# 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 reader Datasets."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import convert
from tensorflow.python.data.util import nest
from tensorflow.python.data.util import sparse
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.util.tf_export import tf_export


# TODO(b/64974358): Increase default buffer size to 256 MB.
_DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024  # 256 KB


@tf_export("data.TextLineDataset")
class TextLineDataset(dataset_ops.Dataset):
  """A `Dataset` comprising lines from one or more text files."""

  def __init__(self, filenames, compression_type=None, buffer_size=None):
    """Creates a `TextLineDataset`.

    Args:
      filenames: A `tf.string` tensor containing one or more filenames.
      compression_type: (Optional.) A `tf.string` scalar evaluating to one of
        `""` (no compression), `"ZLIB"`, or `"GZIP"`.
      buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
        to buffer. A value of 0 results in the default buffering values chosen
        based on the compression type.
    """
    super(TextLineDataset, self).__init__()
    self._filenames = ops.convert_to_tensor(
        filenames, dtype=dtypes.string, name="filenames")
    self._compression_type = convert.optional_param_to_tensor(
        "compression_type",
        compression_type,
        argument_default="",
        argument_dtype=dtypes.string)
    self._buffer_size = convert.optional_param_to_tensor(
        "buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)

  def _as_variant_tensor(self):
    return gen_dataset_ops.text_line_dataset(
        self._filenames, self._compression_type, self._buffer_size)

  @property
  def output_classes(self):
    return ops.Tensor

  @property
  def output_shapes(self):
    return tensor_shape.scalar()

  @property
  def output_types(self):
    return dtypes.string


class _TFRecordDataset(dataset_ops.Dataset):
  """A `Dataset` comprising records from one or more TFRecord files."""

  def __init__(self, filenames, compression_type=None, buffer_size=None):
    """Creates a `TFRecordDataset`.

    Args:
      filenames: A `tf.string` tensor containing one or more filenames.
      compression_type: (Optional.) A `tf.string` scalar evaluating to one of
        `""` (no compression), `"ZLIB"`, or `"GZIP"`.
      buffer_size: (Optional.) A `tf.int64` scalar representing the number of
        bytes in the read buffer. 0 means no buffering.
    """
    super(_TFRecordDataset, self).__init__()
    # Force the type to string even if filenames is an empty list.
    self._filenames = ops.convert_to_tensor(
        filenames, dtypes.string, name="filenames")
    self._compression_type = convert.optional_param_to_tensor(
        "compression_type",
        compression_type,
        argument_default="",
        argument_dtype=dtypes.string)
    self._buffer_size = convert.optional_param_to_tensor(
        "buffer_size",
        buffer_size,
        argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES)

  def _as_variant_tensor(self):
    return gen_dataset_ops.tf_record_dataset(
        self._filenames, self._compression_type, self._buffer_size)

  @property
  def output_classes(self):
    return ops.Tensor

  @property
  def output_shapes(self):
    return tensor_shape.TensorShape([])

  @property
  def output_types(self):
    return dtypes.string


class ParallelInterleaveDataset(dataset_ops.Dataset):
  """A `Dataset` that maps a function over its input and flattens the result."""

  def __init__(self, input_dataset, map_func, cycle_length, block_length,
               sloppy, buffer_output_elements, prefetch_input_elements):
    """See `tf.contrib.data.parallel_interleave()` for details."""
    super(ParallelInterleaveDataset, self).__init__()
    self._input_dataset = input_dataset

    @function.Defun(*nest.flatten(
        sparse.as_dense_types(input_dataset.output_types,
                              input_dataset.output_classes)))
    def tf_map_func(*args):
      """A wrapper for Defun that facilitates shape inference."""
      # Pass in shape information from the input_dataset.
      dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes,
                                            input_dataset.output_classes)
      for arg, shape in zip(args, nest.flatten(dense_shapes)):
        arg.set_shape(shape)

      nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
      nested_args = sparse.deserialize_sparse_tensors(
          nested_args, input_dataset.output_types, input_dataset.output_shapes,
          input_dataset.output_classes)
      if dataset_ops._should_unpack_args(nested_args):  # pylint: disable=protected-access
        dataset = map_func(*nested_args)
      else:
        dataset = map_func(nested_args)

      if not isinstance(dataset, dataset_ops.Dataset):
        raise TypeError("`map_func` must return a `Dataset` object.")

      self._output_classes = dataset.output_classes
      self._output_types = dataset.output_types
      self._output_shapes = dataset.output_shapes

      return dataset._as_variant_tensor()  # pylint: disable=protected-access

    self._map_func = tf_map_func
    self._map_func.add_to_graph(ops.get_default_graph())

