.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return BatchDataset(self, batch_size, drop_remainder)
  
    def padded_batch(self,
                     batch_size,
                     padded_shapes,
                     padding_values=None,
                     drop_remainder=False):
      """Combines consecutive elements of this dataset into padded batches.
  
      This transformation combines multiple consecutive elements of the input
      dataset into a single element.
  
      Like `tf.data.Dataset.batch`, the tensors in the resulting element will
      have an additional outer dimension, which will be `batch_size` (or
      `N % batch_size` for the last element if `batch_size` does not divide the
      number of input elements `N` evenly and `drop_remainder` is `False`). If
      your program depends on the batches having the same outer dimension, you
      should set the `drop_remainder` argument to `True` to prevent the smaller
      batch from being produced.
  
      Unlike `tf.data.Dataset.batch`, the input elements to be batched may have
      different shapes, and this transformation will pad each component to the
      respective shape in `padding_shapes`. The `padding_shapes` argument
      determines the resulting shape for each dimension of each component in an
      output element:
  
      * If the dimension is a constant (e.g. `tf.Dimension(37)`), the component
        will be padded out to that length in that dimension.
      * If the dimension is unknown (e.g. `tf.Dimension(None)`), the component
        will be padded out to the maximum length of all elements in that
        dimension.
  
      See also `tf.data.experimental.dense_to_sparse_batch`, which combines
      elements that may have different shapes into a `tf.SparseTensor`.
  
      Args:
        batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
          consecutive elements of this dataset to combine in a single batch.
        padded_shapes: A nested structure of `tf.TensorShape` or
          `tf.int64` vector tensor-like objects representing the shape
          to which the respective component of each input element should
          be padded prior to batching. Any unknown dimensions
          (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
          tensor-like object) will be padded to the maximum size of that
          dimension in each batch.
        padding_values: (Optional.) A nested structure of scalar-shaped
          `tf.Tensor`, representing the padding values to use for the
          respective components.  Defaults are `0` for numeric types and
          the empty string for string types.
        drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
          whether the last batch should be dropped in the case it has fewer than
          `batch_size` elements; the default behavior is not to drop the smaller
          batch.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values,
                                drop_remainder)
  
    def map(self, map_func, num_parallel_calls=None):
      """Maps `map_func` across the elements of this dataset.
  
      This transformation applies `map_func` to each element of this dataset, and
      returns a new dataset containing the transformed elements, in the same
      order as they appeared in the input.
  
      For example:
  
      ```python
      # NOTE: The following examples use `{ ... }` to represent the
      # contents of a dataset.
      a = { 1, 2, 3, 4, 5 }
  
      a.map(lambda x: x + 1) = { 2, 3, 4, 5, 6 }
      ```
  
      The input signature of `map_func` is determined by the structure of each
      element in this dataset. For example:
  
      ```python
      # Each element is a `tf.Tensor` object.
      a = { 1, 2, 3, 4, 5 }
      # `map_func` takes a single argument of type `tf.Tensor` with the same
      # shape and dtype.
      result = a.map(lambda x: ...)
  
      # Each element is a tuple containing two `tf.Tensor` objects.
      b = { (1, "foo"), (2, "bar"), (3, "baz") }
      # `map_func` takes two arguments of type `tf.Tensor`.
      result = b.map(lambda x_int, y_str: ...)
  
      # Each element is a dictionary mapping strings to `tf.Tensor` objects.
      c = { {"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}, {"a": 3, "b": "baz"} }
      # `map_func` takes a single argument of type `dict` with the same keys as
      # the elements.
      result = c.map(lambda d: ...)
      ```
  
      The value or values returned by `map_func` determine the structure of each
      element in the returned dataset.
  
      ```python
      # `map_func` returns a scalar `tf.Tensor` of type `tf.float32`.
      def f(...):
        return tf.constant(37.0)
      result = dataset.map(f)
      result.output_classes == tf.Tensor
      result.output_types == tf.float32
      result.output_shapes == []  # scalar
  
      # `map_func` returns two `tf.Tensor` objects.
      def g(...):
        return tf.constant(37.0), tf.constant(["Foo", "Bar", "Baz"])
      result = dataset.map(g)
      result.output_classes == (tf.Tensor, tf.Tensor)
      result.output_types == (tf.float32, tf.string)
      result.output_shapes == ([], [3])
  
      # Python primitives, lists, and NumPy arrays are implicitly converted to
      # `tf.Tensor`.
      def h(...):
        return 37.0, ["Foo", "Bar", "Baz"], np.array([1.0, 2.0] dtype=np.float64)
      result = dataset.map(h)
      result.output_classes == (tf.Tensor, tf.Tensor, tf.Tensor)
      result.output_types == (tf.float32, tf.string, tf.float64)
      result.output_shapes == ([], [3], [2])
  
      # `map_func` can return nested structures.
      def i(...):
        return {"a": 37.0, "b": [42, 16]}, "foo"
      result.output_classes == ({"a": tf.Tensor, "b": tf.Tensor}, tf.Tensor)
      result.output_types == ({"a": tf.float32, "b": tf.int32}, tf.string)
      result.output_shapes == ({"a": [], "b": [2]}, [])
      ```
  
      In addition to `tf.Tensor` objects, `map_func` can accept as arguments and
      return `tf.SparseTensor` objects.
  
      Args:
        map_func: A function mapping a nested structure of tensors (having
          shapes and types defined by `self.output_shapes` and
         `self.output_types`) to another nested structure of tensors.
        num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
          representing the number elements to process in parallel. If not
          specified, elements will be processed sequentially. If the value
          `tf.data.experimental.AUTOTUNE` is used, then the number of parallel
          calls is set dynamically based on available CPU.
  
      Returns:
        Dataset: A `Dataset`.
      """
      if num_parallel_calls is None:
        return MapDataset(self, map_func, preserve_cardinality=True)
      else:
        return ParallelMapDataset(
            self, map_func, num_parallel_calls, preserve_cardinality=True)
  
    def flat_map(self, map_func):
      """Maps `map_func` across this dataset and flattens the result.
  
      Use `flat_map` if you want to make sure that the order of your dataset
      stays the same. For example, to flatten a dataset of batches into a
      dataset of their elements:
  
      ```python
      # NOTE: The following examples use `{ ... }` to represent the
      # contents of a dataset. '[...]' represents a tensor.
      a = {[1,2,3,4,5], [6,7,8,9], [10]}
  
      a.flat_map(lambda x: Dataset.from_tensor_slices(x)) ==
        {[1,2,3,4,5,6,7,8,9,10]}
      ```
  
      `tf.data.Dataset.interleave()` is a generalization of `flat_map`, since
      `flat_map` produces the same output as
      `tf.data.Dataset.interleave(cycle_length=1)`
  
      Args:
        map_func: A function mapping a nested structure of tensors (having shapes
          and types defined by `self.output_shapes` and `self.output_types`) to a
          `Dataset`.
  
      Returns:
        Dataset: A `Dataset`.
      """
      return FlatMapDataset(self, map_func)
  
    def interleave(self,
                   map_func,
                   cycle_length,
                   block_length=1,
                   num_parallel_calls=None):
      """Maps `map_func` across this dataset, and interleaves the results.
  
      For example, you can use `Dataset.interleave()` to process many input files
      concurrently:
  
      ```python
      # Preprocess 4 files concurrently, and interleave blocks of 16 records from
      # each file.
      filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ...]
      dataset = (Dataset.from_tensor_slices(filenames)
                 .interleave(lambda x:
                     TextLineDataset(x).map(parse_fn, num_parallel_calls=1),
                     cycle_length=4, block_length=16))
      ```
  
      The `cycle_length` and `block_length` arguments control the order in which
      elements are produced. `cycle_length` controls the number of input elements
      that are processed concurrently. If you set `cycle_length` to 1, this
      transformation will handle one input element at a time, and will produce
      identical results to `tf.data.Dataset.flat_map`. In general,
      this transformation will apply `map_func` to `cycle_length` input elements,
      open iterators on the returned `Dataset` objects, and cycle through them
      producing `block_length` consecutive elements from each iterator, and
      consuming the next input element each time it reaches the end of an
      iterator.
  
      For example:
  
      ```python
      # NOTE: The following examples use `{ ... }` to represent the
      # contents of a dataset.
      a = { 1, 2, 3, 4, 5 }
  
      # NOTE: New lines indicate "block" boundaries.
      a.interleave(lambda x: Dataset.from_tensors(x).repeat(6),
                   cycle_length=2, block_length=4) == {
          1, 1, 1, 1,
          2, 2, 2, 2,
          1, 1,
          2, 2,
          3, 3, 3, 3,
          4, 4, 4, 4,
          3, 3,
          4, 4,
          5, 5, 5, 5,
          5, 5,
      }
      ```
  
      NOTE: The order of elements yielded by this transformation is
      deterministic, as long as `map_func` is a pure function. If
      `map_func` contains any stateful operations, the order in which
      that state is accessed is undefined.
  
      Args:
        map_func: A function mapping a nested structure of tensors (having shapes
          and types defined by `self.output_shapes` and `self.output_types`) to a
          `Dataset`.
        cycle_length: The number of elements from this dataset that will be
          processed concurrently.
        block_length: The number of consecutive elements to produce from each
          input element before cycling to another input element.
        num_parallel_calls: (Optional.) If specified, the implementation creates
          a threadpool, which is used to fetch inputs from cycle elements
          asynchronously and in parallel. The default behavior is to fetch inputs
          from cycle elements synchronously with no parallelism. If the value
          `tf.data.experimental.AUTOTUNE` is used, then the number of parallel
          calls is set dynamically based on available CPU.
  
