text: # Copyright 2018 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.
  # ==============================================================================
  """Ops for converting between row_splits and segment_ids."""
  
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  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 math_ops
  from tensorflow.python.ops.ragged import ragged_util
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
  
  
  # For background on "segments" and "segment ids", see:
  # https://www.tensorflow.org/api_docs/python/tf/math#Segmentation
  @tf_export("ragged.row_splits_to_segment_ids")
  @dispatch.add_dispatch_support
  def row_splits_to_segment_ids(splits, name=None, out_type=None):
    """Generates the segmentation corresponding to a RaggedTensor `row_splits`.
  
    Returns an integer vector `segment_ids`, where `segment_ids[i] == j` if
    `splits[j] <= i < splits[j+1]`.  Example:
  
    >>> print(tf.ragged.row_splits_to_segment_ids([0, 3, 3, 5, 6, 9]))
     tf.Tensor([0 0 0 2 2 3 4 4 4], shape=(9,), dtype=int64)
  
    Args:
      splits: A sorted 1-D integer Tensor.  `splits[0]` must be zero.
      name: A name prefix for the returned tensor (optional).
      out_type: The dtype for the return value.  Defaults to `splits.dtype`,
        or `tf.int64` if `splits` does not have a dtype.
  
    Returns:
      A sorted 1-D integer Tensor, with `shape=[splits[-1]]`
  
    Raises:
      ValueError: If `splits` is invalid.
    """
    with ops.name_scope(name, "RaggedSplitsToSegmentIds", [splits]) as name:
      splits = ops.convert_to_tensor(
          splits, name="splits",
          preferred_dtype=dtypes.int64)
      if splits.dtype not in (dtypes.int32, dtypes.int64):
        raise ValueError("splits must have dtype int32 or int64")
      splits.shape.assert_has_rank(1)
      if tensor_shape.dimension_value(splits.shape[0]) == 0:
        raise ValueError("Invalid row_splits: []")
      if out_type is None:
        out_type = splits.dtype
      else:
        out_type = dtypes.as_dtype(out_type)
      row_lengths = splits[1:] - splits[:-1]
      nrows = array_ops.shape(splits, out_type=out_type)[-1] - 1
      indices = math_ops.range(nrows)
      return ragged_util.repeat(indices, repeats=row_lengths, axis=0)
  
  
  # For background on "segments" and "segment ids", see:
  # https://www.tensorflow.org/api_docs/python/tf/math#Segmentation
  @tf_export("ragged.segment_ids_to_row_splits")
  @dispatch.add_dispatch_support
  def segment_ids_to_row_splits(segment_ids, num_segments=None,
                                out_type=None, name=None):
    """Generates the RaggedTensor `row_splits` corresponding to a segmentation.
  
    Returns an integer vector `splits`, where `splits[0] = 0` and
    `splits[i] = splits[i-1] + count(segment_ids==i)`.  Example:
  
    >>> print(tf.ragged.segment_ids_to_row_splits([0, 0, 0, 2, 2, 3, 4, 4, 4]))
    tf.Tensor([0 3 3 5 6 9], shape=(6,), dtype=int64)
  
    Args:
      segment_ids: A 1-D integer Tensor.
      num_segments: A scalar integer indicating the number of segments.  Defaults
        to `max(segment_ids) + 1` (or zero if `segment_ids` is empty).
      out_type: The dtype for the return value.  Defaults to `segment_ids.dtype`,
        or `tf.int64` if `segment_ids` does not have a dtype.
      name: A name prefix for the returned tensor (optional).
  
    Returns:
      A sorted 1-D integer Tensor, with `shape=[num_segments + 1]`.
    """
    # Local import bincount_ops to avoid import-cycle.
    from tensorflow.python.ops import bincount_ops  # pylint: disable=g-import-not-at-top
    if out_type is None:
      if isinstance(segment_ids, ops.Tensor):
        out_type = segment_ids.dtype
      elif isinstance(num_segments, ops.Tensor):
        out_type = num_segments.dtype
      else:
        out_type = dtypes.int64
    else:
      out_type = dtypes.as_dtype(out_type)
    with ops.name_scope(name, "SegmentIdsToRaggedSplits", [segment_ids]) as name:
      # Note: we cast int64 tensors to int32, since bincount currently only
      # supports int32 inputs.
      segment_ids = ragged_util.convert_to_int_tensor(segment_ids, "segment_ids",
                                                      dtype=dtypes.int32)
      segment_ids.shape.assert_has_rank(1)
      if num_segments is not None:
        num_segments = ragged_util.convert_to_int_tensor(num_segments,
                                                         "num_segments",
                                                         dtype=dtypes.int32)
        num_segments.shape.assert_has_rank(0)
  
      row_lengths = bincount_ops.bincount(
          segment_ids,
          minlength=num_segments,
          maxlength=num_segments,
          dtype=out_type)
      splits = array_ops.concat([[0], math_ops.cumsum(row_lengths)], axis=0)
  
      # Update shape information, if possible.
      if num_segments is not None:
        const_num_segments = tensor_util.constant_value(num_segments)
        if const_num_segments is not None:
          splits.set_shape(tensor_shape.TensorShape([const_num_segments + 1]))
  
      return splits
  