# 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.dataset_ops import Dataset
from tensorflow.python.data.util import convert
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
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):
  """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


@tf_export("data.TFRecordDataset")
class TFRecordDataset(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


@tf_export("data.FixedLengthRecordDataset")
class FixedLengthRecordDataset(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
