#   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 unittest

import gradient_checker
import numpy as np
from decorator_helper import prog_scope
from op_test import (
    OpTest,
    check_cudnn_version_and_compute_capability,
    convert_float_to_uint16,
    get_device_place,
    get_places,
    is_custom_device,
)
from utils import dygraph_guard, static_guard

import paddle
from paddle import base

paddle.enable_static()


class TestTransposeOp(OpTest):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        self.prim_op_type = "prim"
        self.inputs = {'X': np.random.random(self.shape).astype("float64")}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("float64"),
            'Out': self.inputs['X'].transpose(self.axis),
        }
        self.if_enable_cinn()

    def init_op_type(self):
        self.op_type = "transpose2"
        self.use_onednn = False

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'], check_pir=True)

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            check_prim=True,
            check_pir=True,
            check_prim_pir=True,
        )

    def if_enable_cinn(self):
        pass

    def initTestCase(self):
        self.shape = (3, 40)
        self.axis = (1, 0)


class TestTransposeOp_ZeroDim(TestTransposeOp):
    def initTestCase(self):
        self.shape = ()
        self.axis = ()

    def if_enable_cinn(self):
        self.enable_cinn = False


class TestCase0(TestTransposeOp):
    def initTestCase(self):
        self.shape = (100,)
        self.axis = (0,)


class TestCase1(TestTransposeOp):
    def initTestCase(self):
        self.shape = (3, 4, 10)
        self.axis = (0, 2, 1)


class TestCase2(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 3, 4, 5)
        self.axis = (0, 2, 3, 1)


class TestCase3(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 3, 4, 5, 6)
        self.axis = (4, 2, 3, 1, 0)


class TestCase4(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 3, 4, 5, 6, 1)
        self.axis = (4, 2, 3, 1, 0, 5)


class TestCase5(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 16, 96)
        self.axis = (0, 2, 1)


class TestCase6(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 10, 12, 16)
        self.axis = (3, 1, 2, 0)


class TestCase7(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 10, 2, 16)
        self.axis = (0, 1, 3, 2)


class TestCase8(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
        self.axis = (0, 1, 3, 2, 4, 5, 6, 7)


class TestCase9(TestTransposeOp):
    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
        self.axis = (6, 1, 3, 5, 0, 2, 4, 7)


class TestCase10(TestTransposeOp):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        self.prim_op_type = "prim"
        self.inputs = {'X': np.random.random(self.shape).astype("float64")}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("float64"),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def initTestCase(self):
        self.shape = (10, 8, 2)
        self.axis = (-1, 1, -3)


class TestCase_ZeroDim(TestTransposeOp):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        self.prim_op_type = "prim"
        self.enable_cinn = False
        self.inputs = {'X': np.random.random(self.shape).astype("float64")}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("float64"),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def initTestCase(self):
        self.shape = ()
        self.axis = ()


class TestAutoTuneTransposeOp(OpTest):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        self.prim_op_type = "prim"
        self.inputs = {'X': np.random.random(self.shape).astype("float64")}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("float64"),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def initTestCase(self):
        base.core.set_autotune_range(0, 3)
        base.core.update_autotune_status()
        base.core.enable_autotune()
        self.shape = (1, 12, 256, 1)
        self.axis = (0, 3, 2, 1)

    def init_op_type(self):
        self.op_type = "transpose2"
        self.use_onednn = False

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'], check_pir=True)
        base.core.disable_autotune()

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            check_prim=True,
            check_prim_pir=True,
            check_pir=True,
        )


@unittest.skipIf(
    not check_cudnn_version_and_compute_capability(min_device_capability=9.0),
    "core is not compiled with CUDA or not support native fp8",
)
class TestFP8FastTranspose(unittest.TestCase):
    def setUp(self):
        self.dtype = paddle.float8_e4m3fn
        self.test_cases = [
            {"shape": (7168, 16384), "perm": [1, 0], "name": "2D(7168,16384)"},
            {
                "shape": (8, 7168, 4096),
                "perm": [0, 2, 1],
                "name": "3D(8,7168,4096)",
            },
            {
                "shape": (8, 2048, 7168),
                "perm": [0, 2, 1],
                "name": "3D(8,2048,7168)",
            },
        ]

