text: import sys
  import time
  import collections
  from collections import Counter
  import pytest
  
  import ray
  from ray.util.iter import from_items, from_iterators, from_range, \
      from_actors, ParallelIteratorWorker, LocalIterator
  from ray.test_utils import Semaphore
  
  
  def test_select_shards(ray_start_regular_shared):
      it = from_items([1, 2, 3, 4], num_shards=4)
      it1 = it.select_shards([0, 2])
      it2 = it.select_shards([1, 3])
      assert it1.take(4) == [1, 3]
      assert it2.take(4) == [2, 4]
  
  
  def test_transform(ray_start_regular_shared):
      def f(it):
          for item in it:
              yield item * 2
  
      def g(it):
          for item in it:
              if item >= 2:
                  yield item
  
      it = from_range(4).transform(f)
      assert repr(it) == "ParallelIterator[from_range[4, shards=2].transform()]"
      assert list(it.gather_sync()) == [0, 4, 2, 6]
  
      it = from_range(4)
      assert list(it.gather_sync().transform(g)) == [2, 3]
  
  
  def test_metrics(ray_start_regular_shared):
      it = from_items([1, 2, 3, 4], num_shards=1)
      it2 = from_items([1, 2, 3, 4], num_shards=1)
  
      def f(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["foo"] += x
          return metrics.counters["foo"]
  
      it = it.gather_sync().for_each(f)
      it2 = it2.gather_sync().for_each(f)
  
      # Tests iterators have isolated contexts.
      assert it.take(4) == [1, 3, 6, 10]
      assert it2.take(4) == [1, 3, 6, 10]
  
  
  def test_zip_with_source_actor(ray_start_regular_shared):
      it = from_items([1, 2, 3, 4], num_shards=2)
      counts = collections.defaultdict(int)
      for actor, value in it.gather_async().zip_with_source_actor():
          counts[actor] += 1
      assert len(counts) == 2
      for a, count in counts.items():
          assert count == 2
  
  
  def test_metrics_union(ray_start_regular_shared):
      it1 = from_items([1, 2, 3, 4], num_shards=1)
      it2 = from_items([1, 2, 3, 4], num_shards=1)
  
      def foo_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["foo"] += x
          return metrics.counters["foo"]
  
      def bar_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["bar"] += 100
          return metrics.counters["bar"]
  
      def verify_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["n"] += 1
          # Check the metrics context is shared.
          if metrics.counters["n"] >= 2:
              assert "foo" in metrics.counters
              assert "bar" in metrics.counters
          return x
  
      it1 = it1.gather_async().for_each(foo_metrics)
      it2 = it2.gather_async().for_each(bar_metrics)
      it3 = it1.union(it2, deterministic=True)
      it3 = it3.for_each(verify_metrics)
      assert it3.take(10) == [1, 100, 3, 200, 6, 300, 10, 400]
  
  
  def test_metrics_union_recursive(ray_start_regular_shared):
      it1 = from_items([1, 2, 3, 4], num_shards=1)
      it2 = from_items([1, 2, 3, 4], num_shards=1)
      it3 = from_items([1, 2, 3, 4], num_shards=1)
  
      def foo_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["foo"] += 1
          return metrics.counters["foo"]
  
      def bar_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["bar"] += 1
          return metrics.counters["bar"]
  
      def baz_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["baz"] += 1
          return metrics.counters["baz"]
  
      def verify_metrics(x):
          metrics = LocalIterator.get_metrics()
          metrics.counters["n"] += 1
          # Check the metrics context is shared recursively.
          print(metrics.counters)
          if metrics.counters["n"] >= 3:
              assert "foo" in metrics.counters
              assert "bar" in metrics.counters
              assert "baz" in metrics.counters
          return x
  
      it1 = it1.gather_async().for_each(foo_metrics)
      it2 = it2.gather_async().for_each(bar_metrics)
      it3 = it3.gather_async().for_each(baz_metrics)
      it12 = it1.union(it2, deterministic=True)
      it123 = it12.union(it3, deterministic=True)
      out = it123.for_each(verify_metrics)
      assert out.take(20) == [1, 1, 1, 2, 2, 3, 2, 4, 3, 3, 4, 4]
  
