# Copyright (c) 2020 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
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle
from op_test import OpTest


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

    def setUp(self):
        self.op_type = "inverse"
        self.config()

        np.random.seed(123)
        mat = np.random.random(self.matrix_shape).astype(self.dtype)
        inverse = np.linalg.inv(mat)

        self.inputs = {'Input': mat}
        self.outputs = {'Output': inverse}

    def test_check_output(self):
        self.check_output()

    def test_grad(self):
        self.check_grad(['Input'], 'Output')


class TestInverseOpBatched(TestInverseOp):
    def config(self):
        self.matrix_shape = [8, 4, 4]
        self.dtype = "float64"


class TestInverseOpLarge(TestInverseOp):
    def config(self):
        self.matrix_shape = [32, 32]
        self.dtype = "float64"

    def test_grad(self):
        self.check_grad(['Input'], 'Output', max_relative_error=1e-6)


class TestInverseOpFP32(TestInverseOp):
    def config(self):
        self.matrix_shape = [10, 10]
        self.dtype = "float32"

    def test_grad(self):
        self.check_grad(['Input'], 'Output', max_relative_error=1e-2)


class TestInverseOpBatchedFP32(TestInverseOpFP32):
    def config(self):
        self.matrix_shape = [8, 4, 4]
        self.dtype = "float32"


class TestInverseOpLargeFP32(TestInverseOpFP32):
    def config(self):
        self.matrix_shape = [32, 32]
        self.dtype = "float32"


class TestInverseAPI(unittest.TestCase):
    def setUp(self):
        np.random.seed(123)
        self.places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            self.places.append(fluid.CUDAPlace(0))

    def check_static_result(self, place):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            input = fluid.data(name="input", shape=[4, 4], dtype="float64")
            result = paddle.inverse(x=input)
            input_np = np.random.random([4, 4]).astype("float64")
            result_np = np.linalg.inv(input_np)

            exe = fluid.Executor(place)
            fetches = exe.run(fluid.default_main_program(),
                              feed={"input": input_np},
                              fetch_list=[result])
            self.assertTrue(np.allclose(fetches[0], np.linalg.inv(input_np)))

    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 fluid.dygraph.guard(place):
                input_np = np.random.random([4, 4]).astype("float64")
                input = fluid.dygraph.to_variable(input_np)
                result = paddle.inverse(input)
                self.assertTrue(
                    np.allclose(result.numpy(), np.linalg.inv(input_np)))


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

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

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

        # When out is set, the data type must be the same as input.
        input = fluid.data(name='input_1', shape=[4, 4], dtype="float32")
        out = fluid.data(name='output', shape=[4, 4], dtype="float64")
        self.assertRaises(TypeError, paddle.inverse, input, out)

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


class TestInverseSingularAPI(unittest