    def test_verify_transpose(self):
        paddle.disable_static()
        with paddle.no_grad():
            for case in self.test_cases:
                x = paddle.randn(case["shape"]).cast(self.dtype)
                np_data = x.numpy()
                gold = np.transpose(np_data, case["perm"])
                out = paddle.transpose(x, case["perm"]).contiguous()
                np.testing.assert_equal(out.numpy(), gold)
        paddle.enable_static()


class TestAutoTuneTransposeFP16Op(OpTest):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.dtype = np.float16
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        self.prim_op_type = "prim"
        self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': np.random.random(self.shape).astype(self.dtype),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def initTestCase(self):
        base.core.set_autotune_range(0, 3)
        base.core.update_autotune_status()
        base.core.enable_autotune()
        self.shape = (1, 12, 256, 1)
        self.axis = (0, 3, 2, 1)

    def init_op_type(self):
        self.op_type = "transpose2"
        self.use_onednn = False

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'], check_pir=True)
        base.core.disable_autotune()

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            check_prim=True,
            check_prim_pir=True,
            check_pir=True,
        )


class TestAutoTuneTransposeBF16Op(OpTest):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.dtype = np.uint16
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        self.prim_op_type = "prim"
        self.if_enable_cinn()
        x = np.random.random(self.shape).astype("float32")
        self.inputs = {'X': convert_float_to_uint16(x)}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': convert_float_to_uint16(
                np.random.random(self.shape).astype("float32")
            ),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def if_enable_cinn(self):
        self.enable_cinn = False

    def initTestCase(self):
        base.core.set_autotune_range(0, 3)
        base.core.update_autotune_status()
        base.core.enable_autotune()
        self.shape = (2, 8, 10)
        self.axis = (0, 2, 1)

    def init_op_type(self):
        self.op_type = "transpose2"
        self.use_onednn = False

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'], check_pir=True)
        base.core.disable_autotune()

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            check_prim=True,
            check_prim_pir=True,
            check_pir=True,
        )


class TestTransposeFP16Op(OpTest):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.dtype = np.float16
        self.prim_op_type = "prim"
        self.if_enable_cinn()
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        x = np.random.random(self.shape).astype(self.dtype)

        self.inputs = {'X': x}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': np.random.random(self.shape).astype(self.dtype),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def if_enable_cinn(self):
        pass

    def init_op_type(self):
        self.op_type = "transpose2"
        self.use_onednn = False

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'], check_pir=True)

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            check_prim=True,
            check_prim_pir=True,
            check_pir=True,
        )

    def initTestCase(self):
        self.shape = (3, 40)
        self.axis = (1, 0)


class TestTransposeBF16Op(OpTest):
    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.dtype = np.uint16
        self.prim_op_type = "prim"
        self.enable_cinn = False
        self.python_api = paddle.transpose
        self.public_python_api = paddle.transpose
        x = np.random.random(self.shape).astype("float32")
        self.if_enable_cinn()

        self.inputs = {'X': convert_float_to_uint16(x)}
        self.attrs = {
            'axis': list(self.axis),
            'use_onednn': self.use_onednn,
        }
        self.outputs = {
            'XShape': convert_float_to_uint16(
                np.random.random(self.shape).astype("float32")
            ),
            'Out': self.inputs['X'].transpose(self.axis),
        }

    def if_enable_cinn(self):
        self.enable_cinn = False

    def init_op_type(self):
        self.op_type = "transpose2"
        self.use_onednn = False

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'], check_pir=True)

    def test_check_grad(self):
        pass

    def initTestCase(self):
        self.shape = (3, 2)
        self.axis = (1, 0)


class TestTransposeOpBool(TestTransposeOp):
    def test_check_grad(self):
        pass


class TestTransposeOpBool1D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (100,)
        self.axis = (0,)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool2D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (3, 40)
        self.axis = (1, 0)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool3D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (3, 4, 10)
        self.axis = (0, 2, 1)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool4D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (2, 3, 4, 5)
        self.axis = (0, 2, 3, 1)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool5D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (2, 3, 4, 5, 6)
        self.axis = (4, 2, 3, 1, 0)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool6D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (2, 3, 4, 5, 6, 1)
        self.axis = (4, 2, 3, 1, 0, 5)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool7D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3)
        self.axis = (0, 1, 3, 2, 4, 5, 6)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpBool8D(TestTransposeOpBool):
    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
        self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis),
        }


