# 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 array_ops."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport reimport timeimport unittestfrom absl.testing import parameterizedimport numpy as npfrom tensorflow.python.client import sessionfrom tensorflow.python.eager import backpropfrom tensorflow.python.eager import contextfrom tensorflow.python.eager import def_functionfrom tensorflow.python.framework import configfrom tensorflow.python.framework import constant_opfrom tensorflow.python.framework import dtypesfrom tensorflow.python.framework import errorsfrom tensorflow.python.framework import errors_implfrom tensorflow.python.framework import opsfrom tensorflow.python.framework import sparse_tensorfrom tensorflow.python.framework import tensor_shapefrom tensorflow.python.framework import tensor_specfrom tensorflow.python.framework import test_opsfrom tensorflow.python.framework import test_utilfrom tensorflow.python.ops import array_opsfrom tensorflow.python.ops import gen_array_opsfrom tensorflow.python.ops import gradient_checker_v2from tensorflow.python.ops import init_opsfrom tensorflow.python.ops import list_opsfrom tensorflow.python.ops import map_fnfrom tensorflow.python.ops import math_opsfrom tensorflow.python.ops import resource_variable_opsfrom tensorflow.python.ops import state_opsfrom tensorflow.python.ops import variable_scopefrom tensorflow.python.ops import variablesfrom tensorflow.python.platform import test as test_lib@test_util.run_all_in_graph_and_eager_modesclass BatchMatrixTransposeTest(test_util.TensorFlowTestCase):  def testNonBatchMatrix(self):    matrix = [[1, 2, 3], [4, 5, 6]]  # Shape (2, 3)    expected_transposed = [[1, 4], [2, 5], [3, 6]]  # Shape (3, 2)    transposed = array_ops.matrix_transpose(matrix)    self.assertEqual((3, 2), transposed.get_shape())    self.assertAllEqual(expected_transposed, transposed)  def testConjugate(self):    m = [[1 + 1j, 2 + 2j, 3 + 3j], [4 + 4j, 5 + 5j, 6 + 6j]]    expected_transposed = [[1 - 1j, 4 - 4j], [2 - 2j, 5 - 5j], [3 - 3j, 6 - 6j]]    matrix = ops.convert_to_tensor(m)    transposed = array_ops.matrix_transpose(matrix, conjugate=True)    self.assertEqual((3, 2), transposed.get_shape())    self.assertAllEqual(expected_transposed, transposed)  def testBatchMatrix(self):    matrix_0 = [[1, 2, 3], [4, 5, 6]]    matrix_0_t = [[1, 4], [2, 5], [3, 6]]    matrix_1 = [[11, 22, 33], [44, 55, 66]]    matrix_1_t = [[11, 44], [22, 55], [33, 66]]    batch_matrix = [matrix_0, matrix_1]  # Shape (2, 2, 3)    expected_transposed = [matrix_0_t, matrix_1_t]  # Shape (2, 3, 2)    transposed = array_ops.matrix_transpose(batch_matrix)    self.assertEqual((2, 3, 2), transposed.get_shape())    self.assertAllEqual(expected_transposed, transposed)  def testNonBatchMatrixDynamicallyDefined(self):    # needs explicit `constant` because lists are not automatically    # converted to sensors when applying `transpose` below    matrix = constant_op.constant([[1, 2, 3], [4, 5, 6]])  # Shape (2, 3)    expected_transposed = [[1, 4], [2, 5], [3, 6]]  # Shape (3, 2)    @def_function.function(input_signature=[        tensor_spec.TensorSpec(shape=None, dtype=dtypes.int32)    ])    def transpose(matrix):      self.assertIs(matrix.shape.ndims, None)      return array_ops.matrix_transpose(matrix)    self.assertAllEqual(expected_transposed, transpose(matrix))  def testBatchMatrixDynamicallyDefined(self):    matrix_0 = [[1, 2, 3], [4, 5, 6]]    matrix_0_t = [[1, 4], [2, 5], [3, 6]]    matrix_1 = [[11, 22, 33], [44, 55, 66]]    matrix_1_t = [[11, 44], [22, 55], [33, 66]]    # needs explicit `constant` because lists are not automatically    # converted to sensors when applying `transpose` below    batch_matrix = constant_op.constant([matrix_0, matrix_1])  # Shape (2, 2, 3)    expected_transposed = [matrix_0_t, matrix_1_t]  # Shape (2, 3, 2)    @def_function.function(input_signature=[        tensor_spec.TensorSpec(shape=None, dtype=dtypes.int32)    ])    def transpose(matrix):      self.assertIs(matrix.shape.ndims, None)      return array_ops.matrix_transpose(matrix)    self.assertAllEqual(expected_transposed, transpose(batch_matrix))  def testTensorWithStaticRankLessThanTwoRaisesBecauseNotAMatrix(self):    vector = [1, 2, 3]    with self.assertRaisesRegex(ValueError, "should be a "):      array_ops.matrix_transpose(vector)  def testNarrowMatrixConjugateTranspose(self):    for dtype in (dtypes.float32, dtypes.float64):      for conjugate in (True, False):        with self.subTest(complex_type=dtype, conjugate=conjugate):          vector = math_ops.complex(              constant_op.constant(0, dtype=dtype),              math_ops.range(96, dtype=dtype))          column_vector = array_ops.expand_dims(vector, axis=-1)          row_vector = array_ops.expand_dims(vector, axis=0)          narrow_matrix = array_ops.tile(column_vector, [1, 2])  # [96, 2]          expected_transposed = array_ops.tile(row_vector, [2, 1])  # [2, 96]          if conjugate:            expected_transposed = -expected_transposed          transposed = array_ops.matrix_transpose(              narrow_matrix, conjugate=conjugate)          self.assertEqual((2, 96), transposed.get_shape())          self.assertAllEqual(expected_transposed, transposed)class BooleanMaskTest(test_util.TensorFlowTestCase):  def setUp(self):    self.rng = np.random.RandomState(42)  def CheckVersusNumpy(self, ndims_mask, arr_shape, make_mask=None, axis=None):    """Check equivalence between boolean_mask and numpy masking."""    if make_mask is None:      make_mask = lambda shape: self.rng.randint(0, 2, size=shape).astype(bool)    arr = np.random.rand(*arr_shape)    mask = make_mask(arr_shape[:ndims_mask])    if axis is not None:      mask = make_mask(arr_shape[axis:ndims_mask + axis])    if axis is None or axis == 0:      masked_arr = arr[mask]    elif axis == 1:      masked_arr = arr[:, mask]    elif axis == 2:      masked_arr = arr[:, :, mask]    masked_tensor = array_ops.boolean_mask(arr, mask, axis=axis)    # Leading dimension size of masked_tensor is always unknown until runtime    # since we don't how many elements will be kept.    leading = 1 if axis is None else axis + 1    self.assertAllEqual(masked_tensor.get_shape()[leading:],                        masked_arr.shape[leading:])    self.assertAllClose(masked_arr, masked_tensor)  def testMaskDim1ArrDim2Axis1(self):    ndims_mask = 1    for arr_shape in [(1, 1), (2, 2), (2, 5)]:      with self.subTest(arr_shape=arr_shape):        self.CheckVersusNumpy(ndims_mask, arr_shape, axis=1)  def testMaskDim2ArrDim2Axis1(self):    ndims_mask = 2    for arr_shape in [(1, 1), (2, 2), (2, 5)]:      with self.subTest(arr_shape=arr_shape):        self.CheckVersusNumpy(ndims_mask, arr_shape, axis=1)  def testMaskDim1ArrDim1(self):    ndims_mask = 1    for arr_shape in [(1,), (2,), (3,), (10,)]:      with self.subTest(arr_shape=arr_shape):        self.CheckVersusNumpy(ndims_mask, arr_shape)  def testMaskDim1ArrDim2(self):    ndims_mask = 1    for arr_shape in [(1, 1), (2, 2), (2, 5)]:      with self.subTest(arr_shape=arr_shape):        self.CheckVersusNumpy(ndims_mask, arr_shape)  def testMaskDim2ArrDim2(self):    ndims_mask = 2    for arr_shape in [(1, 1), (2, 2), (2, 5)]:      with self.subTest(arr_shape=arr_shape):        self.CheckVersusNumpy(ndims_mask, arr_shape)  def testMaskDim2ArrDim3(self):    ndims_mask = 2    for arr_shape in [(1, 1, 1), (1, 2, 2), (2, 2, 1)]:      with self.subTest(arr_shape=arr_shape):        self.CheckVersusNumpy(ndims_mask, arr_shape)  def testEmptyInput2D(self):    mask = np.array([True, False])    arr = np.array([[], []]).astype(np.float32)    numpy_result = arr[mask]    tf_result = array_ops.boolean_mask(arr, mask)    self.assertAllEqual(numpy_result.shape[1:], tf_result.get_shape()[1:])    with self.cached_session():      self.assertAllClose(numpy_result, tf_result)  def testEmptyInput1D(self):    mask = np.array([]).astype(bool)    arr = np.array([]).astype(np.float32)    numpy_result = arr[mask]    tf_result = array_ops.boolean_mask(arr, mask)    self.assertAllEqual(numpy_result.shape[1:], tf_result.get_shape()[1:])    with self.cached_session():      self.assertAllClose(numpy_result, tf_result)  def testEmptyOutput(self):    make_mask = lambda shape: np.zeros(shape, dtype=bool)    for ndims_mask in range(1, 4):      for ndims_arr in range(ndims_mask, ndims_mask + 3):        for _ in range(3):          with self.subTest(ndims_mask=ndims_mask, ndims_arr=ndims_arr, _=_):            arr_shape = np.random.randint(1, 5, size=ndims_arr)            self.CheckVersusNumpy(ndims_mask, arr_shape, make_mask=make_mask)  def testWorksWithDimensionsEqualToNoneDuringGraphBuild(self):    # The rank of the mask tensor must be specified. This is explained    # in the docstring as well.    @def_function.function    def func(ph_tensor, ph_mask):      return array_ops.boolean_mask(ph_tensor, ph_mask)    f = func.get_concrete_function(        tensor_spec.TensorSpec(None, dtypes.int32),        tensor_spec.TensorSpec([None], dtypes.bool))    arr = np.array([[1, 2], [3, 4]], np.int32)    mask = np.array([False, True])    masked_tensor = f(arr, mask)    self.assertAllEqual(masked_tensor, arr[mask])  def testMaskDimensionsSetToNoneRaises(self):    # The rank of the mask tensor must be specified. This is explained    # in the docstring as well.    @def_function.function    def func(tensor, mask):      return array_ops.boolean_mask(tensor, mask)    with self.assertRaisesRegex(ValueError, "dimensions must be specified"):      _ = func.get_concrete_function(          tensor_spec.TensorSpec([None, 2], dtypes.int32),          tensor_spec.TensorSpec(None, dtypes.bool))  def testMaskHasMoreDimsThanTensorRaises(self):    mask = [[True, True], [False, False]]    tensor = [1, 2, 3, 4]    with self.cached_session():      with self.assertRaisesRegex(ValueError, "incompatible"):        self.evaluate(array_ops.boolean_mask(tensor, mask))  def testMaskIsScalarRaises(self):    mask = True    tensor = 1    with self.cached_session():      with self.assertRaisesRegex(ValueError, "mask.