  
  def test_from_items(ray_start_regular_shared):
      it = from_items([1, 2, 3, 4])
      assert repr(it) == "ParallelIterator[from_items[int, 4, shards=2]]"
      assert list(it.gather_sync()) == [1, 2, 3, 4]
      assert next(it.gather_sync()) == 1
  
  
  def test_from_items_repeat(ray_start_regular_shared):
      it = from_items([1, 2, 3, 4], repeat=True)
      assert repr(
          it) == "ParallelIterator[from_items[int, 4, shards=2, repeat=True]]"
      assert it.take(8) == [1, 2, 3, 4, 1, 2, 3, 4]
  
  
  def test_from_iterators(ray_start_regular_shared):
      it = from_iterators([range(2), range(2)])
      assert repr(it) == "ParallelIterator[from_iterators[shards=2]]"
      assert list(it.gather_sync()) == [0, 0, 1, 1]
  
  
  def test_from_range(ray_start_regular_shared):
      it = from_range(4)
      assert repr(it) == "ParallelIterator[from_range[4, shards=2]]"
      assert list(it.gather_sync()) == [0, 2, 1, 3]
  
  
  def test_from_actors(ray_start_regular_shared):
      @ray.remote
      class CustomWorker(ParallelIteratorWorker):
          def __init__(self, data):
              ParallelIteratorWorker.__init__(self, data, False)
  
      a = CustomWorker.remote([1, 2])
      b = CustomWorker.remote([3, 4])
      it = from_actors([a, b])
      assert repr(it) == "ParallelIterator[from_actors[shards=2]]"
      assert list(it.gather_sync()) == [1, 3, 2, 4]
  
  
  def test_for_each(ray_start_regular_shared):
      it = from_range(4).for_each(lambda x: x * 2)
      assert repr(it) == "ParallelIterator[from_range[4, shards=2].for_each()]"
      assert list(it.gather_sync()) == [0, 4, 2, 6]
  
  
  def test_for_each_concur_async(ray_start_regular_shared):
      main_wait = Semaphore.remote(value=0)
      test_wait = Semaphore.remote(value=0)
  
      def task(x):
          i, main_wait, test_wait = x
          ray.get(main_wait.release.remote())
          ray.get(test_wait.acquire.remote())
          return i + 10
  
      @ray.remote(num_cpus=0.01)
      def to_list(it):
          return list(it)
  
      it = from_items(
          [(i, main_wait, test_wait) for i in range(8)], num_shards=2)
      it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.01})
  
      list_promise = to_list.remote(it.gather_async())
  
      for i in range(4):
          assert i in [0, 1, 2, 3]
          ray.get(main_wait.acquire.remote())
  
      # There should be exactly 4 tasks executing at this point.
      assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
  
      # When we finish one task, exactly one more should start.
      ray.get(test_wait.release.remote())
      ray.get(main_wait.acquire.remote())
      assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
  
      # Finish everything and make sure the output matches a regular iterator.
      for i in range(7):
          ray.get(test_wait.release.remote())
  
      assert repr(
          it) == "ParallelIterator[from_items[tuple, 8, shards=2].for_each()]"
      result_list = ray.get(list_promise)
      assert set(result_list) == set(range(10, 18))
  
  
  def test_for_each_concur_sync(ray_start_regular_shared):
      main_wait = Semaphore.remote(value=0)
      test_wait = Semaphore.remote(value=0)
  
      def task(x):
          i, main_wait, test_wait = x
          ray.get(main_wait.release.remote())
          ray.get(test_wait.acquire.remote())
          return i + 10
  
      @ray.remote(num_cpus=0.01)
      def to_list(it):
          return list(it)
  
      it = from_items(
          [(i, main_wait, test_wait) for i in range(8)], num_shards=2)
      it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.01})
  
      list_promise = to_list.remote(it.gather_sync())
  
      for i in range(4):
          assert i in [0, 1, 2, 3]
          ray.get(main_wait.acquire.remote())
  
