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 ConstantOp."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from google.protobuf import text_format
  
  from tensorflow.core.framework import graph_pb2
  from tensorflow.core.framework import tensor_pb2
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes as dtypes_lib
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import importer
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import logging_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
  from tensorflow.python.util import compat
  
  
  class ConstantTest(test.TestCase):
  
    def _testCpu(self, x):
      np_ans = np.array(x)
      with self.test_session(use_gpu=False):
        tf_ans = ops.convert_to_tensor(x).eval()
      dtype = dtypes_lib.as_dtype(np_ans.dtype)
      if dtype.is_floating or dtype.is_complex:
        self.assertAllClose(np_ans, tf_ans)
      else:
        self.assertAllEqual(np_ans, tf_ans)
  
    def _testGpu(self, x):
      np_ans = np.array(x)
      with self.test_session(use_gpu=True):
        tf_ans = ops.convert_to_tensor(x).eval()
      dtype = dtypes_lib.as_dtype(np_ans.dtype)
      if dtype.is_floating or dtype.is_complex:
        self.assertAllClose(np_ans, tf_ans)
      else:
        self.assertAllEqual(np_ans, tf_ans)
  
    def _testAll(self, x):
      self._testCpu(x)
      self._testGpu(x)
  
    def testBFloat16(self):
      bfloat16 = dtypes_lib.bfloat16.as_numpy_dtype
      self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(bfloat16))
      self._testAll(
          np.random.normal(size=30).reshape([2, 3, 5]).astype(bfloat16))
      self._testAll(np.empty((2, 0, 5)).astype(bfloat16))
  
    def testHalf(self):
      self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float16))
      self._testAll(
          np.random.normal(size=30).reshape([2, 3, 5]).astype(np.float16))
      self._testAll(np.empty((2, 0, 5)).astype(np.float16))
  
    def testFloat(self):
      self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float32))
      self._testAll(
          np.random.normal(size=30).reshape([2, 3, 5]).astype(np.float32))
      self._testAll(np.empty((2, 0, 5)).astype(np.float32))
  
    def testDouble(self):
      self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float64))
      self._testAll(
          np.random.normal(size=30).reshape([2, 3, 5]).astype(np.float64))
      self._testAll(np.empty((2, 0, 5)).astype(np.float64))
  
    def testInt32(self):
      self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.int32))
      self._testAll((100 * np.random.normal(size=30)).reshape([2, 3, 5]).astype(
          np.int32))
      self._testAll(np.empty((2, 0, 5)).astype(np.int32))
  
    def testInt64(self):
      self._testAll(np.arange(-15, 15).reshape([2, 3, 5]).astype(np.int64))
      self._testAll((100 * np.random.normal(size=30)).reshape([2, 3, 5]).astype(
          np.int64))
      self._testAll(np.empty((2, 0, 5)).astype(np.int64))
  
    def testComplex64(self):
      self._testAll(
          np.complex(1, 2) *
          np.arange(-15, 15).reshape([2, 3, 5]).astype(np.complex64))
      self._testAll(
          np.complex(1, 2) *
          np.random.normal(size=30).reshape([2, 3, 5]).astype(np.complex64))
      self._testAll(np.empty((2, 0, 5)).astype(np.complex64))
  
    def testComplex128(self):
      self._testAll(
          np.complex(1, 2) *
          np.arange(-15, 15).reshape([2, 3, 5]).astype(np.complex128))
      self._testAll(
          np.complex(1, 2) *
          np.random.normal(size=30).reshape([2, 3, 5]).astype(np.complex128))
      self._testAll(np.empty((2, 0, 5)).astype(np.complex128))
  
    def testString(self):
      self._testCpu(
          np.array([compat.as_bytes(str(x)) for x in np.arange(-15, 15)]).reshape(
              [2, 3, 5]))
      self._testCpu(np.empty((2, 0, 5)).astype(np.str_))
  
    def testVariant(self):
      # TODO(ebrevdo): Re-enable use_gpu=True once non-DMA Variant
      # copying between CPU and GPU is supported.
      with self.test_session(use_gpu=False):
        variant_tensor = tensor_pb2.TensorProto(
            dtype=dtypes_lib.variant.as_datatype_enum,
            tensor_shape=tensor_shape.TensorShape([]).as_proto(),
            variant_val=[
                tensor_pb2.VariantTensorDataProto(
                    # Match registration in variant_op_registry.cc
                    type_name=b"int",
                    metadata=np.array(1, dtype=np.int32).tobytes())
            ])
        const = constant_op.constant(variant_tensor)
        const_value = const.op.get_attr("value")
  