      Returns:
        Dataset: A `Dataset`.
      """
      if num_parallel_calls is None:
        return InterleaveDataset(self, map_func, cycle_length, block_length)
      else:
        return ParallelInterleaveDataset(self, map_func, cycle_length,
                                         block_length, num_parallel_calls)
  
    def filter(self, predicate):
      """Filters this dataset according to `predicate`.
  
      Args:
        predicate: A function mapping a nested structure of tensors (having shapes
          and types defined by `self.output_shapes` and `self.output_types`) to a
          scalar `tf.bool` tensor.
  
      Returns:
        Dataset: The `Dataset` containing the elements of this dataset for which
            `predicate` is `True`.
      """
      return FilterDataset(self, predicate)
  
    def apply(self, transformation_func):
      """Applies a transformation function to this dataset.
  
      `apply` enables chaining of custom `Dataset` transformations, which are
      represented as functions that take one `Dataset` argument and return a
      transformed `Dataset`.
  
      For example:
  
      ```
      dataset = (dataset.map(lambda x: x ** 2)
                 .apply(group_by_window(key_func, reduce_func, window_size))
                 .map(lambda x: x ** 3))
      ```
  
      Args:
        transformation_func: A function that takes one `Dataset` argument and
          returns a `Dataset`.
  
      Returns:
        Dataset: The `Dataset` returned by applying `transformation_func` to this
            dataset.
      """
      dataset = transformation_func(self)
      if not isinstance(dataset, DatasetV2):
        raise TypeError("`transformation_func` must return a Dataset.")
      dataset._input_datasets = [self]  # pylint: disable=protected-access
      return dataset
  
    def window(self, size, shift=None, stride=1, drop_remainder=False):
      """Combines input elements into a dataset of windows.
  
      Each window is a dataset itself and contains `size` elements (or
      possibly fewer if there are not enough input elements to fill the window
      and `drop_remainder` evaluates to false).
  
      The `stride` argument determines the stride of the input elements,
      and the `shift` argument determines the shift of the window.
  
      For example:
      - `tf.data.Dataset.range(7).window(2)` produces
        `{{0, 1}, {2, 3}, {4, 5}, {6}}`
      - `tf.data.Dataset.range(7).window(3, 2, 1, True)` produces
        `{{0, 1, 2}, {2, 3, 4}, {4, 5, 6}}`
      - `tf.data.Dataset.range(7).window(3, 1, 2, True)` produces
        `{{0, 2, 4}, {1, 3, 5}, {2, 4, 6}}`
  
      Args:
        size: A `tf.int64` scalar `tf.Tensor`, representing the number of elements
          of the input dataset to combine into a window.
        shift: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
          forward shift of the sliding window in each iteration. Defaults to
          `size`.
        stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
          stride of the input elements in the sliding window.
        drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
          whether a window should be dropped in case its size is smaller than
          `window_size`.
  
      Returns:
        Dataset: A `Dataset` of windows, each of which is a nested `Dataset` with
          the same structure as this dataset, but a finite subsequence of its
          elements.
      """
      if shift is None:
        shift = size
      return WindowDataset(self, size, shift, stride, drop_remainder)
  
    def reduce(self, initial_state, reduce_func):
      """Reduces the input dataset to a single element.
  
      The transformation calls `reduce_func` successively on every element of
      the input dataset until the dataset is exhausted, aggregating information in
      its internal state. The `initial_state` argument is used for the initial
      state and the final state is returned as the result.
  
      For example:
      - `tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, _: x + 1)`
        produces `5`
      - `tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, y: x + y)`
        produces `10`
  
      Args:
        initial_state: A nested structure of tensors, representing the initial
          state of the transformation.
        reduce_func: A function that maps `(old_state, input_element)` to
          `new_state`. It must take two arguments and return a nested structure
          of tensors. The structure of `new_state` must match the structure of
          `initial_state`.
  
      Returns:
        A nested structure of `tf.Tensor` objects, corresponding to the final
        state of the transformation.
  
      """
  
      with ops.name_scope("initial_state"):
        # Convert any `SparseTensorValue`s to `SparseTensor`s and all other
        # values to tensors.
        initial_state = nest.pack_sequence_as(initial_state, [
            sparse_tensor_lib.SparseTensor.from_value(t)
            if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(
                t, name="component_%d" % i)
            for i, t in enumerate(nest.flatten(initial_state))
        ])
  
      # Compute initial values for the state classes, shapes and types based on
      # the initial state.
      state_structure = structure_lib.Structure.from_value(initial_state)
  
      # Iteratively rerun the reduce function until reaching a fixed point on
      # `state_structure`.
      need_to_rerun = True
      while need_to_rerun:
  
        wrapped_func = StructuredFunctionWrapper(
            reduce_func,
            "reduce()",
            input_structure=structure_lib.NestedStructure(
                (state_structure, self._element_structure)),
            add_to_graph=False)
  
        # Extract and validate class information from the returned values.
        output_classes = wrapped_func.output_classes
        state_classes = state_structure._to_legacy_output_classes()  # pylint: disable=protected-access
        for new_state_class, state_class in zip(
            nest.flatten(output_classes), nest.flatten(state_classes)):
          if not issubclass(new_state_class, state_class):
            raise TypeError(
                "The element classes for the new state must match the initial "
                "state. Expected %s; got %s." % (state_classes,
                                                 wrapped_func.output_classes))
  
        # Extract and validate type information from the returned values.
        output_types = wrapped_func.output_types
        state_types = state_structure._to_legacy_output_types()  # pylint: disable=protected-access
        for new_state_type, state_type in zip(
            nest.flatten(output_types), nest.flatten(state_types)):
          if new_state_type != state_type:
            raise TypeError(
                "The element types for the new state must match the initial "
                "state. Expected %s; got %s." % (state_types,
                                                 wrapped_func.output_types))
  
        # Extract shape information from the returned values.
        output_shapes = wrapped_func.output_shapes
        state_shapes = state_structure._to_legacy_output_shapes()  # pylint: disable=protected-access
        flat_state_shapes = nest.flatten(state_shapes)
        flat_new_state_shapes = nest.flatten(output_shapes)
        weakened_state_shapes = [
            original.most_specific_compatible_shape(new)
            for original, new in zip(flat_state_shapes, flat_new_state_shapes)
        ]
  
        need_to_rerun = False
        for original_shape, weakened_shape in zip(flat_state_shapes,
                                                  weakened_state_shapes):
          if original_shape.ndims is not None and (
              weakened_shape.ndims is None or
              original_shape.as_list() != weakened_shape.as_list()):
            need_to_rerun = True
            break
  
        if need_to_rerun:
          # TODO(b/110122868): Support a "most specific compatible structure"
          # method for combining structures, to avoid using legacy structures
          # here.
          state_structure = structure_lib.convert_legacy_structure(
              state_types,
              nest.pack_sequence_as(state_shapes, weakened_state_shapes),
              state_classes)
  
      reduce_func = wrapped_func.function
      reduce_func.add_to_graph(ops.get_default_graph())
  
      # pylint: disable=protected-access
      return state_structure._from_compatible_tensor_list(
          gen_dataset_ops.reduce_dataset(
              self._as_variant_tensor(),
              state_structure._to_tensor_list(initial_state),
              reduce_func.captured_inputs,
              f=reduce_func,
              output_shapes=state_structure._flat_shapes,
              output_types=state_structure._flat_types))
  
    def with_options(self, options):
      """Returns a new `tf.data.Dataset` with the given options set.
  
      The options are "global" in the sense they apply to the entire dataset.
      If options are set multiple times, they are merged as long as different
      options do not use different non-default values.
  
      Args:
        options: A `tf.data.Options` that identifies the options the use.
  
      Returns:
        Dataset: A `Dataset` with the given options.
  
      Raises:
        ValueError: when an option is set more than once to a non-default value
      """
      return _OptionsDataset(self, options)
  
  
  @tf_export(v1=["data.Dataset"])
  class DatasetV1(DatasetV2):
    """Represents a potentially large set of elements.
  
    A `Dataset` can be used to represent an input pipeline as a
    collection of elements (nested structures of tensors) and a "logical
    plan" of transformations that act on those elements.
    """
  
    def __init__(self):  # pylint: disable=useless-super-delegation
      super(DatasetV1, self).__init__()
  
    @deprecation.deprecated(
        None, "Use `for ... in dataset:` to iterate over a dataset. If using "
        "`tf.estimator`, return the `Dataset` object directly from your input "
        "function. As a last resort, you can use "
        "`tf.compat.v1.data.make_one_shot_iterator(dataset)`.")
    def make_one_shot_iterator(self):
      """Creates an `Iterator` for enumerating the elements of this dataset.
  
      Note: The returned iterator will be initialized automatically.
      A "one-shot" iterator does not currently support re-initialization.
  