class TestTransposeOpError(unittest.TestCase):
    def test_errors(self):
        paddle.enable_static()
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(
                name='x', shape=[-1, 10, 5, 3], dtype='float64'
            )

            def test_x_Variable_check():
                # the Input(x)'s type must be Variable
                paddle.transpose("not_variable", perm=[1, 0, 2])

            self.assertRaises(TypeError, test_x_Variable_check)

            def test_perm_list_check():
                # Input(perm)'s type must be list
                paddle.transpose(x, perm="[1, 0, 2]")

            self.assertRaises(TypeError, test_perm_list_check)

            def test_perm_length_and_x_dim_check():
                # Input(perm) is the permutation of dimensions of Input(input)
                # its length should be equal to dimensions of Input(input)
                paddle.transpose(x, perm=[1, 0, 2, 3, 4])

            self.assertRaises(ValueError, test_perm_length_and_x_dim_check)

            def test_each_elem_value_check():
                # Each element in Input(perm) should be less than Input(x)'s dimension
                paddle.transpose(x, perm=[3, 5, 7])

            self.assertRaises(ValueError, test_each_elem_value_check)


class TestTransposeApi(unittest.TestCase):
    def test_static_out(self):
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(name='x', shape=[2, 3, 4], dtype='float32')
            x_trans1 = paddle.transpose(x, perm=[1, 0, 2])
            x_trans2 = paddle.transpose(x, perm=(2, 1, 0))
            place = paddle.CPUPlace()
            exe = paddle.static.Executor(place)
            x_np = np.random.random([2, 3, 4]).astype("float32")
            result1, result2 = exe.run(
                feed={"x": x_np}, fetch_list=[x_trans1, x_trans2]
            )
            expected_result1 = np.transpose(x_np, [1, 0, 2])
            expected_result2 = np.transpose(x_np, (2, 1, 0))

            np.testing.assert_array_equal(result1, expected_result1)
            np.testing.assert_array_equal(result2, expected_result2)

    def test_dygraph_out(self):
        # This is an old test before 2.0 API so we need to disable static
        # to trigger dygraph
        paddle.disable_static()
        x = paddle.randn([2, 3, 4])
        x_trans1 = paddle.transpose(x, perm=[1, 0, 2])
        x_trans2 = paddle.transpose(x, perm=(2, 1, 0))
        x_np = x.numpy()
        expected_result1 = np.transpose(x_np, [1, 0, 2])
        expected_result2 = np.transpose(x_np, (2, 1, 0))

        np.testing.assert_array_equal(x_trans1.numpy(), expected_result1)
        np.testing.assert_array_equal(x_trans2.numpy(), expected_result2)
        # This is an old test before 2.0 API so we enable static again after
        # dygraph test
        paddle.enable_static()


class TestTAPI(unittest.TestCase):
    def test_static_out(self):
        with base.program_guard(base.Program()):
            data = paddle.static.data(shape=[10], dtype="float64", name="data")
            data_t = paddle.t(data)
            place = base.CPUPlace()
            exe = base.Executor(place)
            data_np = np.random.random([10]).astype("float64")
            (result,) = exe.run(feed={"data": data_np}, fetch_list=[data_t])
            expected_result = np.transpose(data_np)
        self.assertEqual((result == expected_result).all(), True)

        with base.program_guard(base.Program()):
            data = paddle.static.data(
                shape=[10, 5], dtype="float64", name="data"
            )
            data_t = paddle.t(data)
            place = base.CPUPlace()
            exe = base.Executor(place)
            data_np = np.random.random([10, 5]).astype("float64")
            (result,) = exe.run(feed={"data": data_np}, fetch_list=[data_t])
            expected_result = np.transpose(data_np)
        self.assertEqual((result == expected_result).all(), True)

