#   Copyright (c) 2018 PaddlePaddle 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.
import numbers
import unittest

import numpy as np
from op_test import get_device_place, is_custom_device

import paddle
from paddle import base
from paddle.base import core


class TestTensorPtr(unittest.TestCase):
    def test_tensor_ptr(self):
        t = core.DenseTensor()
        np_arr = np.zeros([2, 3])
        t.set(np_arr, core.CPUPlace())
        self.assertGreater(t._ptr(), 0)


class TestTensor(unittest.TestCase):
    def setUp(self):
        self.support_dtypes = [
            'bool',
            'uint8',
            'int8',
            'int16',
            'int32',
            'int64',
            'float16',
            'float32',
            'float64',
            'complex64',
            'complex128',
        ]

    def test_int_tensor(self):
        scope = core.Scope()
        var = scope.var("test_tensor")
        place = core.CPUPlace()

        tensor = var.get_tensor()

        tensor._set_dims([1000, 784])
        tensor._alloc_int(place)
        tensor_array = np.array(tensor)
        self.assertEqual((1000, 784), tensor_array.shape)
        tensor_array[3, 9] = 1
        tensor_array[19, 11] = 2
        tensor.set(tensor_array, place)

        tensor_array_2 = np.array(tensor)
        self.assertEqual(1, tensor_array_2[3, 9])
        self.assertEqual(2, tensor_array_2[19, 11])

    def test_float_tensor(self):
        scope = core.Scope()
        var = scope.var("test_tensor")
        place = core.CPUPlace()

        tensor = var.get_tensor()

        tensor._set_dims([1000, 784])
        tensor._alloc_float(place)

        tensor_array = np.array(tensor)
        self.assertEqual((1000, 784), tensor_array.shape)
        tensor_array[3, 9] = 1.0
        tensor_array[19, 11] = 2.0
        tensor.set(tensor_array, place)

        tensor_array_2 = np.array(tensor)
        self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
        self.assertAlmostEqual(2.0, tensor_array_2[19, 11])

    def test_int8_tensor(self):
        scope = core.Scope()
        var = scope.var("int8_tensor")
        cpu_tensor = var.get_tensor()
        tensor_array = np.random.randint(
            -127, high=128, size=[100, 200], dtype=np.int8
        )
        place = core.CPUPlace()
        cpu_tensor.set(tensor_array, place)
        cpu_tensor_array_2 = np.array(cpu_tensor)
        self.assertAlmostEqual(cpu_tensor_array_2.all(), tensor_array.all())

        if core.is_compiled_with_cuda() or is_custom_device():
            cuda_tensor = var.get_tensor()
            tensor_array = np.random.randint(
                -127, high=128, size=[100, 200], dtype=np.int8
            )
            place = get_device_place()
            cuda_tensor.set(tensor_array, place)
            cuda_tensor_array_2 = np.array(cuda_tensor)
            self.assertAlmostEqual(
                cuda_tensor_array_2.all(), tensor_array.all()
            )

    def test_complex64_tensor(self):
        scope = core.Scope()
        var = scope.var("complex64_tensor")
        cpu_tensor = var.get_tensor()
        tensor_array = (
            np.random.uniform(-1, 1, (100, 200))
            + 1j * np.random.uniform(-1, 1, (100, 200))
        ).astype(np.complex64)
        place = core.CPUPlace()
        cpu_tensor.set(tensor_array, place)
        cpu_tensor_array_2 = np.array(cpu_tensor)
        self.assertAlmostEqual(cpu_tensor_array_2.all(), tensor_array.all())

        if core.is_compiled_with_cuda() or is_custom_device():
            cuda_tensor = var.get_tensor()
            tensor_array = (
                np.random.uniform(-1, 1, (100, 200))
                + 1j * np.random.uniform(-1, 1, (100, 200))
            ).astype(np.complex64)
            place = get_device_place()
            cuda_tensor.set(tensor_array, place)
            cuda_tensor_array_2 = np.array(cuda_tensor)
            self.assertAlmostEqual(
                cuda_tensor_array_2.all(), tensor_array.all()
            )