      # There should be exactly 4 tasks executing at this point.
      assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
  
      for i in range(8):
          ray.get(test_wait.release.remote())
  
      assert repr(
          it) == "ParallelIterator[from_items[tuple, 8, shards=2].for_each()]"
      result_list = ray.get(list_promise)
      assert set(result_list) == set(range(10, 18))
  
  
  def test_combine(ray_start_regular_shared):
      it = from_range(4, 1).combine(lambda x: [x, x])
      assert repr(it) == "ParallelIterator[from_range[4, shards=1].combine()]"
      assert list(it.gather_sync()) == [0, 0, 1, 1, 2, 2, 3, 3]
  
  
  def test_duplicate(ray_start_regular_shared):
      it = from_range(5, num_shards=1)
  
      it1, it2 = it.gather_sync().duplicate(2)
      it1 = it1.batch(2)
  
      it3 = it1.union(it2, deterministic=False)
      results = it3.take(20)
      assert results == [0, [0, 1], 1, 2, [2, 3], 3, 4, [4]]
  
  
  def test_chain(ray_start_regular_shared):
      it = from_range(4).for_each(lambda x: x * 2).for_each(lambda x: x * 2)
      assert repr(
          it
      ) == "ParallelIterator[from_range[4, shards=2].for_each().for_each()]"
      assert list(it.gather_sync()) == [0, 8, 4, 12]
  
  
  def test_filter(ray_start_regular_shared):
      it = from_range(4).filter(lambda x: x < 3)
      assert repr(it) == "ParallelIterator[from_range[4, shards=2].filter()]"
      assert list(it.gather_sync()) == [0, 2, 1]
  
  
  @pytest.mark.skipif(sys.platform == "win32", reason="Failing on Windows.")
  def test_local_shuffle(ray_start_regular_shared):
      # confirm that no data disappears, and they all stay within the same shard
      it = from_range(8, num_shards=2).local_shuffle(shuffle_buffer_size=2)
      assert repr(it) == ("ParallelIterator[from_range[8, shards=2]" +
                          ".local_shuffle(shuffle_buffer_size=2, seed=None)]")
      shard_0 = it.get_shard(0)
      shard_1 = it.get_shard(1)
      assert set(shard_0) == {0, 1, 2, 3}
      assert set(shard_1) == {4, 5, 6, 7}
  
      # check that shuffling results in different orders
      it1 = from_range(100, num_shards=10).local_shuffle(shuffle_buffer_size=5)
      it2 = from_range(100, num_shards=10).local_shuffle(shuffle_buffer_size=5)
      assert list(it1.gather_sync()) != list(it2.gather_sync())
  
      # buffer size of 1 should not result in any shuffling
      it3 = from_range(10, num_shards=1).local_shuffle(shuffle_buffer_size=1)
      assert list(it3.gather_sync()) == list(range(10))
  
      # statistical test
      it4 = from_items(
          [0, 1] * 10000, num_shards=1).local_shuffle(shuffle_buffer_size=100)
      result = "".join(it4.gather_sync().for_each(str))
      freq_counter = Counter(zip(result[:-1], result[1:]))
      assert len(freq_counter) == 4
      for key, value in freq_counter.items():
          assert value / len(freq_counter) > 0.2
  
  
  def test_repartition_less(ray_start_regular_shared):
      it = from_range(9, num_shards=3)
      # chaining operations after a repartition should work
      it1 = it.repartition(2).for_each(lambda x: 2 * x)
      assert repr(it1) == ("ParallelIterator[from_range[9, " +
                           "shards=3].repartition[num_partitions=2].for_each()]")
  
      assert it1.num_shards() == 2
      shard_0_set = set(it1.get_shard(0))
      shard_1_set = set(it1.get_shard(1))
      assert shard_0_set == {0, 4, 6, 10, 12, 16}
      assert shard_1_set == {2, 8, 14}
  
  
  def test_repartition_more(ray_start_regular_shared):
      it = from_range(100, 2).repartition(3)
      assert it.num_shards() == 3
      assert set(it.get_shard(0)) == set(range(0, 50, 3)) | set(
          (range(50, 100, 3)))
      assert set(
          it.get_shard(1)) == set(range(1, 50, 3)) | set(range(51, 100, 3))
      assert set(
          it.get_shard(2)) == set(range(2, 50, 3)) | set(range(52, 100, 3))
  