        with base.program_guard(base.Program()):
            data = paddle.static.data(
                shape=[1, 5], dtype="float64", name="data"
            )
            data_t = paddle.t(data)
            place = base.CPUPlace()
            exe = base.Executor(place)
            data_np = np.random.random([1, 5]).astype("float64")
            (result,) = exe.run(feed={"data": data_np}, fetch_list=[data_t])
            expected_result = np.transpose(data_np)
        self.assertEqual((result == expected_result).all(), True)

    def test_dygraph_out(self):
        with base.dygraph.guard():
            np_x = np.random.random([10]).astype("float64")
            data = paddle.to_tensor(np_x)
            z = paddle.t(data)
            np_z = z.numpy()
            z_expected = np.array(np.transpose(np_x))
        self.assertEqual((np_z == z_expected).all(), True)

        with base.dygraph.guard():
            np_x = np.random.random([10, 5]).astype("float64")
            data = paddle.to_tensor(np_x)
            z = paddle.t(data)
            np_z = z.numpy()
            z_expected = np.array(np.transpose(np_x))
        self.assertEqual((np_z == z_expected).all(), True)

        with base.dygraph.guard():
            np_x = np.random.random([1, 5]).astype("float64")
            data = paddle.to_tensor(np_x)
            z = paddle.t(data)
            np_z = z.numpy()
            z_expected = np.array(np.transpose(np_x))
        self.assertEqual((np_z == z_expected).all(), True)

    def test_errors(self):
        with base.program_guard(base.Program()):
            x = paddle.static.data(name='x', shape=[10, 5, 3], dtype='float64')

            def test_x_dimension_check():
                paddle.t(x)

            self.assertRaises(ValueError, test_x_dimension_check)


class TestMoveAxis(unittest.TestCase):
    def test_static_moveaxis1(self):
        x_np = np.random.randn(2, 3, 4, 5, 7)
        expected = np.moveaxis(x_np, [0, 4, 3, 2], [1, 3, 2, 0])
        paddle.enable_static()
        with paddle.static.program_guard(base.Program()):
            x = paddle.static.data("x", shape=[2, 3, 4, 5, 7], dtype='float64')
            out = paddle.moveaxis(x, [0, 4, 3, 2], [1, 3, 2, 0])

            exe = paddle.static.Executor()
            out_np = exe.run(feed={"x": x_np}, fetch_list=[out])[0]

        np.testing.assert_array_equal(out_np, expected)

    def test_dygraph_moveaxis1(self):
        x_np = np.random.randn(2, 3, 4, 5, 7)
        expected = np.moveaxis(x_np, [0, 4, 3, 2], [1, 3, 2, 0])
        paddle.disable_static()
        x = paddle.to_tensor(x_np)
        out = paddle.moveaxis(x, [0, 4, 3, 2], [1, 3, 2, 0])
        self.assertEqual(out.shape, [4, 2, 5, 7, 3])
        np.testing.assert_array_equal(out.numpy(), expected)
        paddle.enable_static()

    def test_static_moveaxis2(self):
        x_np = np.random.randn(2, 3, 5)
        expected = np.moveaxis(x_np, -2, -1)
        paddle.enable_static()
        with paddle.static.program_guard(base.Program()):
            x = paddle.static.data("x", shape=[2, 3, 5], dtype='float64')
            out = x.moveaxis(-2, -1)

            exe = paddle.static.Executor()
            out_np = exe.run(feed={"x": x_np}, fetch_list=[out])[0]

        np.testing.assert_array_equal(out_np, expected)

    def test_dygraph_moveaxis2(self):
        x_np = np.random.randn(2, 3, 5)
        expected = np.moveaxis(x_np, -2, -1)
        paddle.disable_static()
        x = paddle.to_tensor(x_np)
        out = x.moveaxis(-2, -1)
        self.assertEqual(out.shape, [2, 5, 3])
        np.testing.assert_array_equal(out.numpy(), expected)
        paddle.enable_static()

    def test_moveaxis3(self):
        paddle.disable_static()
        x = paddle.to_tensor(
            [[1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j]]
        )
        out = x.moveaxis(0, 1)
        self.assertEqual(out.shape, [2, 3])
        paddle.enable_static()

    def test_moveaxis_alias_movedim(self):
        x_np = np.random.randn(2, 3, 5)
        expected = np.moveaxis(x_np, -2, -1)
        paddle.disable_static()
        # tensor method
        x = paddle.to_tensor(x_np)
        out = x.movedim(-2, -1)
        self.assertEqual(out.shape, [2, 5, 3])
        # paddle method
        out = paddle.movedim(x, -2, -1)
        self.assertEqual(out.shape, [2, 5, 3])
        # arg alias
        out = paddle.movedim(input=x, source=-2, destination=-1)
        self.assertEqual(out.shape, [2, 5, 3])
        np.testing.assert_array_equal(out.numpy(), expected)
        paddle.enable_static()

    def test_error(self):
        x = paddle.randn([2, 3, 4, 5])
        # src must have the same number with dst
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [1, 0], [2])