        # Ensure we stored the tensor proto properly.
        self.assertProtoEquals(variant_tensor, const_value)
  
        # Smoke test -- ensure this executes without trouble.
        # Right now, non-numpy-compatible objects cannot be returned from a
        # session.run call; similarly, objects that can't be converted to
        # native numpy types cannot be passed to ops.convert_to_tensor.
        # TODO(ebrevdo): Add registration mechanism for
        # ops.convert_to_tensor and for session.run output.
        logging_const_op = logging_ops.Print(
            const, [const],
            message="Variant storing an int, decoded const value:").op
        logging_const_op.run()
  
    def testStringWithNulls(self):
      with self.test_session():
        val = ops.convert_to_tensor(b"\0\0\0\0").eval()
      self.assertEqual(len(val), 4)
      self.assertEqual(val, b"\0\0\0\0")
  
      with self.test_session():
        val = ops.convert_to_tensor(b"xx\0xx").eval()
      self.assertEqual(len(val), 5)
      self.assertAllEqual(val, b"xx\0xx")
      nested = [[b"\0\0\0\0", b"xx\0xx"], [b"\0_\0_\0_\0", b"\0"]]
  
      with self.test_session():
        val = ops.convert_to_tensor(nested).eval()
      # NOTE(mrry): Do not use assertAllEqual, because it converts nested to a
      #   numpy array, which loses the null terminators.
      self.assertEqual(val.tolist(), nested)
  
    def testExplicitShapeNumPy(self):
      with ops.Graph().as_default():
        c = constant_op.constant(
            np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float32),
            shape=[2, 3, 5])
      self.assertEqual(c.get_shape(), [2, 3, 5])
  
    def testImplicitShapeNumPy(self):
      with ops.Graph().as_default():
        c = constant_op.constant(
            np.arange(-15, 15).reshape([2, 3, 5]).astype(np.float32))
      self.assertEqual(c.get_shape(), [2, 3, 5])
  
    def testExplicitShapeList(self):
      with ops.Graph().as_default():
        c = constant_op.constant([1, 2, 3, 4, 5, 6, 7], shape=[7])
      self.assertEqual(c.get_shape(), [7])
  
    def testImplicitShapeList(self):
      with ops.Graph().as_default():
        c = constant_op.constant([1, 2, 3, 4, 5, 6, 7])
      self.assertEqual(c.get_shape(), [7])
  
    def testExplicitShapeNumber(self):
      with ops.Graph().as_default():
        c = constant_op.constant(1, shape=[1])
      self.assertEqual(c.get_shape(), [1])
  
    def testImplicitShapeNumber(self):
      with ops.Graph().as_default():
        c = constant_op.constant(1)
      self.assertEqual(c.get_shape(), [])
  
    def testShapeInconsistent(self):
      with ops.Graph().as_default():
        c = constant_op.constant([1, 2, 3, 4, 5, 6, 7], shape=[10])
      self.assertEqual(c.get_shape(), [10])
  
    # pylint: disable=g-long-lambda
    def testShapeWrong(self):
      with ops.Graph().as_default():
        with self.assertRaisesWithPredicateMatch(
            ValueError,
            lambda e: ("Too many elements provided. Needed at most 5, "
                       "but received 7" == str(e))):
          constant_op.constant([1, 2, 3, 4, 5, 6, 7], shape=[5])
  
    # pylint: enable=g-long-lambda
    # TODO(b/35396543): Temporarily disable: suspicion that
    # this is causing test timeouts.
    def _testTooLargeConstant(self):
      with ops.Graph().as_default():
        large_array = np.zeros((512, 1024, 1024), dtype=np.float32)
        with self.assertRaisesRegexp(
            ValueError,
            "Cannot create a tensor proto whose content is larger than 2GB."):
          c = constant_op.constant(large_array)
  
    # TODO(b/35396543): Temporarily disable: suspicion that
    # this is causing test timeouts.
    def _testTooLargeGraph(self):
      with ops.Graph().as_default() as g:
        large_array = np.zeros((256, 1024, 1024), dtype=np.float32)
        c = constant_op.constant(large_array)
        d = constant_op.constant(large_array)
        with self.assertRaisesRegexp(ValueError,
                                     "GraphDef cannot be larger than 2GB."):
          g.as_graph_def()
  
    def testSparseValuesRaiseErrors(self):
      with self.assertRaisesRegexp(ValueError,
                                   "setting an array element with a sequence"):
        c = constant_op.constant([[1, 2], [3]], dtype=dtypes_lib.int32)
  
      with self.assertRaisesRegexp(ValueError, "must be a dense"):
        c = constant_op.constant([[1, 2], [3]])
  
