# 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.
# ==============================================================================
"""Ignore_errors dataset transformations."""
from tensorflow.python.data.ops import ignore_errors_op
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export


@tf_export("data.experimental.ignore_errors")
@deprecation.deprecated(None, "Use `tf.data.Dataset.ignore_errors` instead.")
def ignore_errors(log_warning=False):
  """Creates a `Dataset` from another `Dataset` and silently ignores any errors.

  Use this transformation to produce a dataset that contains the same elements
  as the input, but silently drops any elements that caused an error. For
  example:

  ```python
  dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])

  # Computing `tf.debugging.check_numerics(1. / 0.)` will raise an
  InvalidArgumentError.
  dataset = dataset.map(lambda x: tf.debugging.check_numerics(1. / x, "error"))

  # Using `ignore_errors()` will drop the element that causes an error.
  dataset =
      dataset.apply(tf.data.experimental.ignore_errors())  # ==> {1., 0.5, 0.2}
  ```
  Args:
     log_warning: (Optional.) A 'tf.bool' scalar indicating whether ignored
      errors should be logged to stderr. Defaults to 'False'.

  Returns:
    A `Dataset` transformation function, which can be passed to
    `tf.data.Dataset.apply`.
  """
  def _apply_fn(dataset):
    return ignore_errors_op.IgnoreErrorsDataset(dataset, log_warning)

  return _apply_fn