# 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.
# ==============================================================================
"""Tests for the experimental input pipeline ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.python.data.experimental.ops import batching
from tensorflow.python.data.experimental.ops import optimization
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test


class OptimizeDatasetTest(test_base.DatasetTestBase):

  def testOptimizationDefault(self):
    dataset = dataset_ops.Dataset.range(10).apply(
        optimization.assert_next(["Map",
                                  "Batch"])).map(lambda x: x * x).batch(10)
    iterator = dataset.with_options(
        dataset_ops.Options()).make_one_shot_iterator()
    get_next = iterator.get_next()

    with self.cached_session() as sess:
      self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

  def testOptimizationFusion(self):
    dataset = dataset_ops.Dataset.range(10).apply(
        optimization.assert_next(
            ["MapAndBatch"])).map(lambda x: x * x).batch(10)
    options = dataset_ops.Options()
    options.experimental_map_and_batch_fusion = True
    dataset = dataset.with_options(options)
    iterator = dataset.make_one_shot_iterator()
    get_next = iterator.get_next()

    with self.cached_session() as sess:
      self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

  def testNumaAwareRewrite(self):
    dataset = dataset_ops.Dataset.range(10).apply(
        optimization.assert_next(["NumaMapAndBatch"])).apply(
            batching.map_and_batch(lambda x: x * x, 10))
    options = dataset_ops.Options()
    options.experimental_numa_aware = True
    dataset = dataset.with_options(options)
    iterator = dataset.make_one_shot_iterator()
    get_next = iterator.get_next()

    with self.cached_session() as sess:
      self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

  def testOptimizationStatefulFunction(self):
    dataset = dataset_ops.Dataset.range(10).map(
        lambda _: random_ops.random_uniform([])).batch(10)
    options = dataset_ops.Options()
    options.experimental_map_and_batch_fusion = True
    dataset = dataset.with_options(options)
    iterator = dataset.make_one_shot_iterator()
    get_next = iterator.get_next()

    with self.cached_session() as sess:
      sess.run(get_next)

  def testOptimizationLargeInputFromTensor(self):
    input_t = array_ops.placeholder(dtypes.int32, (None, None, None))
    dataset = dataset_ops.Dataset.from_tensors(input_t)
    options = dataset_ops.Options()
    options.experimental_map_and_batch_fusion = True
    dataset = dataset.with_options(options)
    iterator = dataset.make_initializable_iterator()
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.cached_session() as sess:
      sess.run(init_op, {input_t: np.ones([512, 1024, 1025], np.int32)})
      sess.run(get_next)

  def testOptimizationLargeInputFromTensorSlices(self):
    input_t = array_ops.placeholder(dtypes.int32, (None, None, None, None))
    dataset = dataset_ops.Dataset.from_tensor_slices(input_t)
    options = dataset_ops.Options()
    options.experimental_map_and_batch_fusion = True
    dataset = dataset.with_options(options)
    iterator = dataset.make_initializable_iterator()
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.cached_session() as sess:
      sess.run(init_op, {input_t: np.ones([1, 512, 1024, 1025], np.int32)})
      sess.run(get_next)


if __name__ == "__main__":
  test.main()