      with self.assertRaisesRegexp(ValueError, "must be a dense"):
        c = constant_op.constant([[1, 2], [3], [4, 5]])
  
  
  class AsTensorTest(test.TestCase):
  
    def testAsTensorForTensorInput(self):
      with ops.Graph().as_default():
        t = constant_op.constant(10.0)
        x = ops.convert_to_tensor(t)
      self.assertIs(t, x)
  
    def testAsTensorForNonTensorInput(self):
      with ops.Graph().as_default():
        x = ops.convert_to_tensor(10.0)
      self.assertTrue(isinstance(x, ops.Tensor))
  
    def testAsTensorForShapeInput(self):
      with self.test_session():
        x = ops.convert_to_tensor(tensor_shape.TensorShape([]))
        self.assertEqual(dtypes_lib.int32, x.dtype)
        self.assertAllEqual([], x.eval())
  
        x = ops.convert_to_tensor(tensor_shape.TensorShape([1, 2, 3]))
        self.assertEqual(dtypes_lib.int32, x.dtype)
        self.assertAllEqual([1, 2, 3], x.eval())
  
        x = ops.convert_to_tensor(tensor_shape.TensorShape([2**31-1, 2, 3]))
        self.assertEqual(dtypes_lib.int32, x.dtype)
        self.assertAllEqual([2**31-1, 2, 3], x.eval())
  
        x = ops.convert_to_tensor(tensor_shape.TensorShape([2**31-1, 2, 3]),
                                  dtype=dtypes_lib.int32)
        self.assertEqual(dtypes_lib.int32, x.dtype)
        self.assertAllEqual([2**31-1, 2, 3], x.eval())
  
        x = ops.convert_to_tensor(tensor_shape.TensorShape([2**31, 2, 3]))
        self.assertEqual(dtypes_lib.int64, x.dtype)
        self.assertAllEqual([2**31, 2, 3], x.eval())
  
        x = ops.convert_to_tensor(tensor_shape.TensorShape([2**31, 2, 3]),
                                  dtype=dtypes_lib.int64)
        self.assertEqual(dtypes_lib.int64, x.dtype)
        self.assertAllEqual([2**31, 2, 3], x.eval())
  
        with self.assertRaisesRegexp(
            ValueError, "a dimension is too large .2147483648."):
          x = ops.convert_to_tensor(tensor_shape.TensorShape([2**31, 2, 3]),
                                    dtype=dtypes_lib.int32)
  
        x = ops.convert_to_tensor(
            tensor_shape.TensorShape([1, 2, 3]), dtype=dtypes_lib.int64)
        self.assertEqual(dtypes_lib.int64, x.dtype)
        self.assertAllEqual([1, 2, 3], x.eval())
  
        x = array_ops.reshape(
            array_ops.zeros([6]), tensor_shape.TensorShape([2, 3]))
        self.assertAllEqual([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], x.eval())
  
      with self.assertRaisesRegexp(ValueError, "partially known"):
        ops.convert_to_tensor(tensor_shape.TensorShape(None))
  
      with self.assertRaisesRegexp(ValueError, "partially known"):
        ops.convert_to_tensor(tensor_shape.TensorShape([1, None, 64]))
  
      with self.assertRaises(TypeError):
        ops.convert_to_tensor(
            tensor_shape.TensorShape([1, 2, 3]), dtype=dtypes_lib.float32)
  
    def testAsTensorForDimensionInput(self):
      with self.test_session():
        x = ops.convert_to_tensor(tensor_shape.TensorShape([1, 2, 3])[1])
        self.assertEqual(dtypes_lib.int32, x.dtype)
        self.assertAllEqual(2, x.eval())
  
        x = ops.convert_to_tensor(
            tensor_shape.TensorShape([1, 2, 3])[1], dtype=dtypes_lib.int64)
        self.assertEqual(dtypes_lib.int64, x.dtype)
        self.assertAllEqual(2, x.eval())
  
      with self.assertRaisesRegexp(ValueError, "unknown Dimension"):
        ops.convert_to_tensor(tensor_shape.TensorShape(None)[1])
  
      with self.assertRaisesRegexp(ValueError, "unknown Dimension"):
        ops.convert_to_tensor(tensor_shape.TensorShape([1, None, 64])[1])
  
      with self.assertRaises(TypeError):
        ops.convert_to_tensor(
            tensor_shape.TensorShape([1, 2, 3])[1], dtype=dtypes_lib.float32)
  
  
  class IdentityOpTest(test.TestCase):
  