*scalar"):        self.evaluate(array_ops.boolean_mask(tensor, mask))  def testMaskShapeDifferentThanFirstPartOfTensorShapeRaises(self):    mask = [True, True, True]    tensor = [[1, 2], [3, 4]]    with self.cached_session():      with self.assertRaisesRegex(ValueError, "incompatible"):        self.evaluate(array_ops.boolean_mask(tensor, mask))  def testStringMask(self):    # Reproduces b/111171330, where the optimized boolean_mask graph would    # be incorrectly placed on GPU.    config.set_optimizer_experimental_options({"shape_optimization": True})    @def_function.function    def func(tile_input):      string_tensor = array_ops.tile([["hello"]], tile_input)      bool_tensor = array_ops.tile([[True]], tile_input)      masked_tensor = array_ops.boolean_mask(string_tensor, bool_tensor)      return masked_tensor    result = func([2, 2])    self.assertAllEqual([b"hello", b"hello", b"hello", b"hello"], result)  def testMaskWithAxisTensor(self):    @def_function.function(autograph=False)    def f():      return array_ops.boolean_mask([1, 2, 3], [True, False, True],                                    axis=constant_op.constant(                                        0, dtype=dtypes.int32))    self.assertAllEqual(self.evaluate(f()), [1, 3])  def testMaskWithAxisNonConstTensor(self):    @def_function.function(        autograph=False,        input_signature=[            tensor_spec.TensorSpec(shape=None, dtype=dtypes.int32)        ])    def f(axis):      return array_ops.boolean_mask([1, 2, 3], [True, False, True], axis=axis)    self.assertAllEqual(        self.evaluate(f(constant_op.constant(0, dtype=dtypes.int32))), [1, 3])@test_util.run_all_in_graph_and_eager_modesclass OperatorShapeTest(test_util.TensorFlowTestCase):  def testExpandScalar(self):    scalar = "hello"    scalar_expanded = array_ops.expand_dims(scalar, [0])    self.assertEqual(scalar_expanded.get_shape(), (1,))  def testSqueezeScalar(self):    scalar = "hello"    scalar_squeezed = array_ops.squeeze(scalar, ())    self.assertEqual(scalar_squeezed.get_shape(), ())  def testSqueezeMatrix(self):    matrix = [[1, 2, 3]]    matrix_squeezed = array_ops.squeeze(matrix, [0])    self.assertEqual(matrix_squeezed.get_shape(), (3))    with self.assertRaisesRegex(        Exception, "Can not squeeze dim.1., expected a dimension of 1, got 3"):      matrix_squeezed = array_ops.squeeze(matrix, [1])  def testSqueezeScalarDim(self):    matrix = [[1, 2, 3]]    matrix_squeezed = array_ops.squeeze(matrix, 0)    self.assertEqual(matrix_squeezed.get_shape(), (3))  def testExpandDimsWithNonScalarDim(self):    with self.assertRaisesRegex(Exception,                                "must be a tensor with a single value"):      array_ops.expand_dims(1, axis=[0, 1])class ReverseV2Test(test_util.TensorFlowTestCase):  def testReverse0DimAuto(self):    x_np = 4    for use_gpu in [False, True]:      with self.subTest(use_gpu=use_gpu):        with self.cached_session(use_gpu=use_gpu):          x_tf = self.evaluate(array_ops.reverse_v2(x_np, []))          self.assertAllEqual(x_tf, x_np)  def _reverse1DimAuto(self, np_dtype):    x_np = np.array([1, 200, 3, 40, 5], dtype=np_dtype)    for use_gpu in [False, True]:      for axis_dtype in [dtypes.int32, dtypes.int64]:        with self.subTest(use_gpu=use_gpu, axis_dtype=axis_dtype):          x_tf = self.evaluate(              array_ops.reverse_v2(x_np,                                   constant_op.constant([0], dtype=axis_dtype)))          self.assertAllEqual(x_tf, np.asarray(x_np)[::-1])  def _reverse2DimAuto(self, np_dtype):    x_np = np.array([[1, 200, 3], [4, 5, 60]], dtype=np_dtype)    for reverse_f in [array_ops.reverse_v2, array_ops.reverse]:      for use_gpu in [False, True]:        for axis_dtype in [dtypes.int32, dtypes.int64]:          with self.subTest(              reverse_f=reverse_f, use_gpu=use_gpu, axis_dtype=axis_dtype):            x_tf_1 = self.evaluate(                reverse_f(x_np, constant_op.constant([0], dtype=axis_dtype)))            x_tf_2 = self.evaluate(                reverse_f(x_np, constant_op.constant([-2], dtype=axis_dtype)))            x_tf_3 = self.evaluate(                reverse_f(x_np, constant_op.constant([1], dtype=axis_dtype)))            x_tf_4 = self.evaluate(                reverse_f(x_np, constant_op.constant([-1], dtype=axis_dtype)))            x_tf_5 = self.evaluate(                reverse_f(x_np, constant_op.constant([1, 0], dtype=axis_dtype)))            self.assertAllEqual(x_tf_1, np.asarray(x_np)[::-1, :])            self.assertAllEqual(x_tf_2, np.asarray(x_np)[::-1, :])            self.assertAllEqual(x_tf_3, np.asarray(x_np)[:, ::-1])            self.assertAllEqual(x_tf_4, np.asarray(x_np)[:, ::-1])            self.assertAllEqual(x_tf_5, np.asarray(x_np)[::-1, ::-1])  # This test covers the axis validation in the shape function  # (no eval())  def testInvalidAxis(self):    x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)    with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),                                "is out of valid range"):      array_ops.reverse_v2(x_np, [-30])    with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),                                "is out of valid range"):      array_ops.reverse_v2(x_np, [2])    with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),                                "axis 0 specified more than once"):      array_ops.reverse_v2(x_np, [0, -2])  # This is the version of reverse that uses axis indices rather than  # bool tensors  # TODO(b/32254538): Change this test to use array_ops.reverse  #  # Note: this test passes placeholder as constant axis is validated  # in shape function (see testInvalidAxis)  def testInvalid(self):    x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)    @def_function.function    def func(ax):      return array_ops.reverse_v2(x_np, ax)    with self.assertRaisesRegex((ValueError, errors_impl.InvalidArgumentError),                                "is out of.*range"):      func([-30])    with self.assertRaisesRegex((ValueError, errors_impl.InvalidArgumentError),                                "is out of.*range"):      func([2])    with self.assertRaisesRegex(        (ValueError, errors_impl.InvalidArgumentError),        "(axis 0 specified more than once|canonicalized axis 0 was repeated.)"):      func([0, -2])  def testReverse1DimAuto(self):    for dtype in [        np.uint8, np.int8, np.uint16, np.int16, np.uint32, np.int32,        np.uint64, np.int64, np.bool,        np.float16, np.float32, np.float64, np.complex64, np.complex128,        np.array(b"").dtype.type    ]:      self._reverse1DimAuto(dtype)  def testReverse2DimAuto(self):    for dtype in [        np.uint8, np.int8, np.uint16, np.int16, np.uint32, np.int32,        np.uint64, np.int64, np.bool,        np.float16, np.float32, np.float64, np.complex64, np.complex128,        np.array(b"").dtype.type    ]:      self._reverse2DimAuto(dtype)  def testReverseRowsOf3Channels(self):    """Tests optimized code for reversing rows with last dim size = 3."""    for reverse_f in [array_ops.reverse_v2, array_ops.reverse]:      for outer_size in (1, 2):        for middle_size in list(range(50)) + [100000]:          with self.subTest(              reverse_f=reverse_f,              outer_size=outer_size,              middle_size=middle_size,              use_gpu=True):            x_np = np.reshape(                np.arange(outer_size * middle_size * 3, dtype=np.float32),                newshape=(outer_size, middle_size, 3))            x_tf = self.evaluate(reverse_f(x_np, [1]))            np_answer = x_np[:, ::-1, :]            self.assertAllEqual(x_tf, np_answer)  def testReverseRowsOf4Channels(self):    for reverse_f in [array_ops.reverse_v2, array_ops.reverse]:      for outer_size in (1, 2):        for middle_size in list(range(50)) + [100000]:          with self.subTest(              reverse_f=reverse_f,              outer_size=outer_size,              middle_size=middle_size,              use_gpu=True):            x_np = np.reshape(                np.arange(outer_size * middle_size * 4, dtype=np.float32),                newshape=(outer_size, middle_size, 4))            x_tf = self.evaluate(reverse_f(x_np, [1]))            np_answer = x_np[:, ::-1, :]            self.assertAllEqual(x_tf, np_answer)  def testReverseColumnsOf3Channels(self):    for reverse_f in [array_ops.reverse_v2, array_ops.reverse]:      for outer_size in list(range(50)) + [100000]:        for middle_size in (1, 2):          with self.subTest(              reverse_f=reverse_f,              outer_size=outer_size,              middle_size=middle_size,              use_gpu=True):            x_np = np.reshape(                np.arange(outer_size * middle_size * 3, dtype=np.float32),                newshape=(outer_size, middle_size, 3))            x_tf = self.evaluate(reverse_f(x_np, [0]))            np_answer = x_np[::-1, :, :]            self.assertAllEqual(x_tf, np_answer)  def testReverseInvalidShape(self):    x = np.ndarray(shape=[0, 1, 1])    v = array_ops.reverse_v2(x, axis=[1])    self.assertAllEqual(self.evaluate(v), v)class MeshgridTest(test_util.TensorFlowTestCase):  def _compareDiff(self, x, y, use_gpu):    for index in ("ij", "xy"):      numpy_out = np.meshgrid(x, y, indexing=index)      tf_out = array_ops.meshgrid(x, y, indexing=index)      with self.cached_session(use_gpu=use_gpu):        for xx, yy in zip(numpy_out, tf_out):          self.assertAllEqual(xx, yy)  def _compareDiffType(self, n, np_dtype, use_gpu):    inputs = []    for index in ("ij", "xy"):      for _ in range(n):        x = np.linspace(-10, 10, 5).astype(np_dtype)        if np_dtype in (np.complex64, np.complex128):          x += 1j        inputs.append(x)      numpy_out = np.meshgrid(*inputs, indexing=index)      with test_util.device(use_gpu=use_gpu):        tf_out = array_ops.meshgrid(*inputs, indexing=index)        for x_np, x_tf in zip(numpy_out, tf_out):          self.assertAllEqual(x_np, x_tf)  def testCompare(self):    for t in (np.float16, np.float32, np.float64, np.int32, np.int64,              np.complex64, np.complex128):      with self.subTest(t=t):        self._compareDiffType(2, t, False)        self._compareDiffType(3, t, False)        x = [1, 2, 3]        y = [4, 5]        a = [[1, 1], [1, 1]]        self._compareDiff(x, y, False)        self._compareDiff(x, a, False)class StridedSliceChecker(object):  """Check a given tensor against the numpy result."""  