# Copyright (c) 2021 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 numpy as np
from op_test import OpTest, get_places
from utils import dygraph_guard, static_guard

import paddle
from paddle import base, static
from paddle.base import core

paddle.enable_static()


class TestMatrixPowerOp(OpTest):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 0

    def setUp(self):
        self.op_type = "matrix_power"
        self.python_api = paddle.tensor.matrix_power
        self.config()

        np.random.seed(123)
        mat = np.random.random(self.matrix_shape).astype(self.dtype)
        powered_mat = np.linalg.matrix_power(mat, self.n)

        self.inputs = {"X": mat}
        self.outputs = {"Out": powered_mat}
        self.attrs = {"n": self.n}

    def test_check_output(self):
        self.check_output(check_pir=True)

    def test_grad(self):
        self.check_grad(
            ["X"],
            "Out",
            numeric_grad_delta=1e-5,
            max_relative_error=1e-7,
            check_pir=True,
        )


class TestMatrixPowerOpN1(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 1


class TestMatrixPowerOpN2(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 2


class TestMatrixPowerOpN3(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 3


class TestMatrixPowerOpN4(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 4


class TestMatrixPowerOpN5(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 5


class TestMatrixPowerOpN6(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 6


class TestMatrixPowerOpN10(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 10


class TestMatrixPowerOpNMinus(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -1

    def test_grad(self):
        self.check_grad(
            ["X"],
            "Out",
            numeric_grad_delta=1e-5,
            max_relative_error=1e-6,
            check_pir=True,
        )


class TestMatrixPowerOpNMinus2(TestMatrixPowerOpNMinus):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -2


class TestMatrixPowerOpNMinus3(TestMatrixPowerOpNMinus):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -3


class TestMatrixPowerOpNMinus4(TestMatrixPowerOpNMinus):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -4


class TestMatrixPowerOpNMinus5(TestMatrixPowerOpNMinus):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -5


class TestMatrixPowerOpNMinus6(TestMatrixPowerOpNMinus):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -6


class TestMatrixPowerOpNMinus10(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = -10

    def test_grad(self):
        self.check_grad(
            ["X"],
            "Out",
            numeric_grad_delta=1e-5,
            max_relative_error=1e-6,
            check_pir=True,
        )


class TestMatrixPowerOpBatched1(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [8, 4, 4]
        self.dtype = "float64"
        self.n = 5


class TestMatrixPowerOpBatched2(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 6, 4, 4]
        self.dtype = "float64"
        self.n = 4


class TestMatrixPowerOpBatched3(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 6, 4, 4]
        self.dtype = "float64"
        self.n = 0


class TestMatrixPowerOpBatchedLong(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [1, 2, 3, 4, 4, 3, 3]
        self.dtype = "float64"
        self.n = 3


class TestMatrixPowerOpLarge1(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [32, 32]
        self.dtype = "float64"
        self.n = 3


class TestMatrixPowerOpLarge2(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float64"
        self.n = 32


class TestMatrixPowerOpZeroSize(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [0, 0]
        self.dtype = "float32"
        self.n = 32


class TestMatrixPowerOpZeroSize1(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [0, 0]
        self.dtype = "float32"
        self.n = 0


class TestMatrixPowerOpZeroSize2(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [0, 0]
        self.dtype = "float32"
        self.n = -1


class TestMatrixPowerOpBatchedZeroSize1(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 0, 4, 4]
        self.dtype = "float32"
        self.n = 4


class TestMatrixPowerOpBatchedZeroSize2(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 0, 4, 4]
        self.dtype = "float32"
        self.n = 0


class TestMatrixPowerOpBatchedZeroSize3(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 0, 4, 4]
        self.dtype = "float32"
        self.n = -1


class TestMatrixPowerOpBatchedZeroSize4(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 6, 0, 0]
        self.dtype = "float32"
        self.n = 1


class TestMatrixPowerOpBatchedZeroSize5(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 6, 0, 0]
        self.dtype = "float32"
        self.n = 0


class TestMatrixPowerOpBatchedZeroSize6(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [2, 6, 0, 0]
        self.dtype = "float32"
        self.n = -1


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpComplex64(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "complex64"
        self.n = 2

    def test_grad(self):
        self.check_grad(["X"], "Out", max_relative_error=1e-2, check_pir=True)


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpBatchedComplex64(TestMatrixPowerOpComplex64):
    def config(self):
        self.matrix_shape = [2, 8, 4, 4]
        self.dtype = "complex64"
        self.n = 2