    def test_complex128_tensor(self):
        scope = core.Scope()
        var = scope.var("complex128_tensor")
        cpu_tensor = var.get_tensor()
        tensor_array = (
            np.random.uniform(-1, 1, (100, 200))
            + 1j * np.random.uniform(-1, 1, (100, 200))
        ).astype(np.complex128)
        place = core.CPUPlace()
        cpu_tensor.set(tensor_array, place)
        cpu_tensor_array_2 = np.array(cpu_tensor)
        self.assertAlmostEqual(cpu_tensor_array_2.all(), tensor_array.all())

        if core.is_compiled_with_cuda() or is_custom_device():
            cuda_tensor = var.get_tensor()
            tensor_array = (
                np.random.uniform(-1, 1, (100, 200))
                + 1j * np.random.uniform(-1, 1, (100, 200))
            ).astype(np.complex128)
            place = get_device_place()
            cuda_tensor.set(tensor_array, place)
            cuda_tensor_array_2 = np.array(cuda_tensor)
            self.assertAlmostEqual(
                cuda_tensor_array_2.all(), tensor_array.all()
            )

    def test_int_lod_tensor(self):
        place = core.CPUPlace()
        scope = core.Scope()
        var_lod = scope.var("test_lod_tensor")
        lod_tensor = var_lod.get_tensor()

        lod_tensor._set_dims([4, 4, 6])
        lod_tensor._alloc_int(place)
        array = np.array(lod_tensor)
        array[0, 0, 0] = 3
        array[3, 3, 5] = 10
        lod_tensor.set(array, place)

        lod_v = np.array(lod_tensor)
        self.assertTrue(np.all(array == lod_v))

    def test_float_lod_tensor(self):
        place = core.CPUPlace()
        scope = core.Scope()
        var_lod = scope.var("test_lod_tensor")

        lod_tensor = var_lod.get_tensor()
        lod_tensor._set_dims([5, 2, 3, 4])
        lod_tensor._alloc_float(place)

        tensor_array = np.array(lod_tensor)
        self.assertEqual((5, 2, 3, 4), tensor_array.shape)
        tensor_array[0, 0, 0, 0] = 1.0
        tensor_array[0, 0, 0, 1] = 2.0
        lod_tensor.set(tensor_array, place)

        lod_v = np.array(lod_tensor)
        self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
        self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])

    def test_empty_tensor(self):
        place = core.CPUPlace()
        scope = core.Scope()
        var = scope.var("test_tensor")

        tensor = var.get_tensor()
        tensor._set_dims([0, 1])
        tensor._alloc_float(place)

        tensor_array = np.array(tensor)
        self.assertEqual((0, 1), tensor_array.shape)

        if core.is_compiled_with_cuda() or is_custom_device():
            gpu_place = get_device_place()
            tensor._alloc_float(gpu_place)
            tensor_array = np.array(tensor)
            self.assertEqual((0, 1), tensor_array.shape)

    def run_slice_tensor(self, place, dtype):
        tensor = base.Tensor()
        shape = [3, 3, 3]
        tensor._set_dims(shape)

        tensor_array = np.array(
            [
                [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
                [[19, 20, 21], [22, 23, 24], [25, 26, 27]],
            ]
        ).astype(dtype)

        tensor.set(tensor_array, place)
        n1 = tensor[1]
        t1 = tensor_array[1]
        self.assertTrue((np.array(n1) == np.array(t1)).all())

        n2 = tensor[1:]
        t2 = tensor_array[1:]
        self.assertTrue((np.array(n2) == np.array(t2)).all())

        n3 = tensor[0:2:]
        t3 = tensor_array[0:2:]
        self.assertTrue((np.array(n3) == np.array(t3)).all())

        n4 = tensor[2::-2]
        t4 = tensor_array[2::-2]
        self.assertTrue((np.array(n4) == np.array(t4)).all())