      Returns:
        An `Iterator` over the elements of this dataset.
      """
      if context.executing_eagerly():
        return iterator_ops.EagerIterator(self)
  
      _ensure_same_dataset_graph(self)
      graph_level_seed, op_level_seed = core_random_seed.get_seed(None)
  
      # NOTE(mrry): We capture by value here to ensure that `_make_dataset()` is
      # a 0-argument function.
      @function.Defun(capture_by_value=True)
      def _make_dataset():
        """Factory function for a dataset."""
        # NOTE(mrry): `Defun` does not capture the graph-level seed from the
        # enclosing graph, so if a graph-level seed is present we set the local
        # graph seed based on a combination of the graph- and op-level seeds.
        if graph_level_seed is not None:
          assert op_level_seed is not None
          core_random_seed.set_random_seed(
              (graph_level_seed + 87654321 * op_level_seed) % (2 ** 63 - 1))
  
        dataset = self._apply_options()
        return dataset._as_variant_tensor()  # pylint: disable=protected-access
  
      try:
        _make_dataset.add_to_graph(ops.get_default_graph())
      except ValueError as err:
        if "Cannot capture a stateful node" in str(err):
          raise ValueError(
              "Failed to create a one-shot iterator for a dataset. "
              "`Dataset.make_one_shot_iterator()` does not support datasets that "
              "capture stateful objects, such as a `Variable` or `LookupTable`. "
              "In these cases, use `Dataset.make_initializable_iterator()`. "
              "(Original error: %s)" % err)
        else:
          six.reraise(ValueError, err)
  
      return iterator_ops.Iterator(
          gen_dataset_ops.one_shot_iterator(
              dataset_factory=_make_dataset, **flat_structure(self)),
          None, self.output_types, self.output_shapes, self.output_classes)
  
    @deprecation.deprecated(
        None, "Use `for ... in dataset:` to iterate over a dataset. If using "
        "`tf.estimator`, return the `Dataset` object directly from your input "
        "function. As a last resort, you can use "
        "`tf.compat.v1.data.make_initializable_iterator(dataset)`.")
    def make_initializable_iterator(self, shared_name=None):
      """Creates an `Iterator` for enumerating the elements of this dataset.
  
      Note: The returned iterator will be in an uninitialized state,
      and you must run the `iterator.initializer` operation before using it:
  
      ```python
      dataset = ...
      iterator = dataset.make_initializable_iterator()
      # ...
      sess.run(iterator.initializer)
      ```
  
      Args:
        shared_name: (Optional.) If non-empty, the returned iterator will be
          shared under the given name across multiple sessions that share the
          same devices (e.g. when using a remote server).
  
      Returns:
        An `Iterator` over the elements of this dataset.
  
      Raises:
        RuntimeError: If eager execution is enabled.
      """
      if context.executing_eagerly():
        raise RuntimeError(
            "dataset.make_initializable_iterator is not supported when eager "
            "execution is enabled.")
      _ensure_same_dataset_graph(self)
      dataset = self._apply_options()
      if shared_name is None:
        shared_name = ""
      if compat.forward_compatible(2018, 8, 3):
        iterator_resource = gen_dataset_ops.iterator_v2(
            container="", shared_name=shared_name, **flat_structure(self))
      else:
        iterator_resource = gen_dataset_ops.iterator(
            container="", shared_name=shared_name, **flat_structure(self))
      with ops.colocate_with(iterator_resource):
        initializer = gen_dataset_ops.make_iterator(
            dataset._as_variant_tensor(),  # pylint: disable=protected-access
            iterator_resource)
      return iterator_ops.Iterator(iterator_resource, initializer,
                                   dataset.output_types, dataset.output_shapes,
                                   dataset.output_classes)
  
    @property
    def _element_structure(self):
      # TODO(b/110122868): Remove this override once all `Dataset` instances
      # implement `element_structure`.
      return structure_lib.convert_legacy_structure(
          self.output_types, self.output_shapes, self.output_classes)
  
    @staticmethod
    @functools.wraps(DatasetV2.from_tensors)
    def from_tensors(tensors):
      return DatasetV1Adapter(DatasetV2.from_tensors(tensors))
  
    @staticmethod
    @functools.wraps(DatasetV2.from_tensor_slices)
    def from_tensor_slices(tensors):
      return DatasetV1Adapter(DatasetV2.from_tensor_slices(tensors))
  
    @staticmethod
    @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensor_slices()`.")
    def from_sparse_tensor_slices(sparse_tensor):
      """Splits each rank-N `tf.SparseTensor` in this dataset row-wise.
  
      Args:
        sparse_tensor: A `tf.SparseTensor`.
  
      Returns:
        Dataset: A `Dataset` of rank-(N-1) sparse tensors.
      """
      return DatasetV1Adapter(SparseTensorSliceDataset(sparse_tensor))
  
    @staticmethod
    @functools.wraps(DatasetV2.from_generator)
    def from_generator(generator, output_types, output_shapes=None, args=None):
      return DatasetV1Adapter(DatasetV2.from_generator(
          generator, output_types, output_shapes, args))
  
    @staticmethod
    @functools.wraps(DatasetV2.range)
    def range(*args):
      return DatasetV1Adapter(DatasetV2.range(*args))
  
    @staticmethod
    @functools.wraps(DatasetV2.zip)
    def zip(datasets):
      return DatasetV1Adapter(DatasetV2.zip(datasets))
  
    @functools.wraps(DatasetV2.concatenate)
    def concatenate(self, dataset):
      return DatasetV1Adapter(super(DatasetV1, self).concatenate(dataset))
  
    @functools.wraps(DatasetV2.prefetch)
    def prefetch(self, buffer_size):
      return DatasetV1Adapter(super(DatasetV1, self).prefetch(buffer_size))
  
    @staticmethod
    @functools.wraps(DatasetV2.list_files)
    def list_files(file_pattern, shuffle=None, seed=None):
      return DatasetV1Adapter(DatasetV2.list_files(file_pattern, shuffle, seed))
  
    @functools.wraps(DatasetV2.repeat)
    def repeat(self, count=None):
      return DatasetV1Adapter(super(DatasetV1, self).repeat(count))
  
    @functools.wraps(DatasetV2.shuffle)
    def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None):
      return DatasetV1Adapter(super(DatasetV1, self).shuffle(
          buffer_size, seed, reshuffle_each_iteration))
  
    @functools.wraps(DatasetV2.cache)
    def cache(self, filename=""):
      return DatasetV1Adapter(super(DatasetV1, self).cache(filename))
  
    @functools.wraps(DatasetV2.take)
    def take(self, count):
      return DatasetV1Adapter(super(DatasetV1, self).take(count))
  
    @functools.wraps(DatasetV2.skip)
    def skip(self, count):
      return DatasetV1Adapter(super(DatasetV1, self).skip(count))
  
    @deprecation.deprecated(
        None, "Use `dataset.apply(tf.data.experimental.filter_for_shard(...))`.")
    def shard(self, num_shards, index):
      """Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
  
      This dataset operator is very useful when running distributed training, as
      it allows each worker to read a unique subset.
  
      When reading a single input file, you can skip elements as follows:
  
      ```python
      d = tf.data.TFRecordDataset(FLAGS.input_file)
      d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
      d = d.repeat(FLAGS.num_epochs)
      d = d.shuffle(FLAGS.shuffle_buffer_size)
      d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads)
      ```
  
      Important caveats:
  
      - Be sure to shard before you use any randomizing operator (such as
        shuffle).
      - Generally it is best if the shard operator is used early in the dataset
        pipeline. For example, when reading from a set of TFRecord files, shard
        before converting the dataset to input samples. This avoids reading every
        file on every worker. The following is an example of an efficient
        sharding strategy within a complete pipeline:
  
      ```python
      d = Dataset.list_files(FLAGS.pattern)
      d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
      d = d.repeat(FLAGS.num_epochs)
      d = d.shuffle(FLAGS.shuffle_buffer_size)
      d = d.interleave(tf.data.TFRecordDataset,
                       cycle_length=FLAGS.num_readers, block_length=1)
      d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads)
      ```
  
      Args:
        num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of
          shards operating in parallel.
        index: A `tf.int64` scalar `tf.Tensor`, representing the worker index.
  
      Returns:
        Dataset: A `Dataset`.
  
      Raises:
        ValueError: if `num_shards` or `index` are illegal values. Note: error
          checking is done on a best-effort basis, and errors aren't guaranteed
          to be caught upon dataset creation. (e.g. providing in a placeholder
          tensor bypasses the early checking, and will instead result in an error
          during a session.run call.)
      """
      return self.apply(filter_for_shard_ops.filter_for_shard(num_shards, index))
  
    @functools.wraps(DatasetV2.batch)
    def batch(self, batch_size, drop_remainder=False):
      return DatasetV1Adapter(super(DatasetV1, self).batch(
          batch_size, drop_remainder))
  
    @functools.wraps(DatasetV2.padded_batch)
    def padded_batch(self,
                     batch_size,
                     padded_shapes,
                     padding_values=None,
                     drop_remainder=False):
      return DatasetV1Adapter(super(DatasetV1, self).padded_batch(
          batch_size, padded_shapes, padding_values, drop_remainder))
  
    @functools.wraps(DatasetV2.map)
    def map(self, map_func, num_parallel_calls=None):
      if num_parallel_calls is None:
        return DatasetV1Adapter(
            MapDataset(self, map_func, preserve_cardinality=False))
      else:
        return DatasetV1Adapter(
            ParallelMapDataset(
                self, map_func, num_parallel_calls, preserve_cardinality=False))
  
    @functools.wraps(DatasetV2.flat_map)
    def flat_map(self, map_func):
      return DatasetV1Adapter(super(DatasetV1, self).flat_map(map_func))
  
    @functools.wraps(DatasetV2.interleave)
    def interleave(self,
                   map_func,
                   cycle_length,
                   block_length=1,
                   num_parallel_calls=None):
      return DatasetV1Adapter(super(DatasetV1, self).interleave(
          map_func, cycle_length, block_length, num_parallel_calls))
  
    @functools.wraps(DatasetV2.filter)
    def filter(self, predicate):
      return DatasetV1Adapter(super(DatasetV1, self).filter(predicate))
  
    @functools.wraps(DatasetV2.apply)
    def apply(self, transformation_func):
      return DatasetV1Adapter(super(DatasetV1, self).apply(transformation_func))
  
