text: # Copyright 2015 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 tensorflow.ops.reverse_sequence_op."""
  
  import itertools
  import sys
  
  import numpy as np
  
  from tensorflow.python.client import session
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.platform import benchmark
  from tensorflow.python.platform import test
  
  
  class WhereOpTest(test.TestCase):
  
    def _testWhere(self, x, truth, expected_err_re=None, fn=array_ops.where):
      with self.cached_session():
        ans = fn(x)
        self.assertTrue(ans.get_shape().is_compatible_with([None, x.ndim]))
        if expected_err_re is None:
          tf_ans = self.evaluate(ans)
          self.assertAllClose(tf_ans, truth, atol=1e-10)
        else:
          with self.assertRaisesOpError(expected_err_re):
            self.evaluate(ans)
  
    def _testWrongNumbers(self, fn=array_ops.where):
      with self.session():
        with self.assertRaises(ValueError):
          fn([False, True], [1, 2], None)
        with self.assertRaises(ValueError):
          fn([False, True], None, [1, 2])
  
    def _testBasicVec(self, fn=array_ops.where):
      x = np.asarray([True, False])
      truth = np.asarray([[0]], dtype=np.int64)
      self._testWhere(x, truth, None, fn)
  
      x = np.asarray([False, True, False])
      truth = np.asarray([[1]], dtype=np.int64)
      self._testWhere(x, truth, None, fn)
  
      x = np.asarray([False, False, True, False, True])
      truth = np.asarray([[2], [4]], dtype=np.int64)
      self._testWhere(x, truth, None, fn)
  
    def _testRandomVec(self, fn=array_ops.where):
      x = np.random.rand(1000000) > 0.5
      truth = np.vstack([np.where(x)[0].astype(np.int64)]).T
      self._testWhere(x, truth, None, fn)
  
    def _testBasicMat(self, fn=array_ops.where):
      x = np.asarray([[True, False], [True, False]])
  
      # Ensure RowMajor mode
      truth = np.asarray([[0, 0], [1, 0]], dtype=np.int64)
  
      self._testWhere(x, truth, None, fn)
  
    def _testBasic3Tensor(self, fn=array_ops.where):
      x = np.asarray([[[True, False], [True, False]],
                      [[False, True], [False, True]],
                      [[False, False], [False, True]]])
  
      # Ensure RowMajor mode
      truth = np.asarray(
          [[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 1], [2, 1, 1]], dtype=np.int64)
  
      self._testWhere(x, truth, None, fn)
  
    def _testRandom(self, dtype, expected_err_re=None, fn=array_ops.where):
      shape = [127, 33, 53]
      x = np.random.randn(*shape) + 1j * np.random.randn(*shape)
      x = (np.random.randn(*shape) > 0).astype(dtype)
      truth = np.where(np.abs(x) > 0)  # Tuples of indices by axis.
      truth = np.vstack(truth).T  # Convert to [num_true, indices].
      self._testWhere(x, truth, expected_err_re, fn)
  
    def _testThreeArgument(self, fn=array_ops.where):
      x = np.array([[-2, 3, -1], [1, -3, -3]])
      np_val = np.where(x > 0, x * x, -x)
      with self.test_session():
        tf_val = self.evaluate(fn(constant_op.constant(x) > 0, x * x, -x))
      self.assertAllEqual(tf_val, np_val)
  
    def testWrongNumbers(self):
      self._testWrongNumbers()
  
    @test_util.run_deprecated_v1
    def testBasicVec(self):
      self._testBasicVec()
  
    @test_util.run_deprecated_v1
    def testRandomVec(self):
      self._testRandomVec()
  
    @test_util.run_deprecated_v1
    def testBasicMat(self):
      self._testBasicMat()
  
    @test_util.run_deprecated_v1
    def testBasic3Tensor(self):
      self._testBasic3Tensor()
  
    @test_util.run_deprecated_v1
    def testRandomBool(self):
      self._testRandom(np.bool_)
  
    @test_util.run_deprecated_v1
    def testRandomInt32(self):
      self._testRandom(np.int32)
  
    @test_util.run_deprecated_v1
    def testRandomInt64(self):
      self._testRandom(np.int64)
  
    @test_util.run_deprecated_v1
    def testRandomFloat(self):
      self._testRandom(np.float32)
  
    @test_util.run_deprecated_v1
    def testRandomDouble(self):
      self._testRandom(np.float64)
  
    @test_util.run_deprecated_v1
    def testRandomComplex64(self):
      self._testRandom(np.complex64)
  
    @test_util.run_deprecated_v1
    def testRandomComplex128(self):
      self._testRandom(np.complex128)
  
    @test_util.run_deprecated_v1
    def testRandomUint8(self):
      self._testRandom(np.uint8)
  
    @test_util.run_deprecated_v1
    def testRandomInt8(self):
      self._testRandom(np.int8)
  
    @test_util.run_deprecated_v1
    def testRandomInt16(self):
      self._testRandom(np.int16)
  
