# 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.
# ==============================================================================
"""The implementation of `tf.data.Dataset.ragged_batch`."""
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import structured_function
from tensorflow.python.data.util import nest
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor
from tensorflow.python.ops.ragged import ragged_tensor


def _ragged_batch(input_dataset,
                  batch_size,
                  drop_remainder=False,
                  row_splits_dtype=dtypes.int64,
                  name=None):
  ragged_dataset = _DenseToRaggedDataset(input_dataset, row_splits_dtype, name)
  return ragged_dataset.batch(batch_size, drop_remainder)


class _DenseToRaggedDataset(dataset_ops.UnaryDataset):
  """A `Dataset` that encodes dense inputs as ragged (w/ ragged_rank=0).

  In particular:

  * Any tf.Tensor elements with rank>0 are encoded as ragged tensors with
    ragged_rank=0.  This allows tensors with varying shape to be batched
    together.
  * Any other elements are left as-is.
  """

  def __init__(self, input_dataset, row_splits_dtype, name=None):
    """Constructs a new _DenseToRaggedDataset.

    Args:
      input_dataset: The dataset whose tf.Tensor elements should be made ragged.
      row_splits_dtype: The dtype that should be used for the `row_splits` of
        any new ragged tensors.  Existing `tf.RaggedTensor` elements do *not*
        have their row_splits dtype changed.
      name: (Optional.) A string indicating a name for the `tf.data` operation.
    """
    # Replace each TensorSpec in the input dataset's structure with a
    # corresponding RaggedTensorSpec.
    def to_ragged_spec(spec):
      """Returns the new spec based on RaggedTensors."""
      if (not isinstance(spec, tensor.TensorSpec) or
          spec.shape.rank is None or
          spec.shape.is_fully_defined()):
        return spec
      else:
        ragged_rank = max([
            axis for (axis, size) in enumerate(spec.shape.as_list())
            if size is None
        ])
        return ragged_tensor.RaggedTensorSpec(
            shape=spec.shape,
            dtype=spec.dtype,
            ragged_rank=ragged_rank,
            row_splits_dtype=row_splits_dtype)

    self._structure = nest.map_structure(to_ragged_spec,
                                         input_dataset.element_spec)

    # Replace each tf.Tensor value in the input dataset with a variant-encoded
    # RaggedTensor. Since we're updating the corresponding structure to be
    # a RaggedTensorSpec, this variant-encoded tensor will be decoded with
    # RaggedTensorSpec._from_tensor_list.
    def to_ragged_variant(value):
      """Re-encode Tensors as RaggedTensors."""
      if (not isinstance(value, tensor.Tensor) or
          value.shape.rank is None or
          value.shape.is_fully_defined()):
        return value
      else:
        spec = to_ragged_spec(tensor.TensorSpec.from_tensor(value))
        if spec._ragged_rank > 0:  # pylint: disable=protected-access
          value = ragged_tensor.RaggedTensor.from_tensor(
              value, ragged_rank=spec._ragged_rank)  # pylint: disable=protected-access
        return spec._to_tensor_list(value)[0]  # pylint: disable=protected-access

    # Tuples are automatically unpacked by `dataset.map` so we repack them.
    if structured_function._should_unpack(input_dataset.element_spec):  # pylint: disable=protected-access
      map_fn = lambda *value: nest.map_structure(to_ragged_variant, value)
    else:
      map_fn = lambda value: nest.map_structure(to_ragged_variant, value)

    self._mapped_dataset = input_dataset.map(map_fn)
    self._name = name
    variant = self._mapped_dataset._variant_tensor  # pylint: disable=protected-access
    super().__init__(input_dataset, variant)

  @property
  def element_spec(self):
    return self._structure