  
  def test_repartition_consistent(ray_start_regular_shared):
      # repartition should be deterministic
      it1 = from_range(9, num_shards=1).repartition(2)
      it2 = from_range(9, num_shards=1).repartition(2)
      # union should work after repartition
      it3 = it1.union(it2)
      assert it1.num_shards() == 2
      assert it2.num_shards() == 2
      assert set(it1.get_shard(0)) == set(it2.get_shard(0))
      assert set(it1.get_shard(1)) == set(it2.get_shard(1))
  
      assert it3.num_shards() == 4
      assert set(it3.gather_async()) == set(it1.gather_async()) | set(
          it2.gather_async())
  
  
  def test_batch(ray_start_regular_shared):
      it = from_range(4, 1).batch(2)
      assert repr(it) == "ParallelIterator[from_range[4, shards=1].batch(2)]"
      assert list(it.gather_sync()) == [[0, 1], [2, 3]]
  
  
  def test_flatten(ray_start_regular_shared):
      it = from_items([[1, 2], [3, 4]], 1).flatten()
      assert repr(
          it) == "ParallelIterator[from_items[list, 2, shards=1].flatten()]"
      assert list(it.gather_sync()) == [1, 2, 3, 4]
  
  
  def test_gather_sync(ray_start_regular_shared):
      it = from_range(4)
      it = it.gather_sync()
      assert (
          repr(it) == "LocalIterator[ParallelIterator[from_range[4, shards=2]]"
          ".gather_sync()]")
      assert sorted(it) == [0, 1, 2, 3]
  
  
  def test_gather_async(ray_start_regular_shared):
      it = from_range(4)
      it = it.gather_async()
      assert (
          repr(it) == "LocalIterator[ParallelIterator[from_range[4, shards=2]]"
          ".gather_async()]")
      assert sorted(it) == [0, 1, 2, 3]
  
  
  def test_gather_async_optimized(ray_start_regular_shared):
      it = from_range(100)
      it = it.gather_async(batch_ms=100, num_async=4)
      assert sorted(it) == list(range(100))
  
  
  def test_get_shard_optimized(ray_start_regular_shared):
      it = from_range(6, num_shards=3)
      shard1 = it.get_shard(shard_index=0, batch_ms=25, num_async=2)
      shard2 = it.get_shard(shard_index=1, batch_ms=15, num_async=3)
      shard3 = it.get_shard(shard_index=2, batch_ms=5, num_async=4)
      assert list(shard1) == [0, 1]
      assert list(shard2) == [2, 3]
      assert list(shard3) == [4, 5]
  
  
  # Tested on 5/13/20
  # Run on 2019 Macbook Pro with 8 cores, 16 threads
  # 14.52 sec
  # 14.64 sec
  # 0.935 sec
  # 0.515 sec
  """
  def test_gather_async_optimized_benchmark(ray_start_regular_shared):
      import numpy as np
      import tensorflow as tf
      train, _ = tf.keras.datasets.fashion_mnist.load_data()
      images, labels = train
      num_bytes = images.nbytes / 1e6
      items = list(images)
      it = from_items(items, num_shards=4)
      it = it.for_each(lambda img: img/255)
      #local_it = it.gather_async(batch_ms=0, num_async=1)
      #local_it = it.gather_async(batch_ms=0, num_async=3)
      #local_it = it.gather_async(batch_ms=10, num_async=1)
      #local_it = it.gather_async(batch_ms=10, num_async=3)
  
      # dummy iterations
      for i in range(20):
          record = next(local_it)
  
      start_time = time.time()
      #print(start_time)
      count = 0
      for record in local_it:
          count += 1
      assert count == len(items) - 20
      end_time = time.time() - start_time
      print(end_time)
  """
  
  
  def test_batch_across_shards(ray_start_regular_shared):
      it = from_iterators([[0, 1], [2, 3]])
      it = it.batch_across_shards()
      assert (
          repr(it) == "LocalIterator[ParallelIterator[from_iterators[shards=2]]"
          ".batch_across_shards()]")
      assert sorted(it) == [[0, 2], [1, 3]]
  
  
  def test_remote(ray_start_regular_shared):
      it = from_iterators([[0, 1], [3, 4], [5, 6, 7]])
      assert it.num_shards() == 3
  