    @functools.wraps(DatasetV2.window)
    def window(self, size, shift=None, stride=1, drop_remainder=False):
      return DatasetV1Adapter(super(DatasetV1, self).window(
          size, shift, stride, drop_remainder))
  
    @functools.wraps(DatasetV2.with_options)
    def with_options(self, options):
      return DatasetV1Adapter(super(DatasetV1, self).with_options(options))
  
  
  # TODO(b/119044825): Until all `tf.data` unit tests are converted to V2, keep
  # this alias in place.
  Dataset = DatasetV1
  
  
  class DatasetV1Adapter(DatasetV1):
    """Wraps a V2 `Dataset` object in the `tf.compat.v1.data.Dataset` API."""
  
    def __init__(self, dataset):
      super(DatasetV1Adapter, self).__init__()
      self._dataset = dataset
  
    def _as_variant_tensor(self):
      return self._dataset._as_variant_tensor()  # pylint: disable=protected-access
  
    def _has_captured_ref(self):
      return self._dataset._has_captured_ref()  # pylint: disable=protected-access
  
    def _inputs(self):
      return self._dataset._inputs()  # pylint: disable=protected-access
  
    def options(self):
      return self._dataset.options()
  
    @property
    def _element_structure(self):
      return self._dataset._element_structure  # pylint: disable=protected-access
  
    def __iter__(self):
      return iter(self._dataset)
  
  
  def _ensure_same_dataset_graph(dataset):
    """Walks the dataset graph to ensure all datasets come from the same graph."""
    current_graph = ops.get_default_graph()
    bfs_q = Queue.Queue()
    bfs_q.put(dataset)  # pylint: disable=protected-access
    visited = []
    while not bfs_q.empty():
      ds = bfs_q.get()
      visited.append(ds)
      ds_graph = ds._graph  # pylint: disable=protected-access
      if current_graph != ds_graph:
        logging.warning("The graph (" + str(current_graph) + ") of the iterator "
                        "is different from the graph (" + str(ds_graph) + ") "
                        "the dataset: " + str(ds) + " was created in. "
                        "If you are using the Estimator API, make sure that no "
                        "part of the dataset returned by the `input_fn` function "
                        "is defined outside the `input_fn` function."
                        "Please ensure that all datasets in the pipeline are "
                        "created in the same graph as the iterator. NOTE: This "
                        "warning will become an error in future versions of "
                        "TensorFlow.")
      for input_ds in ds._inputs():  # pylint: disable=protected-access
        if input_ds not in visited:
          bfs_q.put(input_ds)
  
  
  @tf_export(v1=["data.make_one_shot_iterator"])
  def make_one_shot_iterator(dataset):
    """Creates a `tf.data.Iterator` for enumerating the elements of a dataset.
  
    Note: The returned iterator will be initialized automatically.
    A "one-shot" iterator does not support re-initialization.
  
    Args:
      dataset: A `tf.data.Dataset`.
  
    Returns:
      A `tf.data.Iterator` over the elements of this dataset.
    """
    try:
      # Call the defined `make_one_shot_iterator()` if there is one, because some
      # datasets (e.g. for prefetching) override its behavior.
      return dataset.make_one_shot_iterator()
    except AttributeError:
      return DatasetV1Adapter(dataset).make_one_shot_iterator()
  
  
  @tf_export(v1=["data.make_initializable_iterator"])
  def make_initializable_iterator(dataset):
    """Creates a `tf.data.Iterator` for enumerating the elements of a dataset.
  
    Note: The returned iterator will be in an uninitialized state,
    and you must run the `iterator.initializer` operation before using it:
  
    ```python
    dataset = ...
    iterator = dataset.make_initializable_iterator()
    # ...
    sess.run(iterator.initializer)
    ```
  
    Args:
      dataset: A `tf.data.Dataset`.
  
    Returns:
      A `tf.data.Iterator` over the elements of `dataset`.
  
    Raises:
      RuntimeError: If eager execution is enabled.
    """
    try:
      # Call the defined `make_one_shot_iterator()` if there is one, because some
      # datasets (e.g. for prefetching) override its behavior.
      return dataset.make_initializable_iterator()
    except AttributeError:
      return DatasetV1Adapter(dataset).make_initializable_iterator()
  
  
  @tf_export("data.Options")
  class Options(options_lib.OptionsBase):
    """Represents options for tf.data.Dataset.
  
    An `Options` object can be, for instance, used to control which static
    optimizations to apply or whether to use performance modeling to dynamically
    tune the parallelism of operations such as `tf.data.Dataset.map` or
    `tf.data.Dataset.interleave`.
    """
  
    experimental_autotune = options_lib.create_option(
        name="experimental_autotune",
        ty=bool,
        docstring=
        "Whether to dynamically adjust the values of tunable parameters (e.g. "
        "degrees of parallelism). If None, defaults to True.")
  
    experimental_deterministic = options_lib.create_option(
        name="experimental_deterministic",
        ty=bool,
        docstring=
        "Whether the outputs need to be produced in deterministic order. If None,"
        " defaults to True.")
  
    experimental_numa_aware = options_lib.create_option(
        name="experimental_numa_aware",
        ty=bool,
        docstring=
        "Whether to use NUMA-aware operations. If None, defaults to False.")
  
    experimental_optimization = options_lib.create_option(
        name="experimental_optimization",
        ty=optimization_options.OptimizationOptions,
        docstring=
        "The optimization options associated with the dataset. See "
        "`tf.data.experimental.OptimizationOptions` for more details.",
        default_factory=optimization_options.OptimizationOptions)
  
    experimental_stats = options_lib.create_option(
        name="experimental_stats",
        ty=stats_options.StatsOptions,
        docstring=
        "The statistics options associated with the dataset. See "
        "`tf.data.experimental.StatsOptions` for more details.",
        default_factory=stats_options.StatsOptions)
  
    experimental_threading = options_lib.create_option(
        name="experimental_threading",
        ty=threading_options.ThreadingOptions,
        docstring=
        "The threading options associated with the dataset. See "
        "`tf.data.experimental.ThreadingOptions` for more details.",
        default_factory=threading_options.ThreadingOptions)
  
    def _static_optimizations(self):
      """Produces the list of enabled static optimizations."""
  
      result = []
      result.extend(self.experimental_optimization._static_optimizations())  # pylint: disable=protected-access
  
      if self.experimental_numa_aware:
        result.append("make_numa_aware")
      if self.experimental_deterministic is False:
        result.append("make_sloppy")
      exp_stats_options = self.experimental_stats
      if exp_stats_options and exp_stats_options.latency_all_edges:
        result.append("latency_all_edges")
      return result
  
    def merge(self, options):
      """Merges itself with the given `tf.data.Options`.
  
      The given `tf.data.Options` can be merged as long as there does not exist an
      attribute that is set to different values in `self` and `options`.
  
      Args:
        options: a `tf.data.Options` to merge with
  
      Raises:
        ValueError: if the given `tf.data.Options` cannot be merged
  
      Returns:
        New `tf.data.Options()` object which is the result of merging self with
        the input `tf.data.Options`.
      """
      return options_lib.merge_options(self, options)
  
  
  class DatasetSource(DatasetV2):
    """Abstract class representing a dataset with no inputs."""
  
    def _inputs(self):
      return []
  
  
  class UnaryDataset(DatasetV2):
    """Abstract class representing a dataset with one input."""
  
    def __init__(self, input_dataset):
      super(UnaryDataset, self).__init__()
      self._input_dataset = input_dataset
  
    def _inputs(self):
      return [self._input_dataset]
  
  
  class UnaryUnchangedStructureDataset(UnaryDataset):
    """Represents a unary dataset with the same input and output structure."""
  
    @property
    def _element_structure(self):
      return self._input_dataset._element_structure  # pylint: disable=protected-access
  
  
  class TensorDataset(DatasetSource):
    """A `Dataset` with a single element, viz. a nested structure of tensors."""
  
    def __init__(self, tensors):
      """See `Dataset.from_tensors()` for details."""
      super(TensorDataset, self).__init__()
      with ops.name_scope("tensors"):
        tensors = nest.pack_sequence_as(tensors, [
            sparse_tensor_lib.SparseTensor.from_value(t)
            if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(
                t, name="component_%d" % i)
            for i, t in enumerate(nest.flatten(tensors))
        ])
      self._structure = structure_lib.Structure.from_value(tensors)
      self._tensors = self._structure._to_tensor_list(tensors)  # pylint: disable=protected-access
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.tensor_dataset(
          self._tensors, output_shapes=self._structure._flat_shapes)  # pylint: disable=protected-access
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  class TensorSliceDataset(DatasetSource):
    """A `Dataset` of slices from a nested structure of tensors."""
  
    def __init__(self, tensors):
      """See `Dataset.from_tensor_slices()` for details."""
      super(TensorSliceDataset, self).__init__()
      with ops.name_scope("tensors"):
        tensors = nest.pack_sequence_as(tensors, [
            sparse_tensor_lib.SparseTensor.from_value(t)
            if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(
                t, name="component_%d" % i)
            for i, t in enumerate(nest.flatten(tensors))
        ])
  
      batched_structure = structure_lib.Structure.from_value(tensors)
      # pylint: disable=protected-access
      self._tensors = batched_structure._to_batched_tensor_list(tensors)
      self._structure = batched_structure._unbatch()
      # pylint: enable=protected-access
  
      batch_dim = tensor_shape.Dimension(tensor_shape.dimension_value(
          self._tensors[0].get_shape()[0]))
      for t in self._tensors[1:]:
        batch_dim.assert_is_compatible_with(tensor_shape.Dimension(
            tensor_shape.dimension_value(t.get_shape()[0])))
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.tensor_slice_dataset(
          self._tensors, output_shapes=self._structure._flat_shapes)  # pylint: disable=protected-access
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  class SparseTensorSliceDataset(DatasetSource):
    """A `Dataset` that splits a rank-N `tf.SparseTensor` into its rows."""
  