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpLarge1Complex64(TestMatrixPowerOpComplex64):
    def config(self):
        self.matrix_shape = [32, 32]
        self.dtype = "complex64"
        self.n = 2


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpLarge2Complex64(TestMatrixPowerOpComplex64):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "complex64"
        self.n = 32


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpComplex64Minus(TestMatrixPowerOpComplex64):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "complex64"
        self.n = -1


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpComplex128(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "complex128"
        self.n = 2

    def test_grad(self):
        self.check_grad(["X"], "Out", max_relative_error=1e-2, check_pir=True)


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpBatchedComplex128(TestMatrixPowerOpComplex128):
    def config(self):
        self.matrix_shape = [2, 8, 4, 4]
        self.dtype = "complex128"
        self.n = 2


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpLarge1Complex128(TestMatrixPowerOpComplex128):
    def config(self):
        self.matrix_shape = [32, 32]
        self.dtype = "complex128"
        self.n = 2


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpLarge2Complex128(TestMatrixPowerOpComplex128):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "complex128"
        self.n = 32


@unittest.skipIf(
    core.is_compiled_with_xpu(),
    "Skip complex due to lack of mean support",
)
class TestMatrixPowerOpComplex128Minus(TestMatrixPowerOpComplex128):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "complex128"
        self.n = -1


class TestMatrixPowerOpFP32(TestMatrixPowerOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float32"
        self.n = 2

    def test_grad(self):
        self.check_grad(["X"], "Out", max_relative_error=1e-2, check_pir=True)


class TestMatrixPowerOpBatchedFP32(TestMatrixPowerOpFP32):
    def config(self):
        self.matrix_shape = [2, 8, 4, 4]
        self.dtype = "float32"
        self.n = 2


class TestMatrixPowerOpLarge1FP32(TestMatrixPowerOpFP32):
    def config(self):
        self.matrix_shape = [32, 32]
        self.dtype = "float32"
        self.n = 2


class TestMatrixPowerOpLarge2FP32(TestMatrixPowerOpFP32):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float32"
        self.n = 32


class TestMatrixPowerOpFP32Minus(TestMatrixPowerOpFP32):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float32"
        self.n = -1


class TestMatrixPowerAPI(unittest.TestCase):
    def setUp(self):
        np.random.seed(123)
        self.places = get_places()

    def check_static_result(self, place):
        with static.program_guard(static.Program(), static.Program()):
            input_x = paddle.static.data(
                name="input_x", shape=[4, 4], dtype="float64"
            )
            result = paddle.linalg.matrix_power(x=input_x, n=-2)
            input_np = np.random.random([4, 4]).astype("float64")
            result_np = np.linalg.matrix_power(input_np, -2)

            exe = base.Executor(place)
            fetches = exe.run(
                feed={"input_x": input_np},
                fetch_list=[result],
            )
            np.testing.assert_allclose(
                fetches[0], np.linalg.matrix_power(input_np, -2), rtol=1e-05
            )

    def test_static(self):
        for place in self.places:
            self.check_static_result(place=place)

    def test_dygraph(self):
        for place in self.places:
            with base.dygraph.guard(place):
                input_np = np.random.random([4, 4]).astype("float64")
                input = paddle.to_tensor(input_np)
                result = paddle.linalg.matrix_power(input, -2)
                np.testing.assert_allclose(
                    result.numpy(),
                    np.linalg.matrix_power(input_np, -2),
                    rtol=1e-05,
                )


class TestMatrixPowerAPIError(unittest.TestCase):
    def test_errors(self):
        input_np = np.random.random([4, 4]).astype("float64")

        # input must be Variable.
        self.assertRaises(TypeError, paddle.linalg.matrix_power, input_np)

        # n must be int
        for n in [2.0, '2', -2.0]:
            input = paddle.static.data(
                name="input_float32", shape=[4, 4], dtype='float32'
            )
            self.assertRaises(TypeError, paddle.linalg.matrix_power, input, n)

        # The data type of input must be float32 or float64.
        for dtype in ["bool", "int32", "int64", "float16"]:
            input = paddle.static.data(
                name="input_" + dtype, shape=[4, 4], dtype=dtype
            )
            self.assertRaises(TypeError, paddle.linalg.matrix_power, input, 2)

        # The number of dimensions of input must be >= 2.
        input = paddle.static.data(name="input_2", shape=[4], dtype="float32")
        self.assertRaises(ValueError, paddle.linalg.matrix_power, input, 2)

