text: # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  # pylint: disable=g-classes-have-attributes
  """Input dataset creator for `model.fit`."""
  
  import tensorflow.compat.v2 as tf
  from tensorflow.python.util.tf_export import keras_export
  
  
  @keras_export('keras.utils.experimental.DatasetCreator', v1=[])
  class DatasetCreator(object):
    """Object that returns a `tf.data.Dataset` upon invoking.
  
    `tf.keras.utils.experimental.DatasetCreator` is designated as a supported type
    for `x`, or the input, in `tf.keras.Model.fit`. Pass an instance of this class
    to `fit` when using a callable (with a `input_context` argument) that returns
    a `tf.data.Dataset`.
  
    ```python
    model = tf.keras.Sequential([tf.keras.layers.Dense(10)])
    model.compile(tf.keras.optimizers.SGD(), loss="mse")
  
    def dataset_fn(input_context):
      global_batch_size = 64
      batch_size = input_context.get_per_replica_batch_size(global_batch_size)
      dataset = tf.data.Dataset.from_tensors(([1.], [1.])).repeat()
      dataset = dataset.shard(
          input_context.num_input_pipelines, input_context.input_pipeline_id)
      dataset = dataset.batch(batch_size)
      dataset = dataset.prefetch(2)
      return dataset
  
    input_options = tf.distribute.InputOptions(
        experimental_fetch_to_device=True,
        experimental_per_replica_buffer_size=2)
    model.fit(tf.keras.utils.experimental.DatasetCreator(
        dataset_fn, input_options=input_options), epochs=10, steps_per_epoch=10)
    ```
  
    `Model.fit` usage with `DatasetCreator` is intended to work across all
    `tf.distribute.Strategy`s, as long as `Strategy.scope` is used at model
    creation:
  
    ```python
    strategy = tf.distribute.experimental.ParameterServerStrategy(
        cluster_resolver)
    with strategy.scope():
      model = tf.keras.Sequential([tf.keras.layers.Dense(10)])
    model.compile(tf.keras.optimizers.SGD(), loss="mse")
    ...
    ```
  
    Note: When using `DatasetCreator`, `steps_per_epoch` argument in `Model.fit`
    must be provided as the cardinality of such input cannot be inferred.
  
    Args:
      dataset_fn: A callable that takes a single argument of type
        `tf.distribute.InputContext`, which is used for batch size calculation and
        cross-worker input pipeline sharding (if neither is needed, the
        `InputContext` parameter can be ignored in the `dataset_fn`), and returns
        a `tf.data.Dataset`.
      input_options: Optional `tf.distribute.InputOptions`, used for specific
        options when used with distribution, for example, whether to prefetch
        dataset elements to accelerator device memory or host device memory, and
        prefetch buffer size in the replica device memory. No effect if not used
        with distributed training. See `tf.distribute.InputOptions` for more
        information.
    """
  
    def __init__(self, dataset_fn, input_options=None):
      if not callable(dataset_fn):
        raise TypeError('`dataset_fn` for `DatasetCreator` must be a `callable`.')
      if input_options and (not isinstance(input_options,
                                           tf.distribute.InputOptions)):
        raise TypeError('`input_options` for `DatasetCreator` must be a '
                        '`tf.distribute.InputOptions`.')
  
      self.dataset_fn = dataset_fn
      self.input_options = input_options
  
    def __call__(self, *args, **kwargs):
      # When a `DatasetCreator` is invoked, it forwards args/kwargs straight to
      # the callable.
      dataset = self.dataset_fn(*args, **kwargs)
      if not isinstance(dataset, tf.data.Dataset):
        raise TypeError('The `callable` provided to `DatasetCreator` must return '
                        'a Dataset.')
      return dataset
  