    def __init__(self, sparse_tensor):
      """See `Dataset.from_sparse_tensor_slices()` for details."""
      super(SparseTensorSliceDataset, self).__init__()
      if not isinstance(sparse_tensor, sparse_tensor_lib.SparseTensor):
        raise TypeError("`sparse_tensor` must be a `tf.SparseTensor` object.")
      self._sparse_tensor = sparse_tensor
  
      indices_shape = self._sparse_tensor.indices.get_shape()
      shape_shape = self._sparse_tensor.dense_shape.get_shape()
      rank = (indices_shape.dims[1] - 1).merge_with(shape_shape.dims[0] - 1)
      self._structure = structure_lib.NestedStructure(
          (structure_lib.TensorStructure(dtypes.int64, [None, rank]),
           structure_lib.TensorStructure(self._sparse_tensor.dtype, [None]),
           structure_lib.TensorStructure(dtypes.int64, [rank])))
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.sparse_tensor_slice_dataset(
          self._sparse_tensor.indices, self._sparse_tensor.values,
          self._sparse_tensor.dense_shape)
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  class _VariantDataset(DatasetV2):
    """A Dataset wrapper around a `tf.variant`-typed function argument."""
  
    def __init__(self, dataset_variant, structure):
      super(_VariantDataset, self).__init__()
      self._dataset_variant = dataset_variant
      self._structure = structure
  
    def _as_variant_tensor(self):
      return self._dataset_variant
  
    def _inputs(self):
      return []
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  @tf_export("data.experimental.DatasetStructure")
  class DatasetStructure(structure_lib.Structure):
    """Represents a `Dataset` of structured values."""
  
    def __init__(self, element_structure):
      self._element_structure = element_structure
  
    @property
    def _flat_shapes(self):
      return [tensor_shape.scalar()]
  
    @property
    def _flat_types(self):
      return [dtypes.variant]
  
    def is_compatible_with(self, other):
      # pylint: disable=protected-access
      return (isinstance(other, DatasetStructure) and
              self._element_structure.is_compatible_with(
                  other._element_structure))
  
    def _to_tensor_list(self, value):
      return [value._as_variant_tensor()]  # pylint: disable=protected-access
  
    def _to_batched_tensor_list(self, value):
      raise NotImplementedError("Unbatching for `tf.data.Dataset` objects.")
  
    def _from_tensor_list(self, flat_value):
      if (len(flat_value) != 1 or flat_value[0].dtype != dtypes.variant or
          not flat_value[0].shape.is_compatible_with(tensor_shape.scalar())):
        raise ValueError(
            "DatasetStructure corresponds to a single tf.variant scalar.")
      return self._from_compatible_tensor_list(flat_value)
  
    def _from_compatible_tensor_list(self, flat_value):
      # pylint: disable=protected-access
      return _VariantDataset(flat_value[0], self._element_structure)
  
    @staticmethod
    def from_value(value):
      return DatasetStructure(value._element_structure)  # pylint: disable=protected-access
  
    def _to_legacy_output_types(self):
      return self
  
    def _to_legacy_output_shapes(self):
      return self
  
    def _to_legacy_output_classes(self):
      return self
  
    def _batch(self, batch_size):
      raise NotImplementedError("Batching for `tf.data.Dataset` objects.")
  
    def _unbatch(self):
      raise NotImplementedError("Unbatching for `tf.data.Dataset` objects.")
  
  
  # pylint: disable=protected-access
  structure_lib.Structure._register_custom_converter(DatasetV2,
                                                     DatasetStructure.from_value)
  # pylint: enable=protected-access
  
  
  class StructuredFunctionWrapper(object):
    """A wrapper for `Defun` that supports structured arguments and return values.
    """
  
    def __init__(self,
                 func,
                 transformation_name,
                 dataset=None,
                 input_classes=None,
                 input_shapes=None,
                 input_types=None,
                 input_structure=None,
                 add_to_graph=True,
                 defun_kwargs=None):
      """Creates a new `StructuredFunctionWrapper` for the given function.
  
      Args:
        func: A function from a nested structure to another nested structure.
        transformation_name: Human-readable name of the transformation in which
          this function is being instantiated, for error messages.
        dataset: (Optional.) A `tf.data.Dataset`. If given, the structure of this
          dataset will be assumed as the structure for `func` arguments; otherwise
          `input_classes`, `input_shapes`, and `input_types` must be defined.
        input_classes: (Optional.) A nested structure of `type`. If given, this
          argument defines the Python types for `func` arguments.
        input_shapes: (Optional.) A nested structure of `tf.TensorShape`. If
          given, this argument defines the shapes and structure for `func`
          arguments.
        input_types: (Optional.) A nested structure of `tf.DType`. If given, this
          argument defines the element types and structure for `func` arguments.
        input_structure: (Optional.) A `Structure` object. If given, this argument
          defines the element types and structure for `func` arguments.
        add_to_graph: (Optional.) If `True`, the function will be added to the
          default graph.
        defun_kwargs: (Optional.) A dictionary mapping string argument names to
          values. If supplied, will be passed to `function.Defun()` as keyword
          arguments.
  
      Raises:
        ValueError: If an invalid combination of `dataset`, `input_classes`,
          `input_shapes`, and `input_types` is passed.
      """
      if input_structure is None:
        if dataset is None:
          if input_classes is None or input_shapes is None or input_types is None:
            raise ValueError("Either `dataset`, `input_structure` or all of "
                             "`input_classes`, `input_shapes`, and `input_types` "
                             "must be specified.")
          self._input_structure = structure_lib.convert_legacy_structure(
              input_types, input_shapes, input_classes)
        else:
          if not (input_classes is None and input_shapes is None and
                  input_types is None):
            raise ValueError("Either `dataset`, `input_structure` or all of "
                             "`input_classes`, `input_shapes`, and `input_types` "
                             "must be specified.")
          self._input_structure = dataset._element_structure  # pylint: disable=protected-access
      else:
        if not (dataset is None and input_classes is None and input_shapes is None
                and input_types is None):
          raise ValueError("Either `dataset`, `input_structure`, or all of "
                           "`input_classes`, `input_shapes`, and `input_types` "
                           "must be specified.")
        self._input_structure = input_structure
  
      self._transformation_name = transformation_name
      readable_transformation_name = transformation_name.replace(
          ".", "_")[:-2] if len(transformation_name) > 2 else ""
      self._func_name = "_".join([
          readable_transformation_name,
          function_utils.get_func_name(func),
          str(ops.uid())
      ])
  
      if defun_kwargs is None:
        defun_kwargs = {}
  
      @function.Defun(
          *self._input_structure._flat_types, func_name=self._func_name,  # pylint: disable=protected-access
          **defun_kwargs)
      def tf_data_structured_function_wrapper(*args):
        """Wrapper for passing nested structures to and from tf.data functions."""
        # pylint: disable=protected-access
        nested_args = self._input_structure._from_compatible_tensor_list(args)
        if not _should_unpack_args(nested_args):
          nested_args = (nested_args,)
  
        ret = func(*nested_args)
        # If `func` returns a list of tensors, `nest.flatten()` and
        # `ops.convert_to_tensor()` would conspire to attempt to stack
        # those tensors into a single tensor, because the customized
        # version of `nest.flatten()` does not recurse into lists. Since
        # it is more likely that the list arose from returning the
        # result of an operation (such as `tf.py_func()`) that returns a
        # list of not-necessarily-stackable tensors, we treat the
        # returned value is a `tuple` instead. A user wishing to pack
        # the return value into a single tensor can use an explicit
        # `tf.stack()` before returning.
        if isinstance(ret, list):
          ret = tuple(ret)
  
        try:
          self._output_structure = structure_lib.Structure.from_value(ret)
        except (ValueError, TypeError):
          raise TypeError("Unsupported return value from function passed to "
                          "%s: %s." % (transformation_name, ret))
  
        _warn_if_collections(transformation_name)
        return self._output_structure._to_tensor_list(ret)
  
      self._function = tf_data_structured_function_wrapper
      if add_to_graph:
        self._function.add_to_graph(ops.get_default_graph())
      else:
        # Use the private method that will execute
        # `tf_data_structured_function_wrapper` but delay adding it to the graph
        # in case (e.g.) we need to rerun the function.
        self._function._create_definition_if_needed()  # pylint: disable=protected-access
  
    @property
    def output_structure(self):
      return self._output_structure
  
    @property
    def output_classes(self):
      return self._output_structure._to_legacy_output_classes()  # pylint: disable=protected-access
  
    @property
    def output_shapes(self):
      return self._output_structure._to_legacy_output_shapes()  # pylint: disable=protected-access
  
    @property
    def output_types(self):
      return self._output_structure._to_legacy_output_types()  # pylint: disable=protected-access
  
    @property
    def function(self):
      return self._function
  
  
  def flat_structure(dataset):
    """Helper for setting `output_shapes` and `output_types` attrs of Dataset ops.
  
    Most Dataset op constructors expect `output_shapes` and `output_types`
    arguments that represent the flattened structure of an element. This helper
    function generates these attrs as a keyword argument dictionary, allowing
    `Dataset._as_variant_tensor()` implementations to pass
    `**flat_structure(self)` to the op constructor.
  
    Args:
      dataset: A `tf.data.Dataset`.
  