REF_TENSOR = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)  REF_TENSOR_ALIGNED = np.arange(1, 97, dtype=np.float32).reshape(3, 4, 8)  def __init__(self, test, x, tensor_type=dtypes.int32, check_type_infer=True):    self.x_np = np.array(x).astype(tensor_type.as_numpy_dtype)    if tensor_type.is_bool:      self.x_np = np.array(x % 3).astype(np.bool)    # Give the value a non-zero imaginary component for complex types.    if tensor_type.is_complex:      self.x_np -= 1j * self.x_np    self.test = test    self.x = constant_op.constant(self.x_np, dtype=tensor_type)    self.check_type_infer = check_type_infer  def __getitem__(self, spec):    op = self.x.__getitem__(spec)    def eval_if_tensor(x):      try:        return self.test.evaluate(x)      except (AttributeError, TypeError, ValueError):        return x    if isinstance(spec, bool) or \      (isinstance(spec, ops.Tensor) and spec.dtype == dtypes.bool) or \      (isinstance(spec, np.ndarray) and spec.dtype == bool) or \      (isinstance(spec, (list, tuple)) and np.asarray(spec).dtype == bool):      tensor = self.test.evaluate(op)      np_spec = eval_if_tensor(spec)      self.test.assertAllEqual(self.x_np[np_spec], tensor)      return tensor    if not isinstance(spec, (list, tuple)):      spec = [spec]    tensor = self.test.evaluate(op)    # Make a numpy spec that pre-evals the tensors    np_specs = []    for s in spec:      if isinstance(s, slice):        start = eval_if_tensor(s.start)        stop = eval_if_tensor(s.stop)        step = eval_if_tensor(s.step)        np_specs.append(slice(start, stop, step))      else:        np_specs.append(eval_if_tensor(s))    self.test.assertAllEqual(self.x_np[tuple(np_specs)], tensor)    if self.check_type_infer:      self.test.assertAllEqual(tensor.shape, op.get_shape())    return tensorSTRIDED_SLICE_TYPES = [    dtypes.int32, dtypes.int64, dtypes.int16, dtypes.int8, dtypes.uint8,    dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128,    dtypes.bool]class StridedSliceTest(test_util.TensorFlowTestCase):  """Test the strided slice operation with variants of slices."""  def test_basic_slice(self):    for tensor_type in STRIDED_SLICE_TYPES:      with self.subTest(tensor_type=tensor_type, use_gpu=True):        checker = StridedSliceChecker(            self, StridedSliceChecker.REF_TENSOR, tensor_type=tensor_type)        _ = checker[:, :, :]        # Various ways of representing identity slice        _ = checker[:, :, :]        _ = checker[::, ::, ::]        _ = checker[::1, ::1, ::1]        # Not zero slice        _ = checker[::1, ::5, ::2]        # Reverse in each dimension independently        _ = checker[::-1, :, :]        _ = checker[:, ::-1, :]        _ = checker[:, :, ::-1]        ## negative index tests i.e. n-2 in first component        _ = checker[-2::-1, :, ::1]        # negative index tests i.e. n-2 in first component, non-unit stride        _ = checker[-2::-1, :, ::2]        # Check rank-0 examples        checker2 = StridedSliceChecker(self, 5, tensor_type=tensor_type)        _ = checker2[None]        _ = checker2[...]        _ = checker2[tuple()]  def testInt64GPU(self):    if not test_util.is_gpu_available():      self.skipTest("No GPU available")    with test_util.force_gpu():      x = constant_op.constant([1., 2., 3.])      begin = constant_op.constant([2], dtype=dtypes.int64)      end = constant_op.constant([3], dtype=dtypes.int64)      strides = constant_op.constant([1], dtype=dtypes.int64)      s = array_ops.strided_slice(x, begin, end, strides)      self.assertAllEqual([3.], self.evaluate(s))  @test_util.assert_no_new_pyobjects_executing_eagerly  @test_util.assert_no_garbage_created  def testTensorSliceEagerMemory(self):    with context.eager_mode():      inputs = constant_op.constant([[[1], [2], [3], [4]]],                                    dtype=dtypes.float32)      # Tests that slicing an EagerTensor doesn't leak memory      inputs[0]  # pylint: disable=pointless-statement  @test_util.assert_no_new_pyobjects_executing_eagerly  @test_util.assert_no_garbage_created  def testVariableSliceEagerMemory(self):    with context.eager_mode():      v = variables.Variable([1., 2.])      v[0]  # pylint: disable=pointless-statement  def testDegenerateSlices(self):    with test_util.device(use_gpu=True):      checker = StridedSliceChecker(self, StridedSliceChecker.REF_TENSOR)      # degenerate by offering a forward interval with a negative stride      _ = checker[0:-1:-1, :, :]      # degenerate with a reverse interval with a positive stride      _ = checker[-1:0, :, :]      # empty interval in every dimension      _ = checker[-1:0, 2:2, 2:3:-1]      # empty first dimension only (used to break for aligned tensors).      checker = StridedSliceChecker(self,                                    StridedSliceChecker.REF_TENSOR_ALIGNED)      _ = checker[1:0]  def testSliceWithUndefinedDimension(self):    t = constant_op.constant([1, 2, 3])    d = tensor_shape.Dimension(None)    self.assertAllEqual(t[d:d:d], t)  def testEllipsis(self):    with test_util.device(use_gpu=True):      raw = [[[[[1, 2], [3, 4], [5, 6]]], [[[7, 8], [9, 10], [11, 12]]]]]      checker = StridedSliceChecker(self, raw)      _ = checker[0:]      # implicit ellipsis      _ = checker[0:, ...]      # ellipsis alone      _ = checker[...]      # ellipsis at end      _ = checker[0:1, ...]      # ellipsis at begin      _ = checker[..., 0:1]      # ellipsis at middle      _ = checker[0:1, ..., 0:1]      # multiple ellipses not allowed      with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),                                  "Multiple ellipses"):        _ = checker[..., :, ...].eval()  def testShrink(self):    with test_util.device(use_gpu=True):      raw = [[[[[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]]],              [[[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]]]      checker = StridedSliceChecker(self, raw)      _ = checker[:, :, :, :, 3]      _ = checker[..., 3]      _ = checker[:, 0]      _ = checker[:, :, 0]  def testBothNewAxisAndShrink(self):    with test_util.device(use_gpu=True):      @def_function.function      def func(inp):        return inp[array_ops.newaxis, :, 0]      f = func.get_concrete_function(          tensor_spec.TensorSpec([2, 2], dtypes.int16))      # TODO(b/190416665): Allow the constant to be eagerly copied/created on      # the GPU.      with ops.device("CPU"):        ones = constant_op.constant([[1, 1], [1, 1]], dtypes.int16)      self.assertAllEqual([[1, 1]], self.evaluate(f(ones)))  def testTensorIndexing(self):    with test_util.device(use_gpu=True):      raw = [[[[[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]]],              [[[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]]]      checker = StridedSliceChecker(self, raw, check_type_infer=False)      bar = constant_op.constant(2)      bar2 = constant_op.constant(3)      _ = checker[..., bar:bar2]      _ = checker[..., bar]      _ = checker[..., 3]      _ = checker[..., 2**64 // 2**63]  # Test longs in Python 2  def testTensorIndexingTypeError(self):    with self.session():      checker = StridedSliceChecker(self, StridedSliceChecker.REF_TENSOR)      expected = re.escape(array_ops._SLICE_TYPE_ERROR)      with self.assertRaisesRegex(TypeError, expected):        _ = checker["foo"]      with self.assertRaisesRegex(TypeError, expected):        _ = checker[constant_op.constant("foo")]      with self.assertRaisesRegex(TypeError, expected):        _ = checker[0.0]      with self.assertRaisesRegex(TypeError, expected):        _ = checker[constant_op.constant(0.0)]      with self.assertRaisesRegex(TypeError, expected):        _ = checker[constant_op.constant([1, 2, 3])]      with self.assertRaisesRegex(TypeError, expected):        _ = checker[[2.1, -0.7, 1.5]]  def testExpand(self):    with test_util.device(use_gpu=True):      raw = [[[[[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]]],              [[[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]]]      checker = StridedSliceChecker(self, raw)      # new axis (followed by implicit ellipsis)      _ = checker[np.newaxis]      # newaxis after ellipsis      _ = checker[..., np.newaxis]      # newaxis in between ellipsis and explicit range      _ = checker[..., np.newaxis, :]      _ = checker[:, ..., np.newaxis, :, :]      # Reverse final dimension with new axis      _ = checker[:, :, np.newaxis, :, 2::-1]      # Ellipsis in middle of two newaxis      _ = checker[np.newaxis, ..., np.newaxis]  def testExpandVariable(self):    with test_util.device(use_gpu=True):      x = variables.Variable(7, dtype=dtypes.int32)      self.evaluate(x.initializer)      y = self.evaluate(x[None])      self.assertEqual(y.shape, (1,))      self.assertAllEqual(y, (7,))  def testOptimizedCases(self):    with test_util.device(use_gpu=True):      checker = StridedSliceChecker(self,                                    StridedSliceChecker.REF_TENSOR_ALIGNED)      # Identity      _ = checker[:]      # Identity      _ = checker[...]      # Identity      _ = checker[np.newaxis, ..., np.newaxis]      # First axis slice      _ = checker[1:]      # First axis slice      _ = checker[np.newaxis, 1:]  def testMasks(self):    with test_util.device(use_gpu=True):      scalar = np.array(0)      # Test tensor type mask      checker = StridedSliceChecker(self, StridedSliceChecker.REF_TENSOR)      _ = checker[checker.x > 2]      _ = checker[checker.x <= 5]      _ = checker[ops.convert_to_tensor(scalar)]      # Test numpy array type mask      raw = np.array([[[[[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]]],                       [[[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23,                                                              24]]]]])      checker1 = StridedSliceChecker(self, raw)      _ = checker1[raw >= 4]      _ = checker1[raw < 19]      _ = checker1[scalar]      # Test boolean and non boolean cases      mask = np.array([True, False, True])      raw1 = np.array([[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]])      checker2 = StridedSliceChecker(self, raw1)      _ = checker2[mask]      _ = checker2[ops.convert_to_tensor(mask)]class StridedSliceShapeTest(test_util.TensorFlowTestCase):  """Test the shape inference of StridedSliceShapes."""  def testUnknown(self):    with test_util.device(use_gpu=True):      @def_function.function      def f(x):        y = x[...]        self.assertAllEqual(y.get_shape().ndims, None)      _ = f.get_concrete_function(tensor_spec.TensorSpec(None, dtypes.float32))  def tensorShapeEqual(self, x, y):    self.assertTrue(x is not None and y is not None or x is None and y is None)    self.assertEqual(x.as_list(), y.as_list())  def testTensorShapeUncertain(self):    with test_util.device(use_gpu=True):      @def_function.function      def f1(x):        y = x[3:5]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([2, None, 7]))      _ = f1.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f2(x):        y = x[3:5, :, 4]        self.tensorShapeEqual(y.get_shape(), tensor_shape.TensorShape([2,                                                                       None]))      _ = f2.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f3(x):        y = x[3:5, 3:4, 4]        self.tensorShapeEqual(y.get_shape(), tensor_shape.TensorShape([2,                                                                       None]))      _ = f3.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f4(x):        y = x[3:5, :, 5:10]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([2, None, 2]))      _ = f4.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f5(x):        y = x[3:5, :, 50:3]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([2, None, 0]))      _ = f5.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f6(x):        y = x[3:5, :, array_ops.newaxis, 50:3,]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([2, None, 1, 0]))      _ = f6.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f7(x):        y = x[1:5:2, :, array_ops.newaxis, 50:3,]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([2, None, 1, 0]))      _ = f7.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f8(x):        y = x[:5:3, :, array_ops.newaxis, 50:3,]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([2, None, 1, 0]))      _ = f8.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f9(x):        y = x[:2:3, :, array_ops.newaxis, 50:3,]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([1, None, 1, 0]))      _ = f9.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))      @def_function.function      def f10(x):        y = x[::-1, :, array_ops.newaxis, ::-2]        self.tensorShapeEqual(y.get_shape(),                              tensor_shape.TensorShape([5, None, 1, 4]))      _ = f10.get_concrete_function(          tensor_spec.TensorSpec((5, None, 7), dtypes.float32))  def testTensorValuedIndexShape(self):    with self.session():      @def_function.function      def f1(x, y):        z = x[y]        self.tensorShapeEqual(z.get_shape(), tensor_shape.TensorShape([3, 7]))      _ = f1.get_concrete_function(          tensor_spec.TensorSpec((5, 3, 7)),          tensor_spec.TensorSpec((), dtypes.int32))      @def_function.function      def f2(x, y):        z = x[y, ::-1]        self.tensorShapeEqual(z.get_shape(), tensor_shape.TensorShape([3, 7]))      _ = f2.get_concrete_function(          tensor_spec.TensorSpec((5, 3, 7)),          tensor_spec.TensorSpec((), dtypes.int32))      @def_function.function      def f3(x, y):        z = x[y, ::-2]        self.tensorShapeEqual(z.get_shape(), tensor_shape.TensorShape([2, 7]))      _ = f3.get_concrete_function(          tensor_spec.TensorSpec((5, 3, 7)),          tensor_spec.TensorSpec((), dtypes.int32))      @def_function.function      def f4(x, y, s):        z = x[y, s:2]        self.tensorShapeEqual(z.get_shape(), tensor_shape.TensorShape([None,                                                                       7]))      _ = f4.get_concrete_function(          tensor_spec.TensorSpec((5, 3, 7)),          tensor_spec.TensorSpec((), dtypes.int32),          tensor_spec.TensorSpec((), dtypes.int32))class GradSliceChecker(object):  """Tests that we can compute a gradient for var^2."""  def __init__(self, test, var, varnp, use_tape):    self.test = test    self.var = var    self.varnp = varnp    self.use_tape = use_tape  def __getitem__(self, spec):    with test_util.AbstractGradientTape(        use_tape=self.use_tape, persistent=True) as tape:      tape.watch(self.var)      val = self.var * self.var      slice_var = self.var[spec]      slice_val = val[spec]      # compute analytic 2nd derivative      analytic_grad2 = 2 * slice_val      dy = variables.Variable(          array_ops.ones_like(slice_var, dtype=dtypes.float32))      assign = dy.assign(slice_var)      slice_val_grad = tape.gradient(slice_val, self.var, [dy])      slice_val_grad2 = tape.gradient(slice_val_grad, dy, [self.var])    self.test.evaluate(assign)    slice_val_grad_evaled, slice_val_grad2_evaled = (        self.test.evaluate([slice_val_grad, slice_val_grad2]))    analytic_grad2_evaled = self.test.evaluate(analytic_grad2)    self.test.assertAllEqual(slice_val_grad2_evaled, analytic_grad2_evaled)    # compute analytic gradient for slice    np_val_grad = (2 * self.varnp * self.varnp)    np_sliceval_grad = np.zeros(self.var.get_shape())    if isinstance(spec, ops.Tensor):      spec = self.test.evaluate([spec])    np_sliceval_grad[spec] = np_val_grad[spec]    # verify gradient    self.test.assertAllEqual(slice_val_grad_evaled, np_sliceval_grad)class StridedSliceGradTest(test_util.TensorFlowTestCase,                           parameterized.TestCase):  """Test that strided slice's custom gradient produces correct gradients."""  @parameterized.parameters(set((True, context.executing_eagerly())))  def testGradient(self, use_tape):    with test_util.device(use_gpu=True):      var = variables.Variable(          array_ops.reshape(              math_ops.range(1, 97, 1, dtype=dtypes.float32), shape=(6, 4, 4)))      self.evaluate(var.initializer)      raw = np.array(range(1, 97, 1)).reshape((6, 4, 4))      grad = GradSliceChecker(self, var, raw, use_tape)      _ = grad[2:6:2, 1:3, 1:3]      _ = grad[3:0:-2, 1:3, 1:3]      _ = grad[3:0:-2, array_ops.newaxis, 1:3, 2, array_ops.newaxis]      _ = grad[3:0:-2, 1:3, 2]      _ = grad[:, -1, :]      _ = grad[:, -2, :]      with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),                                  "out of bounds"):        _ = grad[:, -200, :]      with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),                                  "out of bounds"):        _ = grad[:, 200, :]      # Test numpy array type mask      _ = grad[raw > 51]      # Test tensor type mask      _ = grad[ops.convert_to_tensor(raw) <= 76]  @parameterized.parameters(set((True, context.executing_eagerly())))  def testGradientZero(self, use_tape):    with test_util.device(use_gpu=True):      var = variables.Variable(8.)      self.evaluate(var.initializer)      grad = GradSliceChecker(self, var, np.array(8), use_tape)      _ = grad[tuple()]  @parameterized.parameters(set((True, context.executing_eagerly())))  def testInt64Indices(self, use_tape):    with test_util.AbstractGradientTape(use_tape=use_tape) as tape:      a = math_ops.range(3, dtype=dtypes.float32)      tape.watch(a)      index = constant_op.constant(1, dtype=dtypes.int64)      b = 2. * a[index]    grad = tape.gradient(b, a)    self.assertAllEqual(self.evaluate(grad), [0., 2., 0.])class StridedSliceGradTypeTest(test_util.TensorFlowTestCase):  """Test varied index types and host located memory."""  def testHostVsDevice(self):    var2 = variables.Variable(        array_ops.reshape(            math_ops.cast(math_ops.range(1, 5, 1), dtypes.float32),            shape=(4, 1, 1)))    varshape = variables.Variable([6, 4, 4], dtype=dtypes.int32)    begin = constant_op.constant([0, 0, 0])    end = constant_op.constant([4, 1, 1])    strides = constant_op.constant([1, 1, 1])    foo = array_ops.strided_slice_grad(varshape, begin, end, strides, var2)    self.evaluate(var2.initializer)    self.evaluate(varshape.initializer)    self.evaluate(foo)  def testInt64Shape(self):    original_dy = array_ops.reshape(        math_ops.cast(math_ops.range(1, 5, 1), dtypes.float32), shape=(4, 1, 1))    original_shape = constant_op.constant([6, 4, 4], dtype=dtypes.int64)    begin = constant_op.constant([0, 0, 0], dtype=dtypes.int64)    end = constant_op.constant([4, 1, 1], dtype=dtypes.int64)    strides = constant_op.constant([1, 1, 1], dtype=dtypes.int64)    dx = array_ops.strided_slice_grad(original_shape, begin, end, strides,                                      original_dy)    self.evaluate(dx)  def testMixedIndexTypes(self):    original_dy = array_ops.reshape(        math_ops.cast(math_ops.range(1, 5, 1), dtypes.float32), shape=(4, 1, 1))    original_shape = constant_op.constant([6, 4, 4], dtype=dtypes.int64)    begin = constant_op.constant([0, 0, 0], dtype=dtypes.int32)    end = constant_op.constant([4, 1, 1], dtype=dtypes.int64)    strides = constant_op.constant([1, 1, 1], dtype=dtypes.int64)    with self.assertRaises((TypeError, errors_impl.InvalidArgumentError)):      dx = array_ops.strided_slice_grad(original_shape, begin, end, strides,                                        original_dy)      