        n5 = tensor[2::-2][0]
        t5 = tensor_array[2::-2][0]
        self.assertTrue((np.array(n5) == np.array(t5)).all())

        n6 = tensor[2:-1:-1]
        t6 = tensor_array[2:-1:-1]
        self.assertTrue((np.array(n6) == np.array(t6)).all())

        n7 = tensor[0:, 0:]
        t7 = tensor_array[0:, 0:]
        self.assertTrue((np.array(n7) == np.array(t7)).all())

        n8 = tensor[0::1, 0::-1, 2:]
        t8 = tensor_array[0::1, 0::-1, 2:]
        self.assertTrue((np.array(n8) == np.array(t8)).all())

    def test_slice_tensor(self):
        for dtype in self.support_dtypes:
            # run cpu first
            place = core.CPUPlace()
            self.run_slice_tensor(place, dtype)

            if core.is_compiled_with_cuda() or is_custom_device():
                place = get_device_place()
                self.run_slice_tensor(place, dtype)

    def test_print_tensor(self):
        scope = core.Scope()
        var = scope.var("test_tensor")
        place = core.CPUPlace()
        tensor = var.get_tensor()
        tensor._set_dims([10, 10])
        tensor._alloc_int(place)
        tensor_array = np.array(tensor)
        self.assertEqual((10, 10), tensor_array.shape)
        tensor_array[0, 0] = 1
        tensor_array[2, 2] = 2
        tensor.set(tensor_array, place)
        print(tensor)
        self.assertTrue(isinstance(str(tensor), str))

        if core.is_compiled_with_cuda() or is_custom_device():
            tensor.set(tensor_array, get_device_place())
            print(tensor)
            self.assertTrue(isinstance(str(tensor), str))

    def test_tensor_pointer(self):
        place = core.CPUPlace()
        scope = core.Scope()
        var = scope.var("test_tensor")
        place = core.CPUPlace()
        tensor = var.get_tensor()
        dtype = paddle.float32
        if paddle.framework.use_pir_api() and isinstance(
            dtype, paddle.base.core.DataType
        ):
            dtype = paddle.pir.core.datatype_to_vartype[dtype]
        self.assertTrue(
            isinstance(tensor._mutable_data(place, dtype), numbers.Integral)
        )

        if core.is_compiled_with_cuda():
            place = get_device_place()
            self.assertTrue(
                isinstance(tensor._mutable_data(place, dtype), numbers.Integral)
            )
            place = core.CUDAPinnedPlace()
            self.assertTrue(
                isinstance(tensor._mutable_data(place, dtype), numbers.Integral)
            )
            places = base.cuda_pinned_places()
            self.assertTrue(
                isinstance(
                    tensor._mutable_data(places[0], dtype), numbers.Integral
                )
            )
        elif is_custom_device():
            place = get_device_place()
            self.assertTrue(
                isinstance(tensor._mutable_data(place, dtype), numbers.Integral)
            )

    def test_tensor_set_fp16(self):
        array = np.random.random((300, 500)).astype("float16")
        tensor = base.Tensor()
        place = core.CPUPlace()
        tensor.set(array, place)
        tensor_dtype = tensor._dtype()
        if paddle.framework.use_pir_api() and isinstance(
            tensor_dtype, paddle.base.libpaddle.VarDesc.VarType
        ):
            tensor_dtype = paddle.pir.core.vartype_to_datatype[tensor_dtype]
        self.assertEqual(tensor_dtype, paddle.float16)
        np.testing.assert_array_equal(np.array(tensor), array)

        if core.is_compiled_with_cuda():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.float16)
            np.testing.assert_array_equal(np.array(tensor), array)

            place = core.CUDAPinnedPlace()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.float16)
            np.testing.assert_array_equal(np.array(tensor), array)
        elif is_custom_device():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.float16)
            np.testing.assert_array_equal(np.array(tensor), array)