    Returns:
      A dictionary of keyword arguments that can be passed to many Dataset op
      constructors.
    """
    # pylint: disable=protected-access
    structure = dataset._element_structure
    return {
        "output_shapes": structure._flat_shapes,
        "output_types": structure._flat_types,
    }
  
  
  class _GeneratorDataset(DatasetSource):
    """A `Dataset` that generates elements by invoking a function."""
  
    def __init__(self, init_args, init_func, next_func, finalize_func):
      """Constructs a `_GeneratorDataset`.
  
      Args:
        init_args: A nested structure representing the arguments to `init_func`.
        init_func: A TensorFlow function that will be called on `init_args` each
          time a C++ iterator over this dataset is constructed. Returns a nested
          structure representing the "state" of the dataset.
        next_func: A TensorFlow function that will be called on the result of
          `init_func` to produce each element, and that raises `OutOfRangeError`
          to terminate iteration.
        finalize_func: A TensorFlow function that will be called on the result of
          `init_func` immediately before a C++ iterator over this dataset is
          destroyed. The return value is ignored.
      """
      super(_GeneratorDataset, self).__init__()
      self._init_args = init_args
  
      self._init_structure = structure_lib.Structure.from_value(init_args)
  
      self._init_func = StructuredFunctionWrapper(
          init_func,
          self._transformation_name(),
          input_structure=self._init_structure)
  
      self._next_func = StructuredFunctionWrapper(
          next_func,
          self._transformation_name(),
          input_structure=self._init_func.output_structure)
  
      self._finalize_func = StructuredFunctionWrapper(
          finalize_func,
          self._transformation_name(),
          input_structure=self._init_func.output_structure)
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.generator_dataset(
          self._init_structure._to_tensor_list(self._init_args)  # pylint: disable=protected-access
          + self._init_func.function.captured_inputs,
          self._next_func.function.captured_inputs,
          self._finalize_func.function.captured_inputs,
          init_func=self._init_func.function,
          next_func=self._next_func.function,
          finalize_func=self._finalize_func.function,
          **flat_structure(self))
  
    @property
    def _element_structure(self):
      return self._next_func.output_structure
  
    def _transformation_name(self):
      return "Dataset.from_generator()"
  
  
  class ZipDataset(DatasetV2):
    """A `Dataset` that zips its inputs together."""
  
    def __init__(self, datasets):
      """See `Dataset.zip()` for details."""
      super(ZipDataset, self).__init__()
      for ds in nest.flatten(datasets):
        if not isinstance(ds, DatasetV2):
          if isinstance(ds, list):
            message = ("The argument to `Dataset.zip()` must be a nested "
                       "structure of `Dataset` objects. Nested structures do not "
                       "support Python lists; please use a tuple instead.")
          else:
            message = ("The argument to `Dataset.zip()` must be a nested "
                       "structure of `Dataset` objects.")
          raise TypeError(message)
      self._datasets = datasets
      self._structure = structure_lib.NestedStructure(
          nest.pack_sequence_as(
              self._datasets,
              [ds._element_structure for ds in nest.flatten(self._datasets)]))  # pylint: disable=protected-access
  
    def _as_variant_tensor(self):
      # pylint: disable=protected-access
      return gen_dataset_ops.zip_dataset(
          [ds._as_variant_tensor() for ds in nest.flatten(self._datasets)],
          **flat_structure(self))
      # pylint: enable=protected-access
  
    def _inputs(self):
      return nest.flatten(self._datasets)
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  class ConcatenateDataset(DatasetV2):
    """A `Dataset` that concatenates its input with given dataset."""
  
    def __init__(self, input_dataset, dataset_to_concatenate):
      """See `Dataset.concatenate()` for details."""
      super(ConcatenateDataset, self).__init__()
      self._input_dataset = input_dataset
      self._dataset_to_concatenate = dataset_to_concatenate
  
      output_types = input_dataset.output_types
      if output_types != dataset_to_concatenate.output_types:
        raise TypeError(
            "Two datasets to concatenate have different types %s and %s" %
            (output_types, dataset_to_concatenate.output_types))
  
      output_classes = input_dataset.output_classes
      if output_classes != dataset_to_concatenate.output_classes:
        raise TypeError(
            "Two datasets to concatenate have different classes %s and %s" %
            (output_classes, dataset_to_concatenate.output_classes))
  
      input_shapes = self._input_dataset.output_shapes
      output_shapes = nest.pack_sequence_as(input_shapes, [
          ts1.most_specific_compatible_shape(ts2)
          for (ts1, ts2) in zip(
              nest.flatten(input_shapes),
              nest.flatten(self._dataset_to_concatenate.output_shapes))
      ])
  
      self._structure = structure_lib.convert_legacy_structure(
          output_types, output_shapes, output_classes)
  
      self._input_datasets = [input_dataset, dataset_to_concatenate]
  
    def _as_variant_tensor(self):
      # pylint: disable=protected-access
      return gen_dataset_ops.concatenate_dataset(
          self._input_dataset._as_variant_tensor(),
          self._dataset_to_concatenate._as_variant_tensor(),
          **flat_structure(self))
      # pylint: enable=protected-access
  
    def _inputs(self):
      return [self._input_dataset, self._dataset_to_concatenate]
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  class RepeatDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that repeats its input several times."""
  
    def __init__(self, input_dataset, count):
      """See `Dataset.repeat()` for details."""
      super(RepeatDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      if count is None:
        self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
      else:
        self._count = ops.convert_to_tensor(
            count, dtype=dtypes.int64, name="count")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.repeat_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          count=self._count,
          **flat_structure(self))
  
  
  class RangeDataset(DatasetSource):
    """A `Dataset` of a step separated range of values."""
  
    def __init__(self, *args):
      """See `Dataset.range()` for details."""
      super(RangeDataset, self).__init__()
      self._parse_args(*args)
  
    def _parse_args(self, *args):
      """Parse arguments according to the same rules as the `range()` builtin."""
      if len(args) == 1:
        self._start = self._build_tensor(0, "start")
        self._stop = self._build_tensor(args[0], "stop")
        self._step = self._build_tensor(1, "step")
      elif len(args) == 2:
        self._start = self._build_tensor(args[0], "start")
        self._stop = self._build_tensor(args[1], "stop")
        self._step = self._build_tensor(1, "step")
      elif len(args) == 3:
        self._start = self._build_tensor(args[0], "start")
        self._stop = self._build_tensor(args[1], "stop")
        self._step = self._build_tensor(args[2], "step")
      else:
        raise ValueError("Invalid arguments to RangeDataset: %s" % str(args))
  
    def _build_tensor(self, int64_value, name):
      return ops.convert_to_tensor(int64_value, dtype=dtypes.int64, name=name)
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.range_dataset(
          start=self._start,
          stop=self._stop,
          step=self._step,
          **flat_structure(self))
  
    @property
    def _element_structure(self):
      return structure_lib.TensorStructure(dtypes.int64, [])
  
  
  class CacheDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that caches elements of its input."""
  
    def __init__(self, input_dataset, filename):
      """See `Dataset.cache()` for details."""
      super(CacheDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._filename = ops.convert_to_tensor(
          filename, dtype=dtypes.string, name="filename")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.cache_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          filename=self._filename,
          **flat_structure(self))
  
  
  class ShuffleDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that randomly shuffles the elements of its input."""
  
    def __init__(self,
                 input_dataset,
                 buffer_size,
                 seed=None,
                 reshuffle_each_iteration=None):
      """Randomly shuffles the elements of this dataset.
  
      Args:
        input_dataset: The input dataset.
        buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
          number of elements from this dataset from which the new
          dataset will sample.
        seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
          random seed that will be used to create the distribution. See
          `tf.set_random_seed` for behavior.
        reshuffle_each_iteration: (Optional.) A boolean, which if true indicates
          that the dataset should be pseudorandomly reshuffled each time it is
          iterated over. (Defaults to `True`.)
  
      Returns:
        A `Dataset`.
  
      Raises:
        ValueError: if invalid arguments are provided.
      """
      super(ShuffleDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._buffer_size = ops.convert_to_tensor(
          buffer_size, dtype=dtypes.int64, name="buffer_size")
      self._seed, self._seed2 = random_seed.get_seed(seed)
  
      if reshuffle_each_iteration is None:
        self._reshuffle_each_iteration = True
      else:
        self._reshuffle_each_iteration = reshuffle_each_iteration
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.shuffle_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          buffer_size=self._buffer_size,
          seed=self._seed,
          seed2=self._seed2,
          reshuffle_each_iteration=self._reshuffle_each_iteration,
          **flat_structure(self))
  
  
  class TakeDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` containing the first `count` elements from its input."""
  
    def __init__(self, input_dataset, count):
      """See `Dataset.take()` for details."""
      super(TakeDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.take_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          count=self._count,
          **flat_structure(self))
  
  
  class SkipDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` skipping the first `count` elements from its input."""
  
    def __init__(self, input_dataset, count):
      """See `Dataset.skip()` for details."""
      super(SkipDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.skip_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          count=self._count,
          **flat_structure(self))
  
  
  class BatchDataset(UnaryDataset):
    """A `Dataset` that batches contiguous elements from its input."""
  
    def __init__(self, input_dataset, batch_size, drop_remainder):
      """See `Dataset.batch()` for details."""
      super(BatchDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._batch_size = ops.convert_to_tensor(
          batch_size, dtype=dtypes.int64, name="batch_size")
      self._drop_remainder = ops.convert_to_tensor(
          drop_remainder, dtype=dtypes.bool, name="drop_remainder")
  
      constant_drop_remainder = tensor_util.constant_value(self._drop_remainder)
      # pylint: disable=protected-access
      if constant_drop_remainder:
        # NOTE(mrry): `constant_drop_remainder` may be `None` (unknown statically)
        # or `False` (explicitly retaining the remainder).
        self._structure = input_dataset._element_structure._batch(
            tensor_util.constant_value(self._batch_size))
      else:
        self._structure = input_dataset._element_structure._batch(None)
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.batch_dataset_v2(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          batch_size=self._batch_size,
          drop_remainder=self._drop_remainder,
          **flat_structure(self))
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  def _is_padded_shape_compatible_with(padded_shape, input_component_shape):
    """Returns `True` if `input_component_shape` can be padded to `padded_shape`.
  