    def testIdTensor(self):
      with ops.Graph().as_default():
        x = constant_op.constant(2.0, shape=[6], name="input")
        id_op = array_ops.identity(x, name="id")
      self.assertTrue(isinstance(id_op.op.inputs[0], ops.Tensor))
      self.assertProtoEquals("name: 'id' op: 'Identity' input: 'input' "
                             "attr { key: 'T' value { type: DT_FLOAT } }",
                             id_op.op.node_def)
  
  
  class ZerosTest(test.TestCase):
  
    def _Zeros(self, shape):
      with self.test_session():
        ret = array_ops.zeros(shape)
        self.assertEqual(shape, ret.get_shape())
        return ret.eval()
  
    def testConst(self):
      self.assertTrue(
          np.array_equal(self._Zeros([2, 3]), np.array([[0] * 3] * 2)))
  
    def testScalar(self):
      self.assertEqual(0, self._Zeros([]))
      self.assertEqual(0, self._Zeros(()))
      with self.test_session():
        scalar = array_ops.zeros(constant_op.constant([], dtype=dtypes_lib.int32))
        self.assertEqual(0, scalar.eval())
  
    def testDynamicSizes(self):
      np_ans = np.array([[0] * 3] * 2)
      with self.test_session():
        # Creates a tensor of 2 x 3.
        d = array_ops.fill([2, 3], 12., name="fill")
        # Constructs a tensor of zeros of the same dimensions as "d".
        z = array_ops.zeros(array_ops.shape(d))
        out = z.eval()
      self.assertAllEqual(np_ans, out)
      self.assertShapeEqual(np_ans, d)
      self.assertShapeEqual(np_ans, z)
  
    def testDtype(self):
      with self.test_session():
        d = array_ops.fill([2, 3], 12., name="fill")
        self.assertEqual(d.get_shape(), [2, 3])
        # Test default type for both constant size and dynamic size
        z = array_ops.zeros([2, 3])
        self.assertEqual(z.dtype, dtypes_lib.float32)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.zeros([2, 3]))
        z = array_ops.zeros(array_ops.shape(d))
        self.assertEqual(z.dtype, dtypes_lib.float32)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.zeros([2, 3]))
        # Test explicit type control
        for dtype in [
            dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int32,
            dtypes_lib.uint8, dtypes_lib.int16, dtypes_lib.int8,
            dtypes_lib.complex64, dtypes_lib.complex128, dtypes_lib.int64,
            dtypes_lib.bool, dtypes_lib.string
        ]:
          z = array_ops.zeros([2, 3], dtype=dtype)
          self.assertEqual(z.dtype, dtype)
          self.assertEqual([2, 3], z.get_shape())
          z_value = z.eval()
          self.assertFalse(np.any(z_value))
          self.assertEqual((2, 3), z_value.shape)
          z = array_ops.zeros(array_ops.shape(d), dtype=dtype)
          self.assertEqual(z.dtype, dtype)
          self.assertEqual([2, 3], z.get_shape())
          z_value = z.eval()
          self.assertFalse(np.any(z_value))
          self.assertEqual((2, 3), z_value.shape)
  
  
  class ZerosLikeTest(test.TestCase):
  
    def _compareZeros(self, dtype, fully_defined_shape, use_gpu):
      with self.test_session(use_gpu=use_gpu):
        # Creates a tensor of non-zero values with shape 2 x 3.
        # NOTE(kearnes): The default numpy dtype associated with tf.string is
        # np.object (and can't be changed without breaking a lot things), which
        # causes a TypeError in constant_op.constant below. Here we catch the
        # special case of tf.string and set the numpy dtype appropriately.
        if dtype == dtypes_lib.string:
          numpy_dtype = np.string_
        else:
          numpy_dtype = dtype.as_numpy_dtype
        if fully_defined_shape:
          d = constant_op.constant(
              np.ones((2, 3), dtype=numpy_dtype), dtype=dtype)
        else:
          d = array_ops.placeholder(dtype=dtype)
        # Constructs a tensor of zeros of the same dimensions and type as "d".
        z_var = array_ops.zeros_like(d)
        # Test that the type is correct
        self.assertEqual(z_var.dtype, dtype)
        # Test that the shape is correct
        if fully_defined_shape:
          self.assertEqual([2, 3], z_var.get_shape())
  