    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._sloppy = ops.convert_to_tensor(
        sloppy, dtype=dtypes.bool, name="sloppy")
    self._buffer_output_elements = convert.optional_param_to_tensor(
        "buffer_output_elements",
        buffer_output_elements,
        argument_default=2 * block_length)
    self._prefetch_input_elements = convert.optional_param_to_tensor(
        "prefetch_input_elements",
        prefetch_input_elements,
        argument_default=2 * cycle_length)

  def _as_variant_tensor(self):
    return gen_dataset_ops.parallel_interleave_dataset(
        self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
        self._map_func.captured_inputs,
        self._cycle_length,
        self._block_length,
        self._sloppy,
        self._buffer_output_elements,
        self._prefetch_input_elements,
        f=self._map_func,
        output_types=nest.flatten(
            sparse.as_dense_types(self.output_types, self.output_classes)),
        output_shapes=nest.flatten(
            sparse.as_dense_shapes(self.output_shapes, self.output_classes)))

  @property
  def output_classes(self):
    return self._output_classes

  @property
  def output_shapes(self):
    return self._output_shapes

  @property
  def output_types(self):
    return self._output_types


@tf_export("data.TFRecordDataset")
class TFRecordDataset(dataset_ops.Dataset):
  """A `Dataset` comprising records from one or more TFRecord files."""

  def __init__(self, filenames, compression_type=None, buffer_size=None,
               num_parallel_reads=None):
    """Creates a `TFRecordDataset` to read for one or more TFRecord files.

    NOTE: The `num_parallel_reads` argument can be used to improve performance
    when reading from a remote filesystem.

    Args:
      filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or
        more filenames.
      compression_type: (Optional.) A `tf.string` scalar evaluating to one of
        `""` (no compression), `"ZLIB"`, or `"GZIP"`.
      buffer_size: (Optional.) A `tf.int64` scalar representing the number of
        bytes in the read buffer. 0 means no buffering.
      num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
        number of files to read in parallel. Defaults to reading files
        sequentially.

    Raises:
      TypeError: If any argument does not have the expected type.
      ValueError: If any argument does not have the expected shape.
    """
    super(TFRecordDataset, self).__init__()
    if isinstance(filenames, dataset_ops.Dataset):
      if filenames.output_types != dtypes.string:
        raise TypeError(
            "`filenames` must be a `tf.data.Dataset` of `tf.string` elements.")
      if not filenames.output_shapes.is_compatible_with(tensor_shape.scalar()):
        raise ValueError(
            "`filenames` must be a `tf.data.Dataset` of scalar `tf.string` "
            "elements.")
    else:
      filenames = ops.convert_to_tensor(filenames, dtype=dtypes.string)
      filenames = array_ops.reshape(filenames, [-1], name="flat_filenames")
      filenames = dataset_ops.Dataset.from_tensor_slices(filenames)

    def read_one_file(filename):
      return _TFRecordDataset(filename, compression_type, buffer_size)

    if num_parallel_reads is None:
      self._impl = filenames.flat_map(read_one_file)
    else:
      self._impl = ParallelInterleaveDataset(
          filenames, read_one_file, cycle_length=num_parallel_reads,
          block_length=1, sloppy=False, buffer_output_elements=None,
          prefetch_input_elements=None)

  def _as_variant_tensor(self):
    return self._impl._as_variant_tensor()  # pylint: disable=protected-access

  @property
  def output_classes(self):
    return self._impl.output_classes

  @property
  def output_shapes(self):
    return self._impl.output_shapes

  @property
  def output_types(self):
    return self._impl.output_types


@tf_export("data.FixedLengthRecordDataset")
class FixedLengthRecordDataset(dataset_ops.Dataset):
  """A `Dataset` of fixed-length records from one or more binary files."""

  def __init__(self,
               filenames,
               record_bytes,
               header_bytes=None,
               footer_bytes=None,
               buffer_size=None):
    """Creates a `FixedLengthRecordDataset`.

    Args:
      filenames: A `tf.string` tensor containing one or more filenames.
      record_bytes: A `tf.int64` scalar representing the number of bytes in
        each record.
      header_bytes: (Optional.) A `tf.int64` scalar representing the number of
        bytes to skip at the start of a file.
      footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
        bytes to ignore at the end of a file.
      buffer_size: (Optional.) A `tf.int64` scalar representing the number of
        bytes to buffer when reading.
    """
    super(FixedLengthRecordDataset, self).__init__()
    self._filenames = ops.convert_to_tensor(
        filenames, dtype=dtypes.string, name="filenames")
    self._record_bytes = ops.convert_to_tensor(
        record_bytes, dtype=dtypes.int64, name="record_bytes")

    self._header_bytes = convert.optional_param_to_tensor(
        "header_bytes", header_bytes)
    self._footer_bytes = convert.optional_param_to_tensor(
        "footer_bytes", footer_bytes)
    self._buffer_size = convert.optional_param_to_tensor(
        "buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)

  def _as_variant_tensor(self):
    return gen_dataset_ops.fixed_length_record_dataset(
        self._filenames, self._header_bytes, self._record_bytes,
        self._footer_bytes, self._buffer_size)

  @property
  def output_classes(self):
    return ops.Tensor

  @property
  def output_shapes(self):
    return tensor_shape.scalar()

  @property
  def output_types(self):
    return dtypes.string