    Args:
      padded_shape: A `tf.TensorShape`.
      input_component_shape: A `tf.TensorShape`.
  
    Returns:
      `True` if `input_component_shape` can be padded to `padded_shape`, otherwise
      `False`.
    """
  
    if padded_shape.dims is None or input_component_shape.dims is None:
      return True
    if len(padded_shape.dims) != len(input_component_shape.dims):
      return False
    for padded_dim, input_dim in zip(
        padded_shape.dims, input_component_shape.dims):
      if (padded_dim.value is not None and input_dim.value is not None
          and padded_dim.value < input_dim.value):
        return False
    return True
  
  
  def _padded_shape_to_tensor(padded_shape, input_component_shape):
    """Converts `padded_shape` to a `tf.Tensor` representing that shape.
  
    Args:
      padded_shape: A shape-like object, which may be a `tf.TensorShape`, a Python
        sequence, or a 1-D `tf.Tensor` of `tf.int64` elements.
      input_component_shape: A `tf.TensorShape`, with which `padded_shape` must
        be compatible.
  
    Returns:
      A 1-D `tf.Tensor` of `tf.int64` elements, representing `padded_shape`.
  
    Raises:
      ValueError: If `padded_shape` is not a shape or not compatible with
        `input_component_shape`.
      TypeError: If `padded_shape` is not convertible to a `tf.int64` tensor.
    """
    try:
      # Try to convert the `padded_shape` to a `tf.TensorShape`
      padded_shape_as_shape = tensor_shape.as_shape(padded_shape)
      # We will return the "canonical" tensor representation, which uses
      # `-1` in place of `None`.
      ret = ops.convert_to_tensor(
          [dim if dim is not None else -1
           for dim in padded_shape_as_shape.as_list()], dtype=dtypes.int64)
    except (TypeError, ValueError):
      # The argument was not trivially convertible to a
      # `tf.TensorShape`, so fall back on the conversion to tensor
      # machinery.
      ret = ops.convert_to_tensor(padded_shape, preferred_dtype=dtypes.int64)
      if ret.shape.dims is not None and len(ret.shape.dims) != 1:
        raise ValueError(
            "Padded shape %s must be a 1-D tensor of tf.int64 values, but its "
            "shape was %s." % (padded_shape, ret.shape))
      if ret.dtype != dtypes.int64:
        raise TypeError(
            "Padded shape %s must be a 1-D tensor of tf.int64 values, but its "
            "element type was %s." % (padded_shape, ret.dtype.name))
      padded_shape_as_shape = tensor_util.constant_value_as_shape(ret)
  
    if not _is_padded_shape_compatible_with(padded_shape_as_shape,
                                            input_component_shape):
      raise ValueError("The padded shape %s is not compatible with the "
                       "corresponding input component shape %s."
                       % (padded_shape_as_shape, input_component_shape))
  
    return ret
  
  
  def _padding_value_to_tensor(value, output_type):
    """Converts the padding value to a tensor.
  
    Args:
      value: The padding value.
      output_type: Its expected dtype.
  
    Returns:
      A scalar `Tensor`.
  
    Raises:
      ValueError: if the padding value is not a scalar.
      TypeError: if the padding value's type does not match `output_type`.
    """
    value = ops.convert_to_tensor(value, name="padding_value")
    if not value.shape.is_compatible_with(tensor_shape.scalar()):
      raise ValueError("Padding value should be a scalar, but is not: %s" % value)
    if value.dtype != output_type:
      raise TypeError("Padding value tensor (%s) does not match output type: %s" %
                      (value, output_type))
    return value
  
  
  def _default_padding(input_dataset):
    """Returns default padding tensors in a structure matching `input_dataset`."""
    def make_zero(t):
      if t.base_dtype == dtypes.string:
        return ""
      elif t.base_dtype == dtypes.variant:
        raise TypeError("Unable to create padding for field of type 'variant'")
      else:
        return np.zeros_like(t.as_numpy_dtype())
  
    return nest.map_structure(make_zero, input_dataset.output_types)
  
  
  class PaddedBatchDataset(UnaryDataset):
    """A `Dataset` that batches and pads contiguous elements from its input."""
  
    def __init__(self, input_dataset, batch_size, padded_shapes, padding_values,
                 drop_remainder):
      """See `Dataset.batch()` for details."""
      super(PaddedBatchDataset, self).__init__(input_dataset)
      if sparse.any_sparse(input_dataset.output_classes):
        # TODO(b/63669786): support batching of sparse tensors
        raise TypeError(
            "Batching of padded sparse tensors is not currently supported")
      self._input_dataset = input_dataset
      self._batch_size = ops.convert_to_tensor(
          batch_size, dtype=dtypes.int64, name="batch_size")
      padding_values = (
          padding_values
          if padding_values is not None else _default_padding(input_dataset))
  
      flat_padded_shapes = nest.flatten_up_to(input_dataset.output_shapes,
                                              padded_shapes)
  
      flat_padded_shapes_as_tensors = []
  
      for input_component_shape, padded_shape in zip(
          nest.flatten(input_dataset.output_shapes), flat_padded_shapes):
        flat_padded_shapes_as_tensors.append(
            _padded_shape_to_tensor(padded_shape, input_component_shape))
  
      self._padded_shapes = nest.pack_sequence_as(input_dataset.output_shapes,
                                                  flat_padded_shapes_as_tensors)
  
      self._padding_values = nest.map_structure_up_to(
          input_dataset.output_shapes, _padding_value_to_tensor, padding_values,
          input_dataset.output_types)
      self._drop_remainder = ops.convert_to_tensor(
          drop_remainder, dtype=dtypes.bool, name="drop_remainder")
  
      def _padded_shape_to_batch_shape(s):
        return tensor_shape.vector(
            tensor_util.constant_value(self._batch_size) if smart_cond.
            smart_constant_value(self._drop_remainder) else None).concatenate(
                tensor_util.constant_value_as_shape(s))
  
      output_shapes = nest.map_structure(
          _padded_shape_to_batch_shape, self._padded_shapes)
      self._structure = structure_lib.convert_legacy_structure(
          self._input_dataset.output_types, output_shapes,
          self._input_dataset.output_classes)
  
    def _as_variant_tensor(self):
      # pylint: disable=protected-access
      # TODO(jsimsa): Switch to using v2 only any time after 6/30/2018.
      if smart_cond.smart_constant_value(self._drop_remainder) is False:
        return gen_dataset_ops.padded_batch_dataset(
            self._input_dataset._as_variant_tensor(),
            batch_size=self._batch_size,
            padded_shapes=[
                ops.convert_to_tensor(s, dtype=dtypes.int64)
                for s in nest.flatten(self._padded_shapes)
            ],
            padding_values=nest.flatten(self._padding_values),
            output_shapes=self._structure._flat_shapes)
      else:
        return gen_dataset_ops.padded_batch_dataset_v2(
            self._input_dataset._as_variant_tensor(),
            batch_size=self._batch_size,
            padded_shapes=[
                ops.convert_to_tensor(s, dtype=dtypes.int64)
                for s in nest.flatten(self._padded_shapes)
            ],
            padding_values=nest.flatten(self._padding_values),
            drop_remainder=self._drop_remainder,
            output_shapes=self._structure._flat_shapes)
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  def _should_unpack_args(args):
    """Returns `True` if `args` should be `*args` when passed to a callable."""
    return type(args) is tuple  # pylint: disable=unidiomatic-typecheck
  
  
  def _warn_if_collections(transformation_name):
    """Prints warning message if the current graph uses common graph collections.
  
    NOTE(mrry): Currently a warning is only generated for lookup tables. Any
    variables created will be automatically hoisted out to the outermost scope
    using `init_scope()`. Some collections (such as for control-flow contexts)
    are benign and should not generate a warning.
  