        # Test that the value is correct
        feed_dict = {}
        if not fully_defined_shape:
          feed_dict[d] = np.ones((2, 3), dtype=numpy_dtype)
        z_value = z_var.eval(feed_dict=feed_dict)
        self.assertFalse(np.any(z_value))
        self.assertEqual((2, 3), z_value.shape)
  
    def testZerosLikeCPU(self):
      for dtype in [
          dtypes_lib.half, dtypes_lib.float32, dtypes_lib.float64,
          dtypes_lib.int8, dtypes_lib.uint8, dtypes_lib.int16, dtypes_lib.uint16,
          dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.bool,
          dtypes_lib.complex64, dtypes_lib.complex128, dtypes_lib.string
      ]:
        self._compareZeros(dtype, fully_defined_shape=False, use_gpu=False)
        self._compareZeros(dtype, fully_defined_shape=True, use_gpu=False)
  
    def testZerosLikeGPU(self):
      for dtype in [
          dtypes_lib.half, dtypes_lib.float32, dtypes_lib.float64,
          dtypes_lib.int32, dtypes_lib.int64,
          dtypes_lib.complex64, dtypes_lib.complex128,
          dtypes_lib.bool
      ]:
        self._compareZeros(dtype, fully_defined_shape=False, use_gpu=True)
        self._compareZeros(dtype, fully_defined_shape=True, use_gpu=True)
  
    def testZerosLikePartialShape(self):
      d = array_ops.placeholder(dtypes_lib.float32, shape=[None, 4, None])
      z = array_ops.zeros_like(d)
      self.assertEqual(d.get_shape().as_list(), z.get_shape().as_list())
  
    def testZerosLikeDtype(self):
      # Make sure zeros_like works even for dtypes that cannot be cast between
      with self.test_session():
        shape = (3, 5)
        dtypes = np.float32, np.complex64
        for in_type in dtypes:
          x = np.arange(15).astype(in_type).reshape(*shape)
          for out_type in dtypes:
            y = array_ops.zeros_like(x, dtype=out_type).eval()
            self.assertEqual(y.dtype, out_type)
            self.assertEqual(y.shape, shape)
            self.assertAllEqual(y, np.zeros(shape, dtype=out_type))
  
    def testZerosLikeVariant(self):
      # TODO(ebrevdo): Re-enable use_gpu=True once non-DMA Variant
      # copying between CPU and GPU is supported AND we register a
      # ZerosLike callback for GPU for Variant storing primitive types
      # in variant_op_registry.cc.
      with self.test_session(use_gpu=False):
        variant_tensor = tensor_pb2.TensorProto(
            dtype=dtypes_lib.variant.as_datatype_enum,
            tensor_shape=tensor_shape.TensorShape([]).as_proto(),
            variant_val=[
                tensor_pb2.VariantTensorDataProto(
                    # Match registration in variant_op_registry.cc
                    type_name=b"int",
                    metadata=np.array(1, dtype=np.int32).tobytes())
            ])
        const_variant = constant_op.constant(variant_tensor)
        zeros_like = array_ops.zeros_like(const_variant)
        zeros_like_op = logging_ops.Print(
            zeros_like, [const_variant, zeros_like],
            message="Variant storing an int, input and output of zeros_like:").op
  
        # Smoke test -- ensure this executes without trouble.
        # Right now, non-numpy-compatible objects cannot be returned from a
        # session.run call; similarly, objects that can't be converted to
        # native numpy types cannot be passed to ops.convert_to_tensor.
        # TODO(ebrevdo): Add registration mechanism for
        # ops.convert_to_tensor and for session.run output.
        zeros_like_op.run()
  
  
  class OnesTest(test.TestCase):
  
    def _Ones(self, shape):
      with self.test_session():
        ret = array_ops.ones(shape)
        self.assertEqual(shape, ret.get_shape())
        return ret.eval()
  
    def testConst(self):
      self.assertTrue(np.array_equal(self._Ones([2, 3]), np.array([[1] * 3] * 2)))
  
    def testScalar(self):
      self.assertEqual(1, self._Ones([]))
      self.assertEqual(1, self._Ones(()))
      with self.test_session():
        scalar = array_ops.ones(constant_op.constant([], dtype=dtypes_lib.int32))
        self.assertEqual(1, scalar.eval())
  
    def testDynamicSizes(self):
      np_ans = np.array([[1] * 3] * 2)
      with self.test_session():
        # Creates a tensor of 2 x 3.
        d = array_ops.fill([2, 3], 12., name="fill")
        # Constructs a tensor of ones of the same dimensions as "d".
        z = array_ops.ones(array_ops.shape(d))
        out = z.eval()
      self.assertAllEqual(np_ans, out)
      self.assertShapeEqual(np_ans, d)
      self.assertShapeEqual(np_ans, z)
  