      @ray.remote
      def get_shard(it, i):
          return list(it.get_shard(i))
  
      assert ray.get(get_shard.remote(it, 0)) == [0, 1]
      assert ray.get(get_shard.remote(it, 1)) == [3, 4]
      assert ray.get(get_shard.remote(it, 2)) == [5, 6, 7]
  
      @ray.remote
      def check_remote(it):
          assert ray.get(get_shard.remote(it, 0)) == [0, 1]
          assert ray.get(get_shard.remote(it, 1)) == [3, 4]
          assert ray.get(get_shard.remote(it, 2)) == [5, 6, 7]
  
      ray.get(check_remote.remote(it))
  
  
  def test_union(ray_start_regular_shared):
      it1 = from_items(["a", "b", "c"], 1)
      it2 = from_items(["x", "y", "z"], 1)
      it = it1.union(it2)
      assert (repr(it) == "ParallelIterator[ParallelUnion[ParallelIterator["
              "from_items[str, 3, shards=1]], ParallelIterator["
              "from_items[str, 3, shards=1]]]]")
      assert list(it.gather_sync()) == ["a", "x", "b", "y", "c", "z"]
  
  
  def test_union_local(ray_start_regular_shared):
      it1 = from_items(["a", "b", "c"], 1).gather_async()
      it2 = from_range(5, 2).for_each(str).gather_async()
      it = it1.union(it2)
      assert sorted(it) == ["0", "1", "2", "3", "4", "a", "b", "c"]
  
  
  def test_union_async(ray_start_regular_shared):
      def gen_fast():
          for i in range(10):
              time.sleep(0.05)
              print("PRODUCE FAST", i)
              yield i
  
      def gen_slow():
          for i in range(10):
              time.sleep(0.3)
              print("PRODUCE SLOW", i)
              yield i
  
      it1 = from_iterators([gen_fast]).for_each(lambda x: ("fast", x))
      it2 = from_iterators([gen_slow]).for_each(lambda x: ("slow", x))
      it = it1.union(it2)
      results = list(it.gather_async())
      assert all(x[0] == "slow" for x in results[-3:]), results
  
  
  def test_union_local_async(ray_start_regular_shared):
      def gen_fast():
          for i in range(10):
              time.sleep(0.05)
              print("PRODUCE FAST", i)
              yield i
  
      def gen_slow():
          for i in range(10):
              time.sleep(0.3)
              print("PRODUCE SLOW", i)
              yield i
  
      it1 = from_iterators([gen_fast]).for_each(lambda x: ("fast", x))
      it2 = from_iterators([gen_slow]).for_each(lambda x: ("slow", x))
      it = it1.gather_async().union(it2.gather_async())
      assert (repr(it) == "LocalIterator[LocalUnion[LocalIterator["
              "ParallelIterator[from_iterators[shards=1].for_each()]"
              ".gather_async()], LocalIterator[ParallelIterator["
              "from_iterators[shards=1].for_each()].gather_async()]]]")
      results = list(it)
      assert all(x[0] == "slow" for x in results[-3:]), results
  
  
  def test_serialization(ray_start_regular_shared):
      it = (from_items([1, 2, 3, 4]).gather_sync().for_each(lambda x: x)
            .filter(lambda x: True).batch(2).flatten())
      assert (repr(it) == "LocalIterator[ParallelIterator["
              "from_items[int, 4, shards=2]].gather_sync()."
              "for_each().filter().batch(2).flatten()]")
  
      @ray.remote
      def get(it):
          return list(it)
  
      assert ray.get(get.remote(it)) == [1, 2, 3, 4]
  
  
  if __name__ == "__main__":
      import sys
      sys.exit(pytest.main(["-v", __file__]))
  