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

import random

import numpy as np

from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base
from tensorflow.contrib.data.python.ops import dataset_ops
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import function
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.platform import test
from tensorflow.python.training import server_lib


class FlatMapDatasetTest(test.TestCase):

  # pylint: disable=g-long-lambda
  def testFlatMapDataset(self):
    repeats = [1, 2, 3, 4, 5, 0, 1]
    components = np.array(repeats, dtype=np.int64)
    iterator = (
        dataset_ops.Dataset.from_tensor_slices(components)
        .flat_map(lambda x: dataset_ops.Dataset.from_tensors([x]).repeat(x))
        .make_initializable_iterator())
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.test_session() as sess:
      sess.run(init_op)
      for i in repeats:
        for _ in range(i):
          self.assertEqual(i, sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

  def testNestedFlatMapDataset(self):
    repeats = [[1, 2], [3, 4], [5, 0], [1, 7]]
    components = np.array(repeats, dtype=np.int64)
    iterator = (
        dataset_ops.Dataset.from_tensor_slices(components)
        .flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x)
                  .flat_map(lambda y: dataset_ops.Dataset.from_tensors(y)
                            .repeat(y))).make_initializable_iterator())
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.test_session() as sess:
      sess.run(init_op)
      for row in repeats:
        for i in row:
          for _ in range(i):
            self.assertEqual(i, sess.run(get_next))

      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

  def testSharedResourceNestedFlatMapDataset(self):
    repeats = [[1, 2], [3, 4], [5, 0], [1, 7]]
    components = np.array(repeats, dtype=np.int64)
    iterator = (
        dataset_ops.Dataset.from_tensor_slices(components)
        .flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x)
                  .flat_map(lambda y: dataset_ops.Dataset.from_tensors(y)
                            .repeat(y))).make_initializable_iterator(
                                shared_name="shared_flat_map_iterator"))
    init_op = iterator.initializer
    get_next = iterator.get_next()

    # Create two concurrent sessions that share the same iterator
    # resource on the same server, and verify that a random
    # interleaving of `Session.run(get_next)` calls on the two
    # sessions yields the expected result.
    server = server_lib.Server.create_local_server()
    with session.Session(server.target) as sess1:
      with session.Session(server.target) as sess2:
        for _ in range(3):
          sess = random.choice([sess1, sess2])
          sess.run(init_op)
          for row in repeats:
            for i in row:
              for _ in range(i):
                sess = random.choice([sess1, sess2])
                self.assertEqual(i, sess.run(get_next))

        with self.assertRaises(errors.OutOfRangeError):
          sess = random.choice([sess1, sess2])
          sess.run(get_next)

  def testMapDict(self):
    iterator = (dataset_ops.Dataset.range(10)
                .map(lambda x: {"foo": x * 2, "bar": x ** 2})
                .flat_map(lambda d: dataset_ops.Dataset.from_tensors(d["foo"])
                          .repeat(d["bar"]))
                .make_initializable_iterator())
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.test_session() as sess:
      sess.run(init_op)
      for i in range(10):
        for _ in range(i ** 2):
          self.assertEqual(i * 2, sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)
  # pylint: enable=g-long-lambda

  def testSparse(self):
    def _map_fn(i):
      return sparse_tensor.SparseTensorValue(
          indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2])

    def _flat_map_fn(x):
      return dataset_ops.Dataset.from_tensor_slices(
          sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values))

    iterator = (
        dataset_ops.Dataset.range(10).map(_map_fn).flat_map(_flat_map_fn)
        .make_initializable_iterator())
    init_op = iterator.initializer
    get_next = iterator.get_next()

    with self.test_session() as sess:
      sess.run(init_op)
      for i in range(10):
        for j in range(2):
          expected = [i, 0] if j % 2 == 0 else [0, -i]
          self.assertAllEqual(expected, sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)


class FlatMapDatasetSerializationTest(
    dataset_serialization_test_base.DatasetSerializationTestBase):

  def testCore(self):
    # Complicated way of saying range(start, start+25).
    def build_ds(start):

      def map_fn(x):
        return dataset_ops.Dataset.range(x, x + 5)

      return dataset_ops.Dataset.range(start, start + 5 * 5, 5).flat_map(map_fn)

    self.run_core_tests(lambda: build_ds(0), lambda: build_ds(10), 25)

  def testMapThenFlatMap(self):

    def build_ds():

      def flat_map_fn(_):

        def map_fn(y):
          return 10 * math_ops.to_int32(y)

        return dataset_ops.Dataset.range(100).map(map_fn)

      return dataset_ops.Dataset.range(5).flat_map(flat_map_fn)

    self.run_core_tests(build_ds, None, 500)

  def testCaptureDefunInMapFn(self):

    def build_ds():

      def map_fn(x):

        @function.Defun(dtypes.int64)
        def defun_fn(x):
          return constant_op.constant(1000) + math_ops.to_int32(x)

        return dataset_ops.Dataset.from_tensor_slices([defun_fn(x)])

      return dataset_ops.Dataset.range(100).flat_map(map_fn)

    self.run_core_tests(build_ds, None, 100)

  def testDisallowVariableCapture(self):

    def build_ds():
      test_var = variable_scope.get_variable(
          name="test_var", shape=(), use_resource=True)
      return dataset_ops.Dataset.range(5).flat_map(
          lambda _: dataset_ops.Dataset.from_tensor_slices([test_var]))

    self.verify_error_on_save(build_ds, 5, errors.InvalidArgumentError)

  def testDisallowCapturingStatefulOps(self):

    def build_ds():

      def flat_map_fn(_):

        def map_fn(x):
          return random_ops.random_uniform(
              (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x)

        return dataset_ops.Dataset.range(100).map(map_fn)

      return dataset_ops.Dataset.range(5).flat_map(flat_map_fn)

    self.verify_error_on_save(build_ds, 500, errors.InvalidArgumentError)

  def testSparseCore(self):

    def _map_fn(i):
      return sparse_tensor.SparseTensorValue(
          indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2])

    def _flat_map_fn(x):
      return dataset_ops.Dataset.from_tensor_slices(
          sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values))

    def _build_ds():
      return dataset_ops.Dataset.range(10).map(_map_fn).flat_map(_flat_map_fn)

    self.run_core_tests(_build_ds, None, 20)


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