        # each element of src must be unique
        with self.assertRaises(ValueError):
            paddle.moveaxis(x, [1, 1], [0, 2])

        # each element of dst must be unique
        with self.assertRaises(ValueError):
            paddle.moveaxis(x, [0, 1], [2, 2])

        # each element of src must be integer
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [0.5], [1])

        # each element of dst must be integer
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [0], [1.5])

        # each element of src must be in the range of [-4, 3)
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [-10, 1], [2, 3])

        # each element of dst must be in the range of [-4, 3)
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [2, 1], [10, 3])


class TestTransposeDoubleGradCheck(unittest.TestCase):
    def transpose_wrapper(self, x):
        return paddle.transpose(x[0], [1, 0, 2])

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not include -1.
        eps = 0.005
        dtype = np.float32

        data = paddle.static.data('data', [2, 3, 4], dtype)
        data.persistable = True
        out = paddle.transpose(data, [1, 0, 2])
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.double_grad_check(
            [data], out, x_init=[data_arr], place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.transpose_wrapper, [data], out, x_init=[data_arr], place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestTransposeTripleGradCheck(unittest.TestCase):
    def transpose_wrapper(self, x):
        return paddle.transpose(x[0], [1, 0, 2])

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not include -1.
        eps = 0.005
        dtype = np.float32

        data = paddle.static.data('data', [2, 3, 4], dtype)
        data.persistable = True
        out = paddle.transpose(data, [1, 0, 2])
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.triple_grad_check(
            [data], out, x_init=[data_arr], place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.transpose_wrapper, [data], out, x_init=[data_arr], place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestTransposeAPI_ZeroDim(unittest.TestCase):
    def test_dygraph(self):
        paddle.disable_static()

        x = paddle.rand([])
        x.stop_gradient = False
        out = paddle.transpose(x, [])
        if hasattr(out, 'retain_grads'):
            out.retain_grads()
        out.backward()

        self.assertEqual(out.shape, [])
        self.assertEqual(x.grad.shape, [])
        self.assertEqual(out.grad.shape, [])

        paddle.enable_static()


class TestMatrixTransposeApi(unittest.TestCase):
    def test_static_out(self):
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(name='x', shape=[2, 3, 4], dtype='float32')
            x_trans1 = paddle.matrix_transpose(x)
            x_trans2 = paddle.linalg.matrix_transpose(x)
            place = paddle.CPUPlace()
            exe = paddle.static.Executor(place)
            x_np = np.random.random([2, 3, 4]).astype("float32")
            result1, result2 = exe.run(
                feed={"x": x_np}, fetch_list=[x_trans1, x_trans2]
            )
            expected_result = np.transpose(x_np, (0, 2, 1))

            np.testing.assert_array_equal(result1, expected_result)
            np.testing.assert_array_equal(result2, expected_result)

    def test_dygraph_out(self):
        paddle.disable_static()
        x = paddle.randn([2, 3, 4])
        x_trans1 = paddle.matrix_transpose(x)
        x_trans2 = paddle.linalg.matrix_transpose(x)
        x_np = x.numpy()
        expected_result = np.transpose(x_np, (0, 2, 1))

        np.testing.assert_array_equal(x_trans1.numpy(), expected_result)
        np.testing.assert_array_equal(x_trans2.numpy(), expected_result)
        paddle.enable_static()


class TestMatrixTransposeAPI_ZeroDim(unittest.TestCase):
    def test_zero_dim_error(self):
        paddle.disable_static()
        x = paddle.rand([])
        with self.assertRaises(ValueError) as context:
            out = paddle.matrix_transpose(x)
        self.assertIn(
            "Tensor.ndim(0) is required to be greater than or equal to 2",
            str(context.exception),
        )
        paddle.enable_static()