    def testAutoPack(self):
      with self.test_session():
        h = array_ops.placeholder(dtypes_lib.int32, shape=[])
        w = array_ops.placeholder(dtypes_lib.int32, shape=[])
        z = array_ops.ones([h, w])
        out = z.eval(feed_dict={h: 4, w: 16})
      self.assertAllEqual(out, np.array([[1] * 16] * 4))
  
    def testDtype(self):
      with self.test_session():
        d = array_ops.fill([2, 3], 12., name="fill")
        self.assertEqual(d.get_shape(), [2, 3])
        # Test default type for both constant size and dynamic size
        z = array_ops.ones([2, 3])
        self.assertEqual(z.dtype, dtypes_lib.float32)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.ones([2, 3]))
        z = array_ops.ones(array_ops.shape(d))
        self.assertEqual(z.dtype, dtypes_lib.float32)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.ones([2, 3]))
        # Test explicit type control
        for dtype in (dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int32,
                      dtypes_lib.uint8, dtypes_lib.int16, dtypes_lib.int8,
                      dtypes_lib.complex64, dtypes_lib.complex128,
                      dtypes_lib.int64, dtypes_lib.bool):
          z = array_ops.ones([2, 3], dtype=dtype)
          self.assertEqual(z.dtype, dtype)
          self.assertEqual([2, 3], z.get_shape())
          self.assertAllEqual(z.eval(), np.ones([2, 3]))
          z = array_ops.ones(array_ops.shape(d), dtype=dtype)
          self.assertEqual(z.dtype, dtype)
          self.assertEqual([2, 3], z.get_shape())
          self.assertAllEqual(z.eval(), np.ones([2, 3]))
  
  
  class OnesLikeTest(test.TestCase):
  
    def testOnesLike(self):
      for dtype in [
          dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int8,
          dtypes_lib.uint8, dtypes_lib.int16, dtypes_lib.uint16, dtypes_lib.int32,
          dtypes_lib.int64, dtypes_lib.bool, dtypes_lib.complex64,
          dtypes_lib.complex128
      ]:
        numpy_dtype = dtype.as_numpy_dtype
        with self.test_session():
          # Creates a tensor of non-zero values with shape 2 x 3.
          d = constant_op.constant(
              np.ones(
                  (2, 3), dtype=numpy_dtype), dtype=dtype)
          # Constructs a tensor of zeros of the same dimensions and type as "d".
          z_var = array_ops.ones_like(d)
          # Test that the type is correct
          self.assertEqual(z_var.dtype, dtype)
          z_value = z_var.eval()
  
        # Test that the value is correct
        self.assertTrue(np.array_equal(z_value, np.array([[1] * 3] * 2)))
        self.assertEqual([2, 3], z_var.get_shape())
  
    def testOnesLikePartialShape(self):
      d = array_ops.placeholder(dtypes_lib.float32, shape=[None, 4, None])
      z = array_ops.ones_like(d)
      self.assertEqual(d.get_shape().as_list(), z.get_shape().as_list())
  
  
  class FillTest(test.TestCase):
  
    def _compare(self, dims, val, np_ans, use_gpu):
      with self.test_session(use_gpu=use_gpu):
        tf_ans = array_ops.fill(dims, val, name="fill")
        out = tf_ans.eval()
      self.assertAllClose(np_ans, out)
      # Fill does not set the shape.
      # self.assertShapeEqual(np_ans, tf_ans)
  
    def _compareAll(self, dims, val, np_ans):
      self._compare(dims, val, np_ans, False)
      self._compare(dims, val, np_ans, True)
  
    def testFillFloat(self):
      np_ans = np.array([[3.1415] * 3] * 2).astype(np.float32)
      self._compareAll([2, 3], np_ans[0][0], np_ans)
  
    def testFillDouble(self):
      np_ans = np.array([[3.1415] * 3] * 2).astype(np.float64)
      self._compareAll([2, 3], np_ans[0][0], np_ans)
  
    def testFillInt32(self):
      np_ans = np.array([[42] * 3] * 2).astype(np.int32)
      self._compareAll([2, 3], np_ans[0][0], np_ans)
  
    def testFillInt64(self):
      np_ans = np.array([[-42] * 3] * 2).astype(np.int64)
      self._compareAll([2, 3], np_ans[0][0], np_ans)
  
    def testFillComplex64(self):
      np_ans = np.array([[0.15] * 3] * 2).astype(np.complex64)
      self._compare([2, 3], np_ans[0][0], np_ans, use_gpu=False)
  
    def testFillComplex128(self):
      np_ans = np.array([[0.15] * 3] * 2).astype(np.complex128)
      self._compare([2, 3], np_ans[0][0], np_ans, use_gpu=False)
  
    def testFillString(self):
      np_ans = np.array([[b"yolo"] * 3] * 2)
      with self.test_session(use_gpu=False):
        tf_ans = array_ops.fill([2, 3], np_ans[0][0], name="fill").eval()
      self.assertAllEqual(np_ans, tf_ans)
  
    def testFillNegative(self):
      with self.test_session():
        for shape in (-1,), (2, -1), (-1, 2), (-2), (-3):
          with self.assertRaises(ValueError):
            array_ops.fill(shape, 7)
  