        # The inner-most 2 dimensions of input should be equal to each other
        input = paddle.static.data(
            name="input_3", shape=[4, 5], dtype="float32"
        )
        self.assertRaises(ValueError, paddle.linalg.matrix_power, input, 2)

    def test_old_ir_errors(self):
        if paddle.framework.use_pir_api():
            return
        # When out is set, the data type must be the same as input.
        input = paddle.static.data(
            name="input_1", shape=[4, 4], dtype="float32"
        )
        out = paddle.static.data(name="output", shape=[4, 4], dtype="float64")
        self.assertRaises(TypeError, paddle.linalg.matrix_power, input, 2, out)


class TestMatrixPowerSingularAPI(unittest.TestCase):
    def setUp(self):
        self.places = get_places()

    def check_static_result(self, place):
        with static.program_guard(static.Program(), static.Program()):
            input = paddle.static.data(
                name="input", shape=[4, 4], dtype="float64"
            )
            result = paddle.linalg.matrix_power(x=input, n=-2)

            input_np = np.zeros([4, 4]).astype("float64")

            exe = base.Executor(place)
            try:
                fetches = exe.run(
                    feed={"input": input_np},
                    fetch_list=[result],
                )
            except RuntimeError as ex:
                print("The mat is singular")
            except ValueError as ex:
                print("The mat is singular")

    def test_static(self):
        paddle.enable_static()
        for place in self.places:
            self.check_static_result(place=place)
        paddle.disable_static()

    def test_dygraph(self):
        for place in self.places:
            with base.dygraph.guard(place):
                input_np = np.ones([4, 4]).astype("float64")
                input = paddle.to_tensor(input_np)
                try:
                    result = paddle.linalg.matrix_power(input, -2)
                except RuntimeError as ex:
                    print("The mat is singular")
                except ValueError as ex:
                    print("The mat is singular")


class TestMatrixPowerEmptyTensor(unittest.TestCase):
    def _get_places(self):
        return get_places()

    def _test_matrix_power_empty_static(self, place):
        with (
            static_guard(),
            paddle.static.program_guard(
                paddle.static.Program(), paddle.static.Program()
            ),
        ):
            x2 = paddle.static.data(name='x2', shape=[0, 6], dtype='float32')
            x3 = paddle.static.data(name='x3', shape=[6, 0], dtype='float32')
            x4 = paddle.static.data(
                name='x4', shape=[0, 0, 2, 3], dtype='float32'
            )
            self.assertRaises(TypeError, paddle.linalg.matrix_power, x2)
            self.assertRaises(TypeError, paddle.linalg.matrix_power, x3)
            self.assertRaises(TypeError, paddle.linalg.matrix_power, x4)

            x = paddle.static.data(name='x', shape=[0, 0], dtype='float32')
            y = paddle.linalg.matrix_power(x, 2)
            x5 = paddle.static.data(
                name='x5', shape=[2, 3, 0, 0], dtype='float32'
            )
            y5 = paddle.linalg.matrix_power(x5, 2)
            exe = paddle.static.Executor(place)
            res = exe.run(
                feed={
                    'x2': np.zeros((0, 6), dtype='float32'),
                    'x3': np.zeros((6, 0), dtype='float32'),
                    'x4': np.zeros((0, 0, 2, 3), dtype='float32'),
                    'x': np.zeros((0, 0), dtype='float32'),
                    'x5': np.zeros((2, 3, 0, 0), dtype='float32'),
                },
                fetch_list=[y, y5],
            )
            self.assertEqual(res[0].shape, (0, 0))
            self.assertEqual(res[1].shape, (2, 3, 0, 0))

    def _test_matrix_power_empty_dynamic(self):
        with dygraph_guard():
            x2 = paddle.full((0, 6), 1.0, dtype='float32')
            x3 = paddle.full((6, 0), 1.0, dtype='float32')
            x4 = paddle.full((2, 3, 0, 0), 1.0, dtype='float32')
            x5 = paddle.full((0, 0, 2, 3), 1.0, dtype='float32')
            self.assertRaises(TypeError, paddle.linalg.matrix_power, x2)
            self.assertRaises(TypeError, paddle.linalg.matrix_power, x3)
            self.assertRaises(TypeError, paddle.linalg.matrix_power, x5)
            x = paddle.full((0, 0), 1.0, dtype='float32')
            y = paddle.linalg.matrix_power(x, 2)
            y4 = paddle.linalg.matrix_power(x4, 2)
            self.assertEqual(y4.shape, [2, 3, 0, 0])
            self.assertEqual(y.shape, [0, 0])

    def test_matrix_power_empty_tensor(self):
        for place in self._get_places():
            self._test_matrix_power_empty_static(place)
        self._test_matrix_power_empty_dynamic()


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