    @test_util.run_deprecated_v1
    def testThreeArgument(self):
      self._testThreeArgument()
  
    def testV2WrongNumbers(self):
      self._testWrongNumbers(array_ops.where_v2)
  
    def testV2BasicVec(self):
      self._testBasicVec(array_ops.where_v2)
  
    def testV2RandomVec(self):
      self._testRandomVec(array_ops.where_v2)
  
    def testV2BasicMat(self):
      self._testBasicMat(array_ops.where_v2)
  
    def testV2Basic3Tensor(self):
      self._testBasic3Tensor(array_ops.where_v2)
  
    def testV2RandomBool(self):
      self._testRandom(np.bool_, None, array_ops.where_v2)
  
    def testV2RandomInt32(self):
      self._testRandom(np.int32, None, array_ops.where_v2)
  
    def testV2RandomInt64(self):
      self._testRandom(np.int64, None, array_ops.where_v2)
  
    def testV2RandomFloat(self):
      self._testRandom(np.float32, None, array_ops.where_v2)
  
    def testV2RandomDouble(self):
      self._testRandom(np.float64, None, array_ops.where_v2)
  
    def testV2RandomComplex64(self):
      self._testRandom(np.complex64, None, array_ops.where_v2)
  
    def testV2RandomComplex128(self):
      self._testRandom(np.complex128, None, array_ops.where_v2)
  
    def testV2RandomUint8(self):
      self._testRandom(np.uint8, None, array_ops.where_v2)
  
    def testV2RandomInt8(self):
      self._testRandom(np.int8, None, array_ops.where_v2)
  
    def testV2RandomInt16(self):
      self._testRandom(np.int16, None, array_ops.where_v2)
  
    def testV2ThreeArgument(self):
      self._testThreeArgument(array_ops.where_v2)
  
    def testV2Broadcasting(self):
      f = np.random.normal(0, 1, (3, 5, 1, 1))
      x = np.zeros((7, 11))
      y = np.ones((7, 11))
      np_val = np.where(f < 0, x, y)
      with self.test_session():
        tf_val = self.evaluate(
            array_ops.where_v2(constant_op.constant(f) < 0, x, y))
      self.assertAllEqual(tf_val, np_val)
  
    def testV2ScalarBroadcasting(self):
      x = np.zeros((7, 11))
      y = np.ones((7, 11))
      np_val = np.where(True, x, y)
      with self.test_session():
        tf_val = self.evaluate(
            array_ops.where_v2(
                constant_op.constant(True, dtype=dtypes.bool), x, y))
      self.assertAllEqual(tf_val, np_val)
  
    def testV2VectorBroadcasting(self):
      x = np.zeros(7)
      y = np.ones(7)
      np_val = np.where([True], x, y)
      with self.test_session():
        tf_val = self.evaluate(
            array_ops.where_v2(
                constant_op.constant([True], dtype=dtypes.bool), x, y))
      self.assertAllEqual(tf_val, np_val)
  
    def testV2PredBroadcasting(self):
      pred = np.array([1, 0, 0]).reshape((3, 1))
      x = np.random.randn(3, 4)
      y = np.random.randn(3, 4)
      np_val = np.where(pred, x, y)
      with self.test_session():
        tf_val = self.evaluate(array_ops.where_v2(pred, x, y))
      self.assertAllClose(tf_val, np_val)
  
    @test_util.run_deprecated_v1
    def testBatchSelect(self):
      x = np.array([[-2, 3, -1] * 64, [1, -3, -3] * 64] * 8192)  # [16384, 192]
      c_mat = np.array([[False] * 192, [True] * 192] * 8192)  # [16384, 192]
      c_vec = np.array([False, True] * 8192)  # [16384]
      np_val = np.where(c_mat, x * x, -x)
      with self.session():
        tf_val = array_ops.where(c_vec, x * x, -x).eval()
      self.assertAllEqual(tf_val, np_val)
  
  
  class WhereBenchmark(test.Benchmark):
  
    def benchmarkWhere(self):
      for (m, n, p, use_gpu) in itertools.product(
          [10],
          [10, 100, 1000, 10000, 100000, 1000000],
          [0.01, 0.5, 0.99],
          [False, True]):
        name = "m_%d_n_%d_p_%g_use_gpu_%s" % (m, n, p, use_gpu)
        device = "/%s:0" % ("gpu" if use_gpu else "cpu")
        with ops.Graph().as_default():
          with ops.device(device):
            x = random_ops.random_uniform((m, n), dtype=dtypes.float32) <= p
            v = resource_variable_ops.ResourceVariable(x)
            op = array_ops.where(v)
          with session.Session(config=benchmark.benchmark_config()) as sess:
            self.evaluate(v.initializer)
            r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
            gb_processed_input = m * n / 1.0e9
            # approximate size of output: m*n*p int64s for each axis.
            gb_processed_output = 2 * 8 * m * n * p / 1.0e9
            gb_processed = gb_processed_input + gb_processed_output
            throughput = gb_processed / r["wall_time"]
            print("Benchmark: %s \t wall_time: %0.03g s \t "
                  "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
            sys.stdout.flush()
  
    def benchmarkBatchSelect(self):
      for (m, n, use_gpu) in itertools.product([1000, 10000, 100000],
                                               [10, 100, 1000], [False, True]):
        name = "m_%d_n_%d_use_gpu_%s" % (m, n, use_gpu)
        device = "/%s:0" % ("gpu" if use_gpu else "cpu")
        with ops.Graph().as_default():
          with ops.device(device):
            x_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32)
            y_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32)
            c_gen = random_ops.random_uniform([m], dtype=dtypes.float32) <= 0.5
            x = resource_variable_ops.ResourceVariable(x_gen)
            y = resource_variable_ops.ResourceVariable(y_gen)
            c = resource_variable_ops.ResourceVariable(c_gen)
            op = array_ops.where(c, x, y)
          with session.Session(config=benchmark.benchmark_config()) as sess:
            self.evaluate(x.initializer)
            self.evaluate(y.initializer)
            self.evaluate(c.initializer)
            r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
            # approximate size of output: m*n*2 floats for each axis.
            gb_processed = m * n * 8 / 1.0e9
            throughput = gb_processed / r["wall_time"]
            print("Benchmark: %s \t wall_time: %0.03g s \t "
                  "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
            sys.stdout.flush()
  
  
  if __name__ == "__main__":
    test.main()
  