self.evaluate(dx)class BenchmarkSlice(object):  def __init__(self, tensor):    self.tensor = tensor  def __getitem__(self, x):    return self.tensor[x]class StridedSliceBenchmark(test_lib.Benchmark):  """Benchmark new strided slice operation on non-trivial case."""  def run_and_time(self, slice_op):    self.evaluate(variables.global_variables_initializer())    for _ in range(10):      _ = self.evaluate(slice_op)    iters = 1000    t0 = time.time()    for _ in range(iters):      self.evaluate(slice_op)    t1 = time.time()    self.report_benchmark(iters=iters, wall_time=(t1 - t0) / 1000.0)  def make_variable(self):    n = 256    shape = (n, n, n)    items = n**3    var = variables.Variable(        array_ops.reshape(math_ops.linspace(1., float(items), items), shape),        dtype=dtypes.float32)    return var  def benchmark_strided_slice_skip(self):    with session.Session():      var = self.make_variable()      helper = BenchmarkSlice(var)      slice_op = helper[::2, ::1, ::2]      self.run_and_time(slice_op)  def benchmark_strided_slice_easy(self):    with session.Session():      var = self.make_variable()      helper = BenchmarkSlice(var)      slice_op = helper[3::1, 3::1, 3::1]      self.run_and_time(slice_op)  def benchmark_slice_easy(self):    with session.Session():      var = self.make_variable()      slice_op = var[3::1, 3::1, 3::1]      self.run_and_time(slice_op)class StridedSliceAssignChecker(object):  def __init__(self, test, x, tensor_type=dtypes.float32, use_resource=False):    self.tensor_type = tensor_type    self.test = test    self._use_resource = use_resource    self.x_np = np.array(x).astype(tensor_type.as_numpy_dtype)    # Give the value a non-zero imaginary component for complex types.    if tensor_type.is_complex:      self.x_np -= 1j * self.x_np    self.x = constant_op.constant(self.x_np, dtype=tensor_type)  def __setitem__(self, index, value):    value = np.array(value).astype(self.tensor_type.as_numpy_dtype)    # Give the value a non-zero imaginary component for complex types.    if self.tensor_type.is_complex:      value -= 1j * value    with test_util.device(use_gpu=True):      if self._use_resource:        var = resource_variable_ops.ResourceVariable(self.x)      else:        var = variables.Variable(self.x)      self.test.evaluate(var.initializer)      val = self.test.evaluate(var[index].assign(value))      # val_copy is used to check that tf.compat.v1.assign works equivalently      # to the assign method above.      val_copy = self.test.evaluate(state_ops.assign(var[index], value))      valnp = np.copy(self.x_np)      valnp[index] = np.array(value)      self.test.assertAllEqual(val, valnp)      self.test.assertAllEqual(val_copy, valnp)class SliceAssignTest(test_util.TensorFlowTestCase, parameterized.TestCase):  def testInvalidSlice(self):    foo = constant_op.constant([1, 2, 3])    with self.assertRaisesRegex(AttributeError, "no attribute 'assign'"):      bar = foo[:2].assign(constant_op.constant([1, 2]))      self.evaluate(bar)  def doTestSliceAssign(self, use_resource):    for dtype in STRIDED_SLICE_TYPES:      with self.subTest(dtype=dtype):        checker = StridedSliceAssignChecker(            self, [[1, 2, 3], [4, 5, 6]],            use_resource=use_resource,            tensor_type=dtype)        # Check if equal        checker[:] = [[10, 20, 30], [40, 50, 60]]        # Check trivial (1,1) shape tensor        checker[1:2, 1:2] = [[66]]        # shrinks shape changes        checker[1:2, 1] = [66]        checker[1, 1:2] = [66]        checker[1, 1] = 66        # newaxis shape changes        checker[:, None, :] = [[[10, 20, 30]], [[40, 50, 50]]]        # shrink and newaxis        checker[None, None, 0, 0:1] = [[[99]]]        # Non unit strides        checker[::1, ::-2] = [[3, 33], [4, 44]]        # degenerate interval        checker[8:10, 0] = []        checker[8:10, 8:10] = [[]]    # Assign vector to scalar (rank-0) using newaxis    checker2 = StridedSliceAssignChecker(self, 222)    checker2[()] = 6  # no indices    checker2[...] = 6  # ellipsis    checker2[None] = [6]  # new axis  @test_util.disable_xla("b/123559667")  def testSliceAssign(self):    self.doTestSliceAssign(use_resource=False)  @test_util.disable_xla("b/123559667")  def testSliceAssignResource(self):    self.doTestSliceAssign(use_resource=True)  def testTypeError(self):    init_val = constant_op.constant([1, 2], dtype=dtypes.int32)    too_small_val = constant_op.constant([3, 4], dtype=dtypes.int8)    too_large_val = constant_op.constant([3, 4], dtype=dtypes.int64)    v = variables.VariableV1(init_val)    with self.assertRaises((ValueError, TypeError)):      self.evaluate(v[:].assign(too_small_val))    with self.assertRaises((ValueError, TypeError)):      self.evaluate(v[:].assign(too_large_val))  def testTypeErrorResource(self):    init_val = constant_op.constant([1, 2], dtype=dtypes.int32)    too_small_val = constant_op.constant([3, 4], dtype=dtypes.int8)    too_large_val = constant_op.constant([3, 4], dtype=dtypes.int64)    v = resource_variable_ops.ResourceVariable(init_val)    self.evaluate(v.initializer)    with self.assertRaises(ValueError):      self.evaluate(v[:].assign(too_large_val))    with self.assertRaises(ValueError):      self.evaluate(v[:].assign(too_small_val))  @test_util.disable_xla("b/123559667")  @test_util.run_in_graph_and_eager_modes  def testTensorStridedSliceUpdateWithInputForward(self):    """Tests tensor_strided_slice_update with input-forwarding taking effect."""    @def_function.function    def assign(x):      y = x + 1      return gen_array_ops.tensor_strided_slice_update(y, [0], [1], [1], [0])    self.assertAllEqual([0, 1], self.evaluate(assign(array_ops.zeros([2]))))  @test_util.disable_xla("b/123559667")  @test_util.run_in_graph_and_eager_modes  def testTensorStridedSliceUpdateNoInputForward(self):    """Tests tensor_strided_slice_update with no input-forwarding."""    x = constant_op.constant([0.2, 0.3])    y = x + 1    # y's buffer won't be forwarded to z because y and z will be alive at the    # same time later.    z = gen_array_ops.tensor_strided_slice_update(y, [0], [1], [1], [0.4])    ans = y + z    self.assertAllClose([1.6, 2.6], self.evaluate(ans))  @test_util.disable_xla("b/123559667")  def testTensorStridedSliceUpdateGradSimple(self):    original = constant_op.constant([0.2, 0.3])    updates = constant_op.constant([0.4])    with backprop.GradientTape() as tape:      tape.watch([original, updates])      updated = gen_array_ops.tensor_strided_slice_update(          original, [0], [1], [1], updates)    d1, d2 = tape.gradient(updated, [original, updates],                           output_gradients=constant_op.constant([2.0, 3.0]))    self.assertAllClose([0.0, 3.0], d1)    self.assertAllClose([2.0], d2)  @parameterized.named_parameters(      ("_%s" % i, *args) for i, args in enumerate([  # pylint:disable=g-complex-comprehension          ([2, 5], [0, 1], [1, 0], [1, 2], [2], 0, 2, 0, 0, 1),          ([4], [5], [3], [1], [3], 1, 0, 0, 0, 0),          ([2, 2, 3, 2], [0, 0, 1], [1, 0, 2], [1, 0, 1], [2, 3], 0, 0, 2, 0, 5)      ]))  @test_util.disable_xla("b/123559667")  def testTensorStridedSliceUpdateGrad(      self, shape, begin, end, strides, updates_shape, *args):    with self.cached_session():      def f(a, b):        return gen_array_ops.tensor_strided_slice_update(            a, begin, end, strides, b, *args)      theoretical, numerical = gradient_checker_v2.compute_gradient(          f, [array_ops.zeros(shape), array_ops.ones(updates_shape)], delta=1.0)      self.assertAllClose(theoretical, numerical)class ShapeSizeRankTest(test_util.TensorFlowTestCase):  @test_util.run_in_graph_and_eager_modes  def testDenseShape(self):    t_value = [[0, 42], [24, 0]]    self.assertAllEqual((2, 2), self.evaluate(array_ops.shape(t_value)))    self.assertEqual(4, self.evaluate(array_ops.size(t_value)))    self.assertEqual(2, self.evaluate(array_ops.rank(t_value)))    t = constant_op.constant(t_value)    self.assertAllEqual((2, 2), self.evaluate(array_ops.shape(t)))    self.assertEqual(4, self.evaluate(array_ops.size(t)))    self.assertEqual(2, self.evaluate(array_ops.rank(t)))  @test_util.run_in_graph_and_eager_modes  def testSparseShape(self):    sp_value = sparse_tensor.SparseTensorValue(        indices=((0, 1), (1, 0)), values=(42, 24), dense_shape=(2, 2))    self.assertAllEqual((2, 2), self.evaluate(array_ops.shape(sp_value)))    self.assertEqual(4, self.evaluate(array_ops.size(sp_value)))    self.assertEqual(2, self.evaluate(array_ops.rank(sp_value)))    sp = sparse_tensor.SparseTensor.from_value(sp_value)    self.assertAllEqual((2, 2), self.evaluate(array_ops.shape(sp)))    self.assertEqual(4, self.evaluate(array_ops.size(sp)))    self.assertEqual(2, self.evaluate(array_ops.rank(sp)))  @test_util.run_in_graph_and_eager_modes  def testSizeDtype(self):    tensor = [1]    self.assertEqual(dtypes.int32, self.evaluate(array_ops.size(tensor)).dtype)    self.assertEqual(        dtypes.int64,        self.evaluate(array_ops.size(tensor, out_type=dtypes.int64)).dtype)class SequenceMaskTest(test_util.TensorFlowTestCase):  def testExceptions(self):    with self.cached_session():      with self.assertRaisesRegex(ValueError, "maxlen must be scalar"):        array_ops.sequence_mask([10, 20], [10, 20])  def testOneDimensionalWithMaxlen(self):    res = array_ops.sequence_mask(constant_op.constant([1, 3, 2]), 5)    self.assertAllEqual(res.get_shape(), [3, 5])    self.assertAllEqual(        res,        [[True, False, False, False, False], [True, True, True, False, False],         [True, True, False, False, False]])  def testOneDimensionalDtypeWithoutMaxlen(self):    # test dtype and default maxlen:    res = array_ops.sequence_mask(        constant_op.constant([0, 1, 4]), dtype=dtypes.float32)    self.assertAllEqual(res.get_shape().as_list(), [3, 4])    self.assertAllEqual(        res, [[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]])  def testOneDimensionalWithoutMaxlen(self):    res = array_ops.sequence_mask(constant_op.constant([0, 1, 4]))    self.assertAllEqual(res.get_shape().as_list(), [3, 4])    self.assertAllEqual(res,                        [[False, False, False, False],                         [True, False, False, False], [True, True, True, True]])  def testTwoDimensional(self):    res = array_ops.sequence_mask(constant_op.constant([[1, 3, 2]]), 5)    self.assertAllEqual(res.get_shape(), [1, 3, 5])    self.assertAllEqual(        res,        [[[True, False, False, False, False], [True, True, True, False, False],          [True, True, False, False, False]]])    # test dtype and default maxlen:    res = array_ops.sequence_mask(        constant_op.constant([[0, 1, 4], [1, 2, 3]]), dtype=dtypes.float32)    self.assertAllEqual(res.get_shape().as_list(), [2, 3, 4])    self.assertAllEqual(        res,        [[[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]],         [[1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 1.0, 0.0]]])  def testDtypes(self):    def check_dtypes(lengths_dtype, maxlen_dtype):      res = array_ops.sequence_mask(          constant_op.constant([1, 3, 2], dtype=lengths_dtype),          constant_op.constant(5, dtype=maxlen_dtype))      self.assertAllEqual(res.get_shape(), [3, 5])      self.assertAllEqual(          res,          [[True, False, False, False, False], [True, True, True, False, False],           [True, True, False, False, False]])    check_dtypes(dtypes.int32, dtypes.int32)    check_dtypes(dtypes.int32, dtypes.int64)    check_dtypes(dtypes.int64, dtypes.int32)    check_dtypes(dtypes.int64, dtypes.int64)  def testOutputDtype(self):    def check_output_dtype(output_dtype):      res = self.evaluate(          array_ops.sequence_mask(              constant_op.constant([1, 3, 2], dtype=dtypes.int32),              constant_op.constant(5, dtype=dtypes.int32),              dtype=output_dtype))      self.assertAllEqual(          res,          self.evaluate(              math_ops.cast([[True, False, False, False, False],                             [True, True, True, False, False],                             [True, True, False, False, False]], output_dtype)))    check_output_dtype(dtypes.bool)    check_output_dtype("bool")    check_output_dtype(np.bool)    check_output_dtype(dtypes.int32)    check_output_dtype("int32")    check_output_dtype(np.int32)    check_output_dtype(dtypes.float32)    check_output_dtype("float32")    check_output_dtype(np.float32)    check_output_dtype(dtypes.int64)    check_output_dtype("float64")    check_output_dtype(np.float64)class ConcatSliceResourceTest(test_util.TensorFlowTestCase):  @test_util.run_in_graph_and_eager_modes  def testConcatSlice(self):    r1 = test_ops.stub_resource_handle_op(container="a", shared_name="b")    r2 = test_ops.stub_resource_handle_op(container="a", shared_name="c")    c = array_ops.stack([r1, r2])    s = array_ops.strided_slice(c, [1], [2])    self.evaluate(test_ops.resource_create_op(s))    with self.assertRaises(errors.AlreadyExistsError):      self.evaluate(test_ops.resource_create_op(r2))class IdentityTest(test_util.TensorFlowTestCase):  @test_util.run_gpu_only  def testEagerIdentity(self):    with context.eager_mode():      def _test(x, y, device):        self.assertAllEqual(x.numpy(), y.numpy())        self.assertTrue(device in y.device.lower())      with test_util.force_gpu():        a = constant_op.constant([[2], [3]], dtype=dtypes.float32)      with test_util.force_gpu():        b = array_ops.identity(a)        _test(a, b, "gpu")      with test_util.force_cpu():        c = array_ops.identity(b)        _test(b, c, "cpu")      with test_util.force_cpu():        d = array_ops.identity(c)        _test(c, d, "cpu")      with test_util.force_gpu():        e = array_ops.identity(d)        _test(d, e, "gpu")class PadTest(test_util.TensorFlowTestCase):  def testEager(self):    with context.eager_mode():      t = constant_op.constant([[1, 2, 3], [4, 5, 6]])      paddings = constant_op.constant([[          1,          1,      ], [2, 2]])      padded = array_ops.pad(t, paddings, "CONSTANT")      self.assertAllEqual(padded.numpy(),                          [[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 2, 3, 0, 0],                           [0, 0, 4, 5, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0]])  def testSymmetricMirrorPadGrad(self):    t = np.broadcast_to(np.arange(0, 7), (3, 2, 1, 7))    paddings = constant_op.constant([        [1, 1],        [0, 0],        [0, 0],        [2, 2],    ])    expected = np.broadcast_to(np.array([9, 27, 27]), (1, 2, 1, 3))    result = gen_array_ops.mirror_pad_grad(t, paddings, "SYMMETRIC")    self.assertAllEqual(result, expected)  def testReflectMirrorPadGrad(self):    t = np.broadcast_to(np.reshape(np.arange(0, 7), (7, 1)), (1, 4, 7, 1))    paddings = constant_op.constant([        [0, 0],        [1, 1],        [2, 2],        [0, 0],    ])    expected = np.broadcast_to(        np.reshape(np.array([16, 18, 8]), (3, 1)), (1, 2, 3, 1))    result = gen_array_ops.mirror_pad_grad(t, paddings, "REFLECT")    self.assertAllEqual(result, expected)class InvertPermutationTest(test_util.TensorFlowTestCase):  def testInvertPermutation(self):    for dtype in [dtypes.int32, dtypes.int64]:      with self.subTest(dtype=dtype, use_gpu=True):        x = constant_op.constant([3, 4, 0, 2, 1], dtype=dtype)        y = array_ops.invert_permutation(x)        self.assertAllEqual(y.get_shape(), [5])        self.assertAllEqual(y, [2, 4, 3, 0, 1])class UnravelIndexTest(test_util.TensorFlowTestCase):  # TODO(b/73086570): Reenable test.  @unittest.skip("Test does not pass internally.")  def testUnravelIndex(self):    with self.cached_session():      for dtype in [dtypes.int32, dtypes.int64]:        with self.subTest(dtype=dtype):          indices_1 = constant_op.constant(1621, dtype=dtype)          dims_1 = constant_op.constant([6, 7, 8, 9], dtype=dtype)          out_1 = array_ops.unravel_index(indices_1, dims_1)          self.assertAllEqual(out_1, [3, 1, 4, 1])          indices_2 = constant_op.constant([1621], dtype=dtype)          dims_2 = constant_op.constant([6, 7, 8, 9], dtype=dtype)          out_2 = array_ops.unravel_index(indices_2, dims_2)          self.assertAllEqual(out_2, [[3], [1], [4], [1]])          indices_3 = constant_op.constant([22, 41, 37], dtype=dtype)          dims_3 = constant_op.constant([7, 6], dtype=dtype)          out_3 = array_ops.unravel_index(indices_3, dims_3)          self.assertAllEqual(out_3, [[3, 6, 6], [4, 5, 1]])  # Test case for GitHub issue 40204.  def testUnravelIndexZeroDim(self):    with self.cached_session():      for dtype in [dtypes.int32, dtypes.int64]:        with self.assertRaisesRegex(errors.InvalidArgumentError,                                    "dims cannot contain a dim of zero"):          indices = constant_op.constant([2, 5, 7], dtype=dtype)          dims = constant_op.constant([3, 0], dtype=dtype)          self.evaluate(array_ops.unravel_index(indices=indices, dims=dims))class GuaranteeConstOpTest(test_util.TensorFlowTestCase):  def testSimple(self):    a = array_ops.constant(10)    guarantee_a = array_ops.guarantee_const(a)    self.assertEqual(10, self.evaluate(guarantee_a))  def testVariables(self):    for use_resource in [False, True]:      with self.subTest(use_resource=use_resource):        a = variable_scope.get_variable(            "var_{}".format(use_resource), [],            initializer=init_ops.constant_initializer(10.0),            use_resource=use_resource)        guarantee_a = array_ops.guarantee_const(a)        self.evaluate(a.initializer)        self.assertEqual(10.0, self.evaluate(guarantee_a))  def testResourceRejection(self):    with ops.device("/cpu:0"):      a = variable_scope.get_variable(          "resource_var", [],          initializer=init_ops.constant_initializer(10.0),          use_resource=True)    with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,                                             "cannot be a resource variable"):      guarantee_a = array_ops.guarantee_const(a.handle)      self.evaluate(a.initializer)      self.evaluate(guarantee_a)class SnapshotOpTest(test_util.TensorFlowTestCase):  def testInvertPermutation(self):    for dtype in [dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]:      with self.subTest(dtype=dtype, use_gpu=True):        x = constant_op.constant([0, 1, 2, 3], dtype=dtype)        y = gen_array_ops.snapshot(x)        self.assertAllEqual(y, [0, 1, 2, 3])@test_util.run_all_in_graph_and_eager_modesclass QuantizeAndDequantizeTest(test_util.TensorFlowTestCase):  # Generates a tensor of the specified `shape` using values from `values`  # scaled by (slice_idx + 1) along `axis` dimension.  def _scale_per_slice(self, shape, axis, values):    # Note: repeats the values if the shape is larger than values.    