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

    def check_dtype_transpose(self, dtype):
        x_np = np.random.random([2, 3, 4]).astype(dtype)
        x = paddle.to_tensor(x_np)
        out = paddle.matrix_transpose(x)
        expected_result = np.transpose(x_np, (0, 2, 1))
        np.testing.assert_array_equal(out.numpy(), expected_result)

    def test_fp32(self):
        self.check_dtype_transpose('float32')

    def test_fp64(self):
        self.check_dtype_transpose('float64')

    def test_fp16(self):
        if paddle.is_compiled_with_cuda() or is_custom_device():
            self.check_dtype_transpose('float16')

    def test_int8(self):
        self.check_dtype_transpose('int8')

    def test_int16(self):
        self.check_dtype_transpose('int16')

    def test_int32(self):
        self.check_dtype_transpose('int32')

    def test_int64(self):
        self.check_dtype_transpose('int64')

    def tearDown(self):
        paddle.enable_static()


class TestTransposeCompatibility(unittest.TestCase):
    def setUp(self):
        self.places = [paddle.CPUPlace()]
        if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
            self.places.append(get_device_place())
        self.func = paddle.transpose
        self.init_data()

    def init_data(self):
        self.shape = [4, 5, 6]
        self.dtype = 'float32'
        self.dim0 = 0
        self.dim1 = 1
        self.perm = [1, 0, 2]

        self.np_input = np.random.rand(*self.shape).astype(self.dtype)
        self.np_out = np.transpose(self.np_input, axes=self.perm)

    def test_dygraph_compatibility(self):
        with dygraph_guard():
            for place in self.places:
                paddle.device.set_device(place)
                x = paddle.to_tensor(self.np_input)
                outs = []
                outs.append(paddle.transpose(x, perm=self.perm))
                outs.append(paddle.transpose(x=x, perm=self.perm))
                outs.append(paddle.transpose(input=x, perm=self.perm))
                outs.append(paddle.transpose(x, self.dim0, self.dim1))
                outs.append(
                    paddle.transpose(x=x, dim0=self.dim0, dim1=self.dim1)
                )
                outs.append(
                    paddle.transpose(input=x, dim0=self.dim0, dim1=self.dim1)
                )

                outs.append(x.transpose(self.perm))
                outs.append(x.transpose(self.dim0, self.dim1))
                outs.append(x.transpose(perm=self.perm))
                outs.append(x.transpose(dim0=self.dim0, dim1=self.dim1))
                outs.append(x.transpose(self.dim0, dim1=self.dim1))

                for out in outs:
                    np.testing.assert_array_equal(self.np_out, out.numpy())

    def test_static_compatibility(self):
        with static_guard():
            for place in self.places:
                main = paddle.static.Program()
                startup = paddle.static.Program()
                with paddle.base.program_guard(main, startup):
                    x = paddle.static.data(
                        name="x", shape=self.shape, dtype=self.dtype
                    )
                    outs = []
                    outs.append(paddle.transpose(x, perm=self.perm))
                    outs.append(paddle.transpose(x=x, perm=self.perm))
                    outs.append(paddle.transpose(input=x, perm=self.perm))
                    outs.append(paddle.transpose(x, self.dim0, self.dim1))
                    outs.append(
                        paddle.transpose(x=x, dim0=self.dim0, dim1=self.dim1)
                    )
                    outs.append(
                        paddle.transpose(
                            input=x, dim0=self.dim0, dim1=self.dim1
                        )
                    )

                    outs.append(x.transpose(self.perm))
                    outs.append(x.transpose(self.dim0, self.dim1))
                    outs.append(x.transpose(perm=self.perm))
                    outs.append(x.transpose(dim0=self.dim0, dim1=self.dim1))
                    outs.append(x.transpose(self.dim0, dim1=self.dim1))

                    exe = paddle.base.Executor(place)
                    fetches = exe.run(
                        main,
                        feed={"x": self.np_input},
                        fetch_list=outs,
                    )
                    for out in fetches:
                        np.testing.assert_array_equal(self.np_out, out)


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