    def test_tensor_set_int16(self):
        array = np.random.randint(100, size=(300, 500)).astype("int16")
        tensor = base.Tensor()
        place = core.CPUPlace()
        tensor.set(array, place)
        tensor_dtype = tensor._dtype()
        if paddle.framework.use_pir_api() and isinstance(
            tensor_dtype, paddle.base.libpaddle.VarDesc.VarType
        ):
            tensor_dtype = paddle.pir.core.vartype_to_datatype[tensor_dtype]
        self.assertEqual(tensor_dtype, paddle.int16)
        np.testing.assert_array_equal(np.array(tensor), array)

        if core.is_compiled_with_cuda():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.int16)
            np.testing.assert_array_equal(np.array(tensor), array)

            place = core.CUDAPinnedPlace()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.int16)
            np.testing.assert_array_equal(np.array(tensor), array)
        elif is_custom_device():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.int16)
            np.testing.assert_array_equal(np.array(tensor), array)

    def test_tensor_set_from_array_list(self):
        array = np.random.randint(1000, size=(200, 300))
        list_array = [array, array]
        tensor = base.Tensor()
        place = core.CPUPlace()
        tensor.set(list_array, place)
        self.assertEqual([2, 200, 300], tensor.shape())
        np.testing.assert_array_equal(np.array(tensor), list_array)

        if core.is_compiled_with_cuda():
            place = get_device_place()
            tensor.set(list_array, place)
            self.assertEqual([2, 200, 300], tensor.shape())
            np.testing.assert_array_equal(np.array(tensor), list_array)

            place = core.CUDAPinnedPlace()
            tensor.set(list_array, place)
            self.assertEqual([2, 200, 300], tensor.shape())
            np.testing.assert_array_equal(np.array(tensor), list_array)
        elif is_custom_device():
            place = get_device_place()
            tensor.set(list_array, place)
            self.assertEqual([2, 200, 300], tensor.shape())
            np.testing.assert_array_equal(np.array(tensor), list_array)

    def test_tensor_set_error(self):
        scope = core.Scope()
        var = scope.var("test_tensor")
        place = core.CPUPlace()

        tensor = var.get_tensor()

        exception = None
        try:
            error_array = ["1", "2"]
            tensor.set(error_array, place)
        except ValueError as ex:
            exception = ex

        self.assertIsNotNone(exception)

    def test_tensor_set_item_complex128(self):
        array = (
            np.random.random((100, 100)) + 1j * np.random.random((100, 100))
        ).astype(np.complex128)
        tensor = base.Tensor()
        place = core.CPUPlace()
        tensor.set(array, place)
        tensor_dtype = tensor._dtype()
        if paddle.framework.use_pir_api() and isinstance(
            tensor_dtype, paddle.base.libpaddle.VarDesc.VarType
        ):
            tensor_dtype = paddle.pir.core.vartype_to_datatype[tensor_dtype]
        self.assertEqual(tensor_dtype, paddle.complex128)
        tensor._set_complex128_element(0, 42.1 + 42.1j)
        np.testing.assert_allclose(
            tensor._get_complex128_element(0), 42.1 + 42.1j
        )

        if core.is_compiled_with_cuda():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.complex128)
            tensor._set_complex128_element(0, 42.1 + 42.1j)
            np.testing.assert_allclose(
                tensor._get_complex128_element(0), 42.1 + 42.1j
            )

            place = core.CUDAPinnedPlace()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.complex128)
            tensor._set_complex128_element(0, 42.1 + 42.1j)
            np.testing.assert_allclose(
                tensor._get_complex128_element(0), 42.1 + 42.1j
            )
        elif is_custom_device():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.complex128)
            tensor._set_complex128_element(0, 42.1 + 42.1j)
            np.testing.assert_allclose(
                tensor._get_complex128_element(0), 42.1 + 42.1j
            )