out = np.take(values, np.remainder(np.arange(np.prod(shape)),                                       len(values))).reshape(shape)    if axis is not None:      scale_shape = [1] * len(shape)      scale_shape[axis] = shape[axis]      out *= np.arange(1, shape[axis] + 1).reshape(scale_shape)    return out  def testAxis(self):    shape = np.array([2, 3, 4, 5])    values = np.array([-1, -0.5, 0, 0.3, 0.8, 0.555, 0.5], dtype=np.float32)    quant_values = np.array(        [-1, -0.5, 0, 38.0 / 128, 102.0 / 128, 71.0 / 128, 0.5],        dtype=np.float32)    for axis in [None, 0, 1, 2, 3]:      with self.subTest(axis=axis):        inputs = constant_op.constant(            self._scale_per_slice(shape, axis, values))        expected = self._scale_per_slice(shape, axis, quant_values)        unused_minmax_value = 0 if axis is None else [0] * shape[axis]        fake_quantized = self.evaluate(            array_ops.quantize_and_dequantize_v2(                inputs,                unused_minmax_value,                unused_minmax_value,                range_given=False,                round_mode="HALF_UP",                axis=axis))        self.assertAllEqual(fake_quantized, expected)        if axis is not None:          fake_quantized = self.evaluate(              array_ops.quantize_and_dequantize_v2(                  inputs,                  unused_minmax_value,                  unused_minmax_value,                  range_given=False,                  axis=(axis - 4)))          self.assertAllClose(fake_quantized, expected)  def testBadAxis(self):    input_tensor = [2.5, 2.5]    input_min = [0, 0]    input_max = [1, 1]    error_message_pattern = "Shape must be at least rank 11 but is rank 1"    # TODO(b/171260356): Eager mode and graph mode throw different error types    error = errors.InvalidArgumentError if context.executing_eagerly(    ) else ValueError    with self.assertRaisesRegex(error, error_message_pattern):      self.evaluate(          array_ops.quantize_and_dequantize_v2(              input=input_tensor,              input_min=input_min,              input_max=input_max,              axis=10))  def testQuantizeDequantizeGrad(self):    shape = (2, 2)    max_threshold = 0    min_threshold = -10    input_value = np.random.rand(2, 2) * 40.0 - 20.0    input_tensor = constant_op.constant(input_value, shape=shape,                                        name="input_tensor")    with self.cached_session():      def f(a):        return array_ops.quantize_and_dequantize_v2(            a,            input_min=min_threshold,            input_max=max_threshold,            range_given=True)      output_grad = gradient_checker_v2.compute_gradient(f, [input_tensor])      self.assertAllClose(output_grad[0], np.zeros([1, 4, 4]))@test_util.run_all_in_graph_and_eager_modesclass SortedSearchTest(test_util.TensorFlowTestCase):  def testUpperBoundFloatHandCoded(self):    cdf = np.array([0, .2, .5, .6, .8, 1.], dtype=np.float32)    arr = np.array([.04, .99, .53, .58, .31, .01, .79, .8, .21],                   dtype=np.float32)    result = np.searchsorted(cdf, arr, side="right")    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="right"))    self.assertAllEqual(result, tf_result)  def testUpperBoundFloatRandomNd(self):    dim_size = 7    for d in range(1, 5):      shape = [dim_size] * d      cdf = np.cumsum(          np.random.uniform(size=shape).astype(np.float32), axis=(d - 1))      arr = np.random.uniform(size=shape).astype(np.float32) * dim_size      tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="right"))      cdf = cdf.reshape([-1, dim_size])      arr = arr.reshape([-1, dim_size])      result = np.zeros(arr.shape, dtype=np.int32)      for i in range(dim_size**(d - 1)):        result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="right")      result = result.reshape(shape)      self.assertAllEqual(result, tf_result)  def testUpperBoundFloatUneven(self):    batch_size = 7    size_search_array = 1000    size_values = 47    cdf = np.cumsum(        np.random.uniform(size=[batch_size, size_search_array]).astype(            np.float32),        axis=1)    arr = np.random.uniform(size=[batch_size, size_values]).astype(        np.float32) * size_search_array    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="right"))    result = np.zeros(arr.shape, dtype=np.int32)    for i in range(batch_size):      result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="right")    self.assertAllEqual(result, tf_result)  def testLowerBoundFloatHandCoded(self):    cdf = np.array([0, .2, .5, .6, .8, 1.], dtype=np.float32)    arr = np.array([.04, .99, .53, .58, .31, .01, .79, .8, .21],                   dtype=np.float32)    result = np.searchsorted(cdf, arr, side="left")    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="left"))    self.assertAllEqual(result, tf_result)  def testLowerBoundFloatRandomNd(self):    dim_size = 7    for d in range(1, 5):      shape = [dim_size] * d      cdf = np.cumsum(          np.random.uniform(size=shape).astype(np.float32), axis=(d - 1))      arr = np.random.uniform(size=shape).astype(np.float32) * dim_size      tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="left"))      cdf = cdf.reshape([-1, dim_size])      arr = arr.reshape([-1, dim_size])      result = np.zeros(arr.shape, dtype=np.int32)      for i in range(dim_size**(d - 1)):        result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="left")      result = result.reshape(shape)      self.assertAllEqual(result, tf_result)  def testLowerBoundFloatUneven(self):    batch_size = 7    size_search_array = 1000    size_values = 47    cdf = np.cumsum(        np.random.uniform(size=[batch_size, size_search_array]).astype(            np.float32),        axis=1)    arr = np.random.uniform(size=[batch_size, size_values]).astype(        np.float32) * size_search_array    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="left"))    result = np.zeros(arr.shape, dtype=np.int32)    for i in range(batch_size):      result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="left")    self.assertAllEqual(result, tf_result)  def testUpperBoundIntHandCoded(self):    cdf = np.array([0, 20, 50, 60, 80, 100], dtype=np.int64)    arr = np.array([4, 99, 53, 58, 31, 1, 79, 8, 21], dtype=np.int64)    result = np.searchsorted(cdf, arr, side="right")    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="right"))    self.assertAllEqual(result, tf_result)  def testUpperBoundIntRandomNd(self):    dim_size = 7    for d in range(1, 5):      shape = [dim_size] * d      cdf = np.cumsum(          np.random.randint(low=0, high=10, size=shape).astype(np.int64),          axis=(d - 1))      arr = np.random.randint(          low=0, high=10 * dim_size, size=shape).astype(np.int64)      tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="right"))      cdf = cdf.reshape([-1, dim_size])      arr = arr.reshape([-1, dim_size])      result = np.zeros(arr.shape, dtype=np.int32)      for i in range(dim_size**(d - 1)):        result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="right")      result = result.reshape(shape)      self.assertAllEqual(result, tf_result)  def testUpperBoundIntUneven(self):    batch_size = 7    size_search_array = 1000    size_values = 47    cdf = np.cumsum(        np.random.randint(low=0, high=10,                          size=[batch_size,                                size_search_array]).astype(np.int64),        axis=1)    arr = np.random.randint(        low=0, high=10 * size_search_array, size=[batch_size,                                                  size_values]).astype(np.int64)    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="right"))    result = np.zeros(arr.shape, dtype=np.int32)    for i in range(batch_size):      result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="right")    self.assertAllEqual(result, tf_result)  def testLowerBoundIntHandCoded(self):    cdf = np.array([0, 20, 50, 60, 80, 100], dtype=np.int64)    arr = np.array([4, 99, 53, 58, 31, 1, 79, 8, 21], dtype=np.int64)    result = np.searchsorted(cdf, arr, side="left")    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="left"))    self.assertAllEqual(result, tf_result)  def testLowerBoundIntRandomNd(self):    dim_size = 7    for d in range(1, 5):      shape = [dim_size] * d      cdf = np.cumsum(          np.random.randint(low=0, high=10, size=shape).astype(np.int64),          axis=(d - 1))      arr = np.random.randint(          low=0, high=10 * dim_size, size=shape).astype(np.int64)      tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="left"))      cdf = cdf.reshape([-1, dim_size])      arr = arr.reshape([-1, dim_size])      result = np.zeros(arr.shape, dtype=np.int32)      for i in range(dim_size**(d - 1)):        result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="left")      result = result.reshape(shape)      self.assertAllEqual(result, tf_result)  def testLowerBoundIntUneven(self):    batch_size = 7    size_search_array = 1000    size_values = 47    cdf = np.cumsum(        np.random.randint(low=0, high=10,                          size=[batch_size,                                size_search_array]).astype(np.int64),        axis=1)    arr = np.random.randint(        low=0, high=10 * size_search_array, size=[batch_size,                                                  size_values]).astype(np.int64)    tf_result = self.evaluate(array_ops.searchsorted(cdf, arr, side="left"))    result = np.zeros(arr.shape, dtype=np.int32)    for i in range(batch_size):      result[i, :] = np.searchsorted(cdf[i, :], arr[i, :], side="left")    self.assertAllEqual(result, tf_result)  def testZeroSequenceSize(self):    dtype = dtypes.int32    for side in ("left", "right"):      with self.subTest(side=side):        self.assertAllEqual(            array_ops.searchsorted(                array_ops.ones([2, 0]),                array_ops.ones([2, 3]),                side=side,                out_type=dtype), array_ops.zeros([2, 3], dtype))  def testZeroValueSize(self):    dtype = dtypes.int32    for side in ("left", "right"):      with self.subTest(side=side):        self.assertAllEqual(            array_ops.searchsorted(                array_ops.ones([2, 3]),                array_ops.ones([2, 0]),                side=side,                out_type=dtype), array_ops.zeros([2, 0], dtype))class BatchGatherNdTest(test_util.TensorFlowTestCase):  def testShapesMatch(self):    """Tests for various different shape combinations."""    