    Args:
      transformation_name: A human-readable name for the transformation.
    """
    if ops.get_default_graph().get_collection(ops.GraphKeys.TABLE_INITIALIZERS):
      warnings.warn("Creating lookup tables inside a function passed to %s is not"
                    " supported. Create each table outside the function, and "
                    "capture it inside the function to use it."
                    % transformation_name)
  
  
  class MapDataset(UnaryDataset):
    """A `Dataset` that maps a function over elements in its input."""
  
    def __init__(self,
                 input_dataset,
                 map_func,
                 use_inter_op_parallelism=True,
                 preserve_cardinality=False):
      """See `Dataset.map()` for details."""
      super(MapDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._use_inter_op_parallelism = use_inter_op_parallelism
      self._preserve_cardinality = preserve_cardinality
      self._map_func = StructuredFunctionWrapper(
          map_func, self._transformation_name(), dataset=input_dataset)
  
    def _as_variant_tensor(self):
      input_t = self._input_dataset._as_variant_tensor()  # pylint: disable=protected-access
      return gen_dataset_ops.map_dataset(
          input_t,
          self._map_func.function.captured_inputs,
          f=self._map_func.function,
          use_inter_op_parallelism=self._use_inter_op_parallelism,
          preserve_cardinality=self._preserve_cardinality,
          **flat_structure(self))
  
    def _functions(self):
      return [self._map_func]
  
    @property
    def _element_structure(self):
      return self._map_func.output_structure
  
    def _transformation_name(self):
      return "Dataset.map()"
  
  
  class ParallelMapDataset(MapDataset):
    """A `Dataset` that maps a function over elements in its input in parallel."""
  
    def __init__(self,
                 input_dataset,
                 map_func,
                 num_parallel_calls,
                 use_inter_op_parallelism=True,
                 preserve_cardinality=False):
      """See `Dataset.map()` for details."""
      super(ParallelMapDataset, self).__init__(
          input_dataset, map_func, use_inter_op_parallelism, preserve_cardinality)
  
      self._num_parallel_calls = ops.convert_to_tensor(
          num_parallel_calls, dtype=dtypes.int32, name="num_parallel_calls")
  
    def _as_variant_tensor(self):
      # pylint: disable=protected-access
      input_t = self._input_dataset._as_variant_tensor()
      return gen_dataset_ops.parallel_map_dataset(
          input_t,
          self._map_func.function.captured_inputs,
          f=self._map_func.function,
          num_parallel_calls=self._num_parallel_calls,
          use_inter_op_parallelism=self._use_inter_op_parallelism,
          preserve_cardinality=self._preserve_cardinality,
          **flat_structure(self))
  
  
  class FlatMapDataset(UnaryDataset):
    """A `Dataset` that maps a function over its input and flattens the result."""
  
    def __init__(self, input_dataset, map_func):
      """See `Dataset.flat_map()` for details."""
      super(FlatMapDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
  
      self._map_func = StructuredFunctionWrapper(
          map_func, self._transformation_name(), dataset=input_dataset)
      if not isinstance(self._map_func.output_structure, DatasetStructure):
        raise TypeError("`map_func` must return a `Dataset` object.")
      self._structure = self._map_func.output_structure._element_structure  # pylint: disable=protected-access
  
    def _functions(self):
      return [self._map_func]
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.flat_map_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._map_func.function.captured_inputs,
          f=self._map_func.function,
          **flat_structure(self))
  
    @property
    def _element_structure(self):
      return self._structure
  
    def _transformation_name(self):
      return "Dataset.flat_map()"
  
  
  class InterleaveDataset(FlatMapDataset):
    """A `Dataset` that maps a function over its input and interleaves the result.
    """
  
    def __init__(self, input_dataset, map_func, cycle_length, block_length):
      """See `Dataset.interleave()` for details."""
      super(InterleaveDataset, self).__init__(input_dataset, map_func)
      self._cycle_length = ops.convert_to_tensor(
          cycle_length, dtype=dtypes.int64, name="cycle_length")
      self._block_length = ops.convert_to_tensor(
          block_length, dtype=dtypes.int64, name="block_length")
  
    def _as_variant_tensor(self):
      # pylint: disable=protected-access
      return gen_dataset_ops.interleave_dataset(
          self._input_dataset._as_variant_tensor(),
          self._map_func.function.captured_inputs,
          self._cycle_length,
          self._block_length,
          f=self._map_func.function,
          **flat_structure(self))
  
    def _transformation_name(self):
      return "Dataset.interleave()"
  
  
  class ParallelInterleaveDataset(FlatMapDataset):
    """A `Dataset` that maps a function over its input and interleaves the result.
  
    """
  
    def __init__(self, input_dataset, map_func, cycle_length, block_length,
                 num_parallel_calls):
      """See `Dataset.interleave()` for details."""
      super(ParallelInterleaveDataset, self).__init__(input_dataset, map_func)
      self._cycle_length = ops.convert_to_tensor(
          cycle_length, dtype=dtypes.int64, name="cycle_length")
      self._block_length = ops.convert_to_tensor(
          block_length, dtype=dtypes.int64, name="block_length")
      self._num_parallel_calls = ops.convert_to_tensor(
          num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls")
  
    def _as_variant_tensor(self):
      # pylint: disable=protected-access
      return gen_dataset_ops.parallel_interleave_dataset_v2(
          self._input_dataset._as_variant_tensor(),
          self._map_func.function.captured_inputs,
          self._cycle_length,
          self._block_length,
          self._num_parallel_calls,
          f=self._map_func.function,
          **flat_structure(self))
  
    def _transformation_name(self):
      return "Dataset.interleave()"
  
  
  class FilterDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that filters its input according to a predicate function."""
  
    def __init__(self, input_dataset, predicate):
      """See `Dataset.filter()` for details."""
      super(FilterDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      wrapped_func = StructuredFunctionWrapper(
          predicate, self._transformation_name(), dataset=input_dataset)
      if not wrapped_func.output_structure.is_compatible_with(
          structure_lib.TensorStructure(dtypes.bool, [])):
        raise ValueError("`predicate` must return a scalar boolean tensor.")
      self._predicate = wrapped_func
  
    def _functions(self):
      return [self._predicate]
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.filter_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          other_arguments=self._predicate.function.captured_inputs,
          predicate=self._predicate.function,
          **flat_structure(self))
  
    def _transformation_name(self):
      return "Dataset.filter()"
  
  
  class PrefetchDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that asynchronously prefetches its input."""
  
    def __init__(self, input_dataset, buffer_size):
      """See `Dataset.prefetch()` for details."""
      super(PrefetchDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      if buffer_size is None:
        buffer_size = -1  # This is the sentinel for auto-tuning.
      self._buffer_size = ops.convert_to_tensor(
          buffer_size, dtype=dtypes.int64, name="buffer_size")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.prefetch_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          buffer_size=self._buffer_size,
          **flat_structure(self))
  
  
  class WindowDataset(UnaryDataset):
    """A dataset that creates window datasets from the input elements."""
  
    def __init__(self, input_dataset, size, shift, stride, drop_remainder):
      """See `window_dataset()` for more details."""
      super(WindowDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._size = ops.convert_to_tensor(size, dtype=dtypes.int64, name="size")
      self._shift = ops.convert_to_tensor(shift, dtype=dtypes.int64, name="shift")
      self._stride = ops.convert_to_tensor(
          stride, dtype=dtypes.int64, name="stride")
      self._drop_remainder = ops.convert_to_tensor(
          drop_remainder, dtype=dtypes.bool, name="drop_remainder")
      nest_of_structures = nest.pack_sequence_as(
          input_dataset.output_classes,
          [
              DatasetStructure(structure_lib.convert_legacy_structure(
                  output_type, output_shape, output_class))
              for output_class, output_shape, output_type in zip(
                  nest.flatten(input_dataset.output_classes),
                  nest.flatten(input_dataset.output_shapes),
                  nest.flatten(input_dataset.output_types))
          ])
      self._structure = structure_lib.NestedStructure(nest_of_structures)
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.window_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._size,
          self._shift,
          self._stride,
          self._drop_remainder,
          **flat_structure(self))
  
    @property
    def _element_structure(self):
      return self._structure
  
  
  class _OptionsDataset(UnaryUnchangedStructureDataset):
    """An identity `Dataset` that stores options."""
  
    def __init__(self, input_dataset, options):
      super(_OptionsDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._options = input_dataset.options()
      if self._options:
        self._options = self._options.merge(options)
      else:
        self._options = options
  
    def _as_variant_tensor(self):
      return self._input_dataset._as_variant_tensor()  # pylint: disable=protected-access
  
    def options(self):
      return self._options
  
  
  class _ModelDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that acts as an identity, and models performance."""
  
    def __init__(self, input_dataset):
      """See `optimize()` for details."""
      super(_ModelDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.model_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          **flat_structure(self))
  
  
  class _OptimizeDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that acts as an identity, and applies optimizations."""
  
    def __init__(self, input_dataset, optimizations):
      """See `optimize()` for details."""
      super(_OptimizeDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      if optimizations is None:
        optimizations = []
      self._optimizations = ops.convert_to_tensor(
          optimizations, dtype=dtypes.string, name="optimizations")
  
    def _as_variant_tensor(self):
      return gen_dataset_ops.optimize_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._optimizations,
          **flat_structure(self))
  
  
  class _SetStatsAggregatorDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that acts as an identity, and sets a stats aggregator."""
  
    def __init__(self, input_dataset, aggregator, prefix, counter_prefix):
      super(_SetStatsAggregatorDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._stats_aggregator = aggregator
      self._prefix = prefix
      self._counter_prefix = counter_prefix
  
    def _as_variant_tensor(self):
      return ged_ops.experimental_set_stats_aggregator_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._stats_aggregator._resource,  # pylint: disable=protected-access
          self._prefix,
          self._counter_prefix,
          **flat_structure(self))
  
  
  class _MaxIntraOpParallelismDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that acts as an identity, overriding intra-op parallelism."""
  
    def __init__(self, input_dataset, max_intra_op_parallelism):
      super(_MaxIntraOpParallelismDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._max_intra_op_parallelism = ops.convert_to_tensor(
          max_intra_op_parallelism,
          dtype=dtypes.int64,
          name="max_intra_op_parallelism")
  
    def _as_variant_tensor(self):
      return ged_ops.experimental_max_intra_op_parallelism_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._max_intra_op_parallelism,
          **flat_structure(self))
  
  
  class _PrivateThreadPoolDataset(UnaryUnchangedStructureDataset):
    """A `Dataset` that acts as an identity, setting a private threadpool."""
  
    def __init__(self, input_dataset, num_threads):
      super(_PrivateThreadPoolDataset, self).__init__(input_dataset)
      self._input_dataset = input_dataset
      self._num_threads = ops.convert_to_tensor(
          num_threads, dtype=dtypes.int64, name="num_threads")
  
    def _as_variant_tensor(self):
      return ged_ops.experimental_private_thread_pool_dataset(
          self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
          self._num_threads,
          **flat_structure(self))
  