    def test_tensor_set_item_complex64(self):
        array = (
            np.random.random((100, 100)) + 1j * np.random.random((100, 100))
        ).astype(np.complex64)
        tensor = base.Tensor()
        place = core.CPUPlace()
        tensor.set(array, place)
        tensor_dtype = tensor._dtype()
        if paddle.framework.use_pir_api() and isinstance(
            tensor_dtype, paddle.base.libpaddle.VarDesc.VarType
        ):
            tensor_dtype = paddle.pir.core.vartype_to_datatype[tensor_dtype]
        self.assertEqual(tensor_dtype, paddle.complex64)
        tensor._set_complex64_element(0, 42.1 + 42.1j)
        np.testing.assert_allclose(
            np.complex64(tensor._get_complex64_element(0)),
            np.complex64(42.1 + 42.1j),
        )

        if core.is_compiled_with_cuda():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.complex64)
            tensor._set_complex64_element(0, 42.1 + 42.1j)
            np.testing.assert_allclose(
                np.complex64(tensor._get_complex64_element(0)),
                np.complex64(42.1 + 42.1j),
            )

            place = core.CUDAPinnedPlace()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.complex64)
            tensor._set_complex64_element(0, 42.1 + 42.1j)
            np.testing.assert_allclose(
                np.complex64(tensor._get_complex64_element(0)),
                np.complex64(42.1 + 42.1j),
            )
        elif is_custom_device():
            place = get_device_place()
            tensor.set(array, place)
            self.assertEqual(tensor_dtype, paddle.complex64)
            tensor._set_complex64_element(0, 42.1 + 42.1j)
            np.testing.assert_allclose(
                np.complex64(tensor._get_complex64_element(0)),
                np.complex64(42.1 + 42.1j),
            )


class TestTensorDataSetter(unittest.TestCase):
    def setUp(self):
        paddle.disable_static()

    def test_same_shape_same_dtype(self):
        x = paddle.tensor([[1, 2], [3, 4]], dtype="float32")
        y = paddle.rand_like(x)
        x.requires_grad = True

        loss = x.sum()
        loss.backward(retain_graph=True)

        x_grad_expected = paddle.ones_like(x)

        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())

        x.data = y

        self.assertEqual(x.data_ptr(), y.data_ptr())
        np.testing.assert_allclose(x.numpy(), y.numpy())
        self.assertEqual(
            x.requires_grad,
            True,
            "x's requires_grad should be True after data setting.",
        )

        loss.backward()
        x_grad_expected = paddle.ones_like(x) * 2

        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())

    def test_new_shape_same_dtype_same_place(self):
        x: paddle.Tensor = paddle.tensor([[1, 2], [3, 4]], dtype="float32")
        y = paddle.rand([3, 4, 5], dtype="float32")
        x.requires_grad = True

        loss = x.sum()
        loss.backward()

        x_grad_expected = paddle.ones_like(x)

        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())
        assert x.grad.dtype == x_grad_expected.dtype

        x.data = y

        self.assertEqual(x.data_ptr(), y.data_ptr())
        np.testing.assert_allclose(x.numpy(), y.numpy())
        self.assertEqual(
            x.requires_grad,
            True,
            "x's requires_grad should be True after data setting.",
        )
        with self.assertRaises((ValueError, RuntimeError)):
            loss = x.sum()
            loss.backward()

        x.clear_gradient()
        z = x.sum()
        z.backward()
        x_grad_expected = paddle.ones_like(x)
        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())
        assert x.grad.dtype == x_grad_expected.dtype

    def test_same_shape_new_dtype_same_place(self):
        x = paddle.tensor([[1, 2], [3, 4]], dtype="float32")
        x_grad_expected = paddle.ones_like(x)
        y = x.to(paddle.float16)
        x.requires_grad = True

        loss = x.sum()

        x.data = y

        loss.backward()
        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())
        assert x.grad.dtype == x_grad_expected.dtype

        x.clear_gradient(False)
        assert x.grad is None
        z = x.sum()
        z.backward()
        x_grad_expected = paddle.ones_like(x)
        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())
        assert x.dtype == x.grad.dtype