shapes = []    # params_shape, indices_shape, batch_dims    shapes.append(((2, 2, 2), (2, 1), 1),)    shapes.append(((2, 2, 2), (2, 2), 1),)    shapes.append(((2, 2, 2), (2, 3), 0),)    shapes.append(((2, 2, 2), (3,), 0),)    shapes.append(((2, 2, 2), (1,), 0),)    shapes.append(((2, 2, 3, 2), (2, 3), 1),)    shapes.append(((2, 2, 3, 2), (2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 1), 1),)    shapes.append(((2, 2, 3, 2), (2, 1, 3), 1),)    shapes.append(((2, 2, 3, 2), (2, 2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 3, 1), 1),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 2), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 1), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 1, 3), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 2, 2), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3, 1), 2),)    for params_shape, indices_shape, batch_dims in shapes:      with self.subTest(          params_shape=params_shape,          indices_shape=indices_shape,          batch_dims=batch_dims):        params = constant_op.constant(1.0, shape=(params_shape))        indices = constant_op.constant(            1, shape=(indices_shape), dtype=dtypes.int32)        out = array_ops.batch_gather_nd(            params=params, indices=indices, batch_dims=batch_dims)        ndims_params = len(params_shape) - batch_dims        ndims_rows = ndims_params - indices_shape[-1]        expected_out_shape = indices_shape[:-1]        if ndims_rows > 0:          expected_out_shape += params_shape[-ndims_rows:]        self.assertSequenceEqual(out.shape, expected_out_shape)  def testReducesToGatherNDWhenBatchDimIsZero(self):    """Confirms setting batch_dims to zero reduces to tf.gather_nd."""    params = constant_op.constant(np.random.uniform(0.0, 1.0, size=(7, 8, 9)))    indices_shapes = []    indices_shapes.append((1,))    indices_shapes.append((3, 1))    indices_shapes.append((3, 3, 1))    indices_shapes.append((2,))    indices_shapes.append((3, 2))    indices_shapes.append((3, 3, 2))    indices_shapes.append((3,))    indices_shapes.append((3, 3))    indices_shapes.append((3, 3, 3))    for indices_shape in indices_shapes:      with self.subTest(indices_shape=indices_shape):        indices = np.random.randint(0, 7, size=indices_shape)        gather_nd_result = gen_array_ops.gather_nd(params, indices)        batch_gather_nd_result = array_ops.batch_gather_nd(            params=params, indices=indices, batch_dims=0)        self.assertAllEqual(gather_nd_result, batch_gather_nd_result)  def testSameResultAsMapFn(self):    """Compares results with gather_nd called on every element with map_fn."""    shapes = []    # params_shape, indices_shape, batch_dims    shapes.append(((2, 2, 2), (2, 1), 1),)    shapes.append(((2, 2, 2), (2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 3), 1),)    shapes.append(((2, 2, 3, 2), (2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 1), 1),)    shapes.append(((2, 2, 3, 2), (2, 1, 3), 1),)    shapes.append(((2, 2, 3, 2), (2, 2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 3, 1), 1),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 2), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 1), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 1, 3), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 2, 2), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3, 1), 2),)    for params_shape, indices_shape, batch_dims in shapes:      with self.subTest(          params_shape=params_shape,          indices_shape=indices_shape,          batch_dims=batch_dims):        params = constant_op.constant(            np.random.uniform(0.0, 1.0, size=(params_shape)))        indices = np.random.randint(0, 2, size=indices_shape)        batch_gather_nd_result = array_ops.batch_gather_nd(            params=params, indices=indices, batch_dims=batch_dims)        if batch_dims > 1:          params = array_ops.reshape(              params, shape=[-1] + list(params_shape[batch_dims:]))          indices = array_ops.reshape(              indices, shape=[-1] + list(indices_shape[batch_dims:]))        map_fn_gather_nd_result = map_fn.map_fn(            fn=self._map_fn_body, elems=(params, indices), dtype=dtypes.float64)        if batch_dims > 1:          out_shape = map_fn_gather_nd_result.shape.as_list()          out_shape = list(params_shape[:batch_dims]) + out_shape[1:]          map_fn_gather_nd_result = array_ops.reshape(              map_fn_gather_nd_result, shape=out_shape)        self.assertAllEqual(map_fn_gather_nd_result, batch_gather_nd_result)  def _map_fn_body(self, elems):    return gen_array_ops.gather_nd(elems[0], elems[1])  def testBatchDimsAsTensor(self):    """Tests Tensor batch_dims as input works as intended."""    shapes = []    # params_shape, indices_shape, batch_dims    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3, 1), 0),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3, 1), 1),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3, 1), 2),)    for params_shape, indices_shape, batch_dims in shapes:      with self.subTest(          params_shape=params_shape,          indices_shape=indices_shape,          batch_dims=batch_dims):        params = constant_op.constant(            np.random.uniform(0.0, 1.0, size=(params_shape)))        indices = np.random.randint(0, 2, size=indices_shape)        batch_gather_nd_result = array_ops.gather_nd(            params=params, indices=indices, batch_dims=batch_dims)        batch_dims_tensor = constant_op.constant([batch_dims])        batch_gather_nd_tensor_batch_dims_result = array_ops.gather_nd(            params=params, indices=indices, batch_dims=batch_dims_tensor)        self.assertAllEqual(batch_gather_nd_tensor_batch_dims_result,                            batch_gather_nd_result)  def testInvalidBatchDimsRaisesException(self):    """Tests whether invalid batch_dims raise expected exceptions."""    params = constant_op.constant(        np.random.uniform(0.0, 1.0, size=(3, 2, 2, 3, 4)))    indices = np.random.randint(0, 2, size=(3, 2, 3))    with self.assertRaises(TypeError):      array_ops.batch_gather_nd(          params=params,          indices=indices,          batch_dims=constant_op.constant((0, 1)))    with self.assertRaises(ValueError):      array_ops.batch_gather_nd(params=params, indices=indices, batch_dims=-1)    with self.assertRaises(ValueError):      array_ops.batch_gather_nd(params=params, indices=indices, batch_dims=4)  def testNoneBatchDimensions(self):    """Tests gather_nd works with None dimensions."""    shapes = []    # params_shape, indices_shape, batch_dims    shapes.append(((2, 2, 2), (2, 1), 1),)    shapes.append(((2, 2, 2), (2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 3), 1),)    shapes.append(((2, 2, 3, 2), (2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 1), 1),)    shapes.append(((2, 2, 3, 2), (2, 1, 3), 1),)    shapes.append(((2, 2, 3, 2), (2, 2, 2), 1),)    shapes.append(((2, 2, 3, 2), (2, 3, 1), 1),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 2), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 1), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 1, 3), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 2, 2), 2),)    shapes.append(((3, 2, 2, 3, 4), (3, 2, 3, 1), 2),)    for params_shape, indices_shape, batch_dims in shapes:      params_ph_shape = list(params_shape)      indices_ph_shape = list(indices_shape)      for i in range(batch_dims):        params_ph_shape[i] = None        indices_ph_shape[i] = None      @def_function.function      def func(params, indices):        return array_ops.batch_gather_nd(            params=params, indices=indices, batch_dims=batch_dims)  # pylint: disable=cell-var-from-loop      f = func.get_concrete_function(          tensor_spec.TensorSpec(params_ph_shape, dtypes.float32),          tensor_spec.TensorSpec(indices_ph_shape, dtypes.int32))      params_val = np.ones(dtype=np.float32, shape=params_shape)      indices_val = np.ones(dtype=np.int32, shape=indices_shape)      res = f(params_val, indices_val)      row_ndims = len(params_shape) - batch_dims - indices_shape[-1]      expected_out_shape = indices_shape[:-1]      if row_ndims > 0:        expected_out_shape += params_shape[-row_ndims:]      self.assertSequenceEqual(res.shape, expected_out_shape)@test_util.run_all_in_graph_and_eager_modesclass RepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase):  @parameterized.parameters(      (3, 4, None),      ([[1, 2], [3, 4]], 2, None),      ([[1, 2], [3, 4]], [1, 2], 0),      ([[1, 2], [3, 4]], [1, 2], 1),      ([[1, 2], [3, 4]], 3, 1),      ([[1, 2], [3, 4]], [1, 2, 3, 4], None),      (np.ones([0, 4]), 0, 1),      (np.ones([1, 2]), [2], None),  )  def testRepeat(self, array, repeats, axis):    array = np.array(array)    @def_function.function(        input_signature=[tensor_spec.TensorSpec(None, dtypes.int32)] * 2)    def repeat_fn(array, repeats):      return array_ops.repeat(array, repeats, axis)    v_tf = array_ops.repeat(constant_op.constant(array), repeats, axis)    v_tf_fn = repeat_fn(        constant_op.constant(array, dtype=dtypes.int32), repeats)    v_np = np.repeat(array, repeats, axis)    self.assertAllEqual(v_tf, v_np)    self.assertAllEqual(v_tf_fn, v_np)@test_util.run_all_in_graph_and_eager_modesclass TileVariantTest(test_util.TensorFlowTestCase):  def test_tile_tensor_list(self):    t = constant_op.constant(np.random.uniform(size=[2, 3, 4]))    handle = list_ops.tensor_list_from_tensor(t, element_shape=None)    with ops.device("CPU:0"):      tiled_handles = array_ops.tile(array_ops.reshape(handle, [1]), [2])    tiled_tensor_0 = list_ops.tensor_list_stack(tiled_handles[0], t.dtype, 2,                                                [3, 4])    tiled_tensor_1 = list_ops.tensor_list_stack(tiled_handles[1], t.dtype, 2,                                                [3, 4])    self.assertAllEqual(t, tiled_tensor_0)    self.assertAllEqual(t, tiled_tensor_1)    # Now mutate some of the lists and make sure the changes are not reflected    # in the tiled handles.    with ops.control_dependencies([        list_ops.tensor_list_scatter([t[0] + 1], [0], input_handle=handle),        list_ops.tensor_list_set_item(tiled_handles[0], 0, t[0] + 2)]):      tiled_tensor_0 = list_ops.tensor_list_stack(tiled_handles[0], t.dtype, 2,                                                  [3, 4])      tiled_tensor_1 = list_ops.tensor_list_stack(tiled_handles[1], t.dtype, 2,                                                  [3, 4])    self.assertAllEqual(t, tiled_tensor_0)    self.assertAllEqual(t, tiled_tensor_1)if __name__ == "__main__":  test_lib.main()