        # Using a placeholder so this won't be caught in static analysis.
        dims = array_ops.placeholder(dtypes_lib.int32)
        fill_t = array_ops.fill(dims, 3.0)
        for shape in (-1,), (2, -1), (-1, 2), (-2), (-3):
          with self.assertRaises(errors_impl.InvalidArgumentError):
            fill_t.eval({dims: shape})
  
    def testShapeFunctionEdgeCases(self):
      # Non-vector dimensions.
      with self.assertRaises(ValueError):
        array_ops.fill([[0, 1], [2, 3]], 1.0)
  
      # Non-scalar value.
      with self.assertRaises(ValueError):
        array_ops.fill([3, 2], [1.0, 2.0])
  
      # Partial dimension information.
      f = array_ops.fill(array_ops.placeholder(dtypes_lib.int32, shape=(4,)), 3.0)
      self.assertEqual([None, None, None, None], f.get_shape().as_list())
  
      f = array_ops.fill(
          [array_ops.placeholder(
              dtypes_lib.int32, shape=()), 17], 1.0)
      self.assertEqual([None, 17], f.get_shape().as_list())
  
    def testGradient(self):
      with self.test_session():
        in_v = constant_op.constant(5.0)
        out_shape = [3, 2]
        out_filled = array_ops.fill(out_shape, in_v)
        err = gradient_checker.compute_gradient_error(in_v, [], out_filled,
                                                      out_shape)
      self.assertLess(err, 1e-3)
  
  
  class PlaceholderTest(test.TestCase):
  
    def testDtype(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.float32, shape=(10, 10), name="p")
        p_identity = array_ops.identity(p)
        feed_array = np.random.rand(10, 10)
        self.assertAllClose(
            p_identity.eval(feed_dict={p: feed_array}), feed_array)
  
        with self.assertRaisesOpError(
            "must feed a value for placeholder tensor 'p' with dtype float"):
          p_identity.eval()
  
    def testShape(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.float32, shape=(10, 10), name="p")
        p_identity = array_ops.identity(p)
        feed_array = np.random.rand(10, 10)
        self.assertAllClose(
            p_identity.eval(feed_dict={p: feed_array}), feed_array)
  
        with self.assertRaisesOpError(
            "must feed a value for placeholder tensor 'p' with dtype float and "
            r"shape \[10,10\]"):
          p_identity.eval()
  
        with self.assertRaisesWithPredicateMatch(
            ValueError, lambda e: "Cannot feed value of shape" in str(e)):
          p_identity.eval(feed_dict={p: feed_array[:5, :5]})
  
    def testUnknownShape(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.float32, shape=None, name="p")
        p_identity = array_ops.identity(p)
        # can feed anything
        feed_array = np.random.rand(10, 3)
        self.assertAllClose(
            p_identity.eval(feed_dict={p: feed_array}), feed_array)
        feed_array = np.random.rand(4, 2, 5)
        self.assertAllClose(
            p_identity.eval(feed_dict={p: feed_array}), feed_array)
  
    def testScalarShape(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.float32, shape=[], name="p")
        p_identity = array_ops.identity(p)
        self.assertAllClose(p_identity.eval(feed_dict={p: 5}), 5)
  
    def testPartialShape(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.float32, shape=[None, 3], name="p")
        p_identity = array_ops.identity(p)
        feed_array = np.random.rand(10, 3)
        self.assertAllClose(
            p_identity.eval(feed_dict={p: feed_array}), feed_array)
  
        with self.assertRaisesWithPredicateMatch(
            ValueError, lambda e: "Cannot feed value of shape" in str(e)):
          p_identity.eval(feed_dict={p: feed_array[:5, :2]})
  
    def testPartialShapeWhenNotFed(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.float32, shape=[None, 3], name="p")
        p_identity = array_ops.identity(p)
  
        # Should trigger an operator error, not a shape error.
        with self.assertRaisesOpError(
            "must feed a value for placeholder tensor 'p' with dtype float"):
          p_identity.eval()
  
    def testControlDependency(self):
      with self.test_session():
        p = array_ops.placeholder(dtypes_lib.int32, shape=[], name="p")
        with ops.control_dependencies([p]):
          c = constant_op.constant(5, dtypes_lib.int32)
        d = math_ops.multiply(p, c)
        val = np.array(2).astype(np.int)
        self.assertEqual(10, d.eval(feed_dict={p: val}))
  
    def testBadShape(self):
      with self.assertRaises(ValueError):
        array_ops.placeholder(dtypes_lib.float32, shape=(-1, 10))
  
    def testTensorStr(self):
      a = array_ops.placeholder(dtypes_lib.float32, shape=None, name="a")
      self.assertEqual("<tf.Tensor 'a:0' shape=<unknown> dtype=float32>", repr(a))
  
      b = array_ops.placeholder(dtypes_lib.int32, shape=(32, 40), name="b")
      self.assertEqual("<tf.Tensor 'b:0' shape=(32, 40) dtype=int32>", repr(b))
  
      c = array_ops.placeholder(dtypes_lib.qint32, shape=(32, None, 2), name="c")
      self.assertEqual("<tf.Tensor 'c:0' shape=(32, ?, 2) dtype=qint32>", repr(c))
  
    def testOldGraph(self):
      # Load graph generated from earlier version of TF where
      # placeholder shape was not set.
      #
      # a = tf.placeholder(tf.float32)
      # b = a + 1.0
      #
      # Older graph's default shape is 'shape {}', not 'shape {
      # unknown_rank: true }'
      graph = """
  node {
    name: "Placeholder"
    op: "Placeholder"
    attr {
      key: "dtype"
      value {
        type: DT_FLOAT
      }
    }
    attr {
      key: "shape"
      value {
        shape {
        }
      }
    }
  }
  node {
    name: "add/y"
    op: "Const"
    attr {
      key: "dtype"
      value {
        type: DT_FLOAT
      }
    }
    attr {
      key: "value"
      value {
        tensor {
          dtype: DT_FLOAT
          tensor_shape {
          }
          float_val: 1.0
        }
      }
    }
  }
  node {
    name: "add"
    op: "Add"
    input: "Placeholder"
    input: "add/y"
    attr {
      key: "T"
      value {
        type: DT_FLOAT
      }
    }
  }
  versions {
    producer: 21
  }
  """
      gdef = graph_pb2.GraphDef()
      text_format.Merge(graph, gdef)
      with self.test_session():
        p, ret = importer.import_graph_def(
            gdef, return_elements=["Placeholder:0", "add:0"])
  
        # Feed in a vector of two elements.  Since the producer version
        # of 21, a shape of {} is interpreted as "any shape".  If
        # producer version were 22, then we'd get a shape mismatch
        # error.
        self.assertAllEqual([2.0, 3.0], ret.eval(feed_dict={p: [1.0, 2.0]}))
  
  
  class PlaceholderWithDefaultTest(test.TestCase):
  
    def testFullShape(self):
      with self.test_session():
        p = array_ops.placeholder_with_default([[2, 2], [2, 2]], shape=[2, 2])
        a = array_ops.identity(p)
        self.assertAllEqual([[2, 2], [2, 2]], a.eval())
        self.assertAllEqual(
            [[3, 3], [3, 3]], a.eval(feed_dict={p: [[3, 3], [3, 3]]}))
  
        with self.assertRaises(ValueError):
          a.eval(feed_dict={p: [[6, 6, 6], [6, 6, 6]]})
  
    def testPartialShape(self):
      with self.test_session():
        p = array_ops.placeholder_with_default([1, 2, 3], shape=[None])
        a = array_ops.identity(p)
        self.assertAllEqual([1, 2, 3], a.eval())
        self.assertAllEqual([3, 37], a.eval(feed_dict={p: [3, 37]}))
  
        with self.assertRaises(ValueError):
          a.eval(feed_dict={p: [[2, 2], [2, 2]]})
  
    def testNoShape(self):
      with self.test_session():
        p = array_ops.placeholder_with_default([17], shape=None)
        a = array_ops.identity(p)
        self.assertAllEqual([17], a.eval())
        self.assertAllEqual([3, 37], a.eval(feed_dict={p: [3, 37]}))
        self.assertAllEqual(
            [[3, 3], [3, 3]], a.eval(feed_dict={p: [[3, 3], [3, 3]]}))
  
    def testGradient(self):
      with self.test_session():
        x = array_ops.placeholder(dtypes_lib.float32, [5, 7])
        y = array_ops.placeholder_with_default(x, None)
        err = gradient_checker.compute_gradient_error(x, [5, 7], y, [5, 7])
        self.assertLess(err, 1e-3)
  
  if __name__ == "__main__":
    test.main()
  