    def test_same_shape_same_dtype_new_place(self):
        if not paddle.device.is_compiled_with_cuda():
            return
        x = paddle.tensor([[1, 2], [3, 4]], dtype="float32").cuda()
        y = x.cpu()
        x_grad_expected = paddle.ones_like(x)
        x.requires_grad = True

        loss = x.sum()
        x.data = y.data

        self.assertEqual(x.data_ptr(), y.data_ptr())
        np.testing.assert_allclose(x.numpy(), y.numpy())
        self.assertEqual(
            x.requires_grad,
            True,
            "x's requires_grad should be True after data setting.",
        )
        loss.backward()
        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())
        assert x.grad.dtype == x_grad_expected.dtype
        assert x.grad.place == x_grad_expected.place

        x.clear_gradient(False)
        assert x.grad is None
        loss = x.sum()
        loss.backward()
        x_grad_expected = paddle.ones_like(x)
        np.testing.assert_equal(x.grad.numpy(), x_grad_expected.numpy())
        assert x.grad.place == x.place


class TestTensorNewSharedTensor(unittest.TestCase):
    def test_shared_data(self):
        x = paddle.to_tensor([1.0, 2.0, 3.0])
        y = x._new_shared_tensor()
        x[0] = 88.88
        # Test that they share the same data
        self.assertEqual(y.shape, x.shape)
        self.assertEqual(y.dtype, x.dtype)
        self.assertEqual(y.place, x.place)
        np.testing.assert_allclose(y.numpy(), x.numpy())

    def test_new_shared_tensor_retain_holder_true(self):
        """Test _new_shared_tensor with retain_holder=True (default)"""
        x = paddle.to_tensor([1.0, 2.0, 3.0], stop_gradient=False)
        y = x._new_shared_tensor(retain_holder=True)

        # Test that they share the same data
        self.assertEqual(y.shape, x.shape)
        self.assertEqual(y.dtype, x.dtype)
        self.assertEqual(y.place, x.place)
        np.testing.assert_allclose(y.numpy(), x.numpy())

        # Test autograd metadata sharing
        self.assertEqual(y.stop_gradient, x.stop_gradient)

        # Test gradient sharing after backward
        loss = x.sum() + y.sum()
        loss.backward()
        self.assertIsNotNone(x.grad)
        self.assertIsNotNone(y.grad)
        np.testing.assert_allclose(x.grad.numpy(), y.grad.numpy())

        self.assertEqual(id(x.grad), id(y.grad))
        # Test data sharing - modification affects both

    def test_new_shared_tensor_retain_holder_false(self):
        """Test _new_shared_tensor with retain_holder=False"""
        x = paddle.to_tensor([1.0, 2.0, 3.0], stop_gradient=False)
        z = x._new_shared_tensor(retain_holder=False)

        # Test metadata is the same
        self.assertEqual(z.shape, x.shape)
        self.assertEqual(z.dtype, x.dtype)
        self.assertEqual(z.place, x.place)

        # Test that new tensor has empty data allocation
        # It should be uninitialized or have default values
        self.assertEqual(z.stop_gradient, x.stop_gradient)

        # Test autograd metadata is still shared
        loss = x.sum()
        loss.backward()
        self.assertIsNotNone(z.grad)

    def test_new_shared_tensor_default_behavior(self):
        """Test _new_shared_tensor with default parameters"""
        x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], stop_gradient=False)
        y = x._new_shared_tensor()  # default retain_holder=True

        # Test gradient calculation
        loss = (x + y).sum()
        loss.backward()

        np.testing.assert_allclose(x.grad.numpy(), y.grad.numpy())

    def test_new_shared_tensor_uninitialized_error(self):
        """Test error when original tensor is not initialized"""
        x = paddle.Tensor()
        x._clear_dataptr()  # Ensure Tensor is uninitialized tensor
        with self.assertRaises(ValueError):
            x._new_shared_tensor()


if __name__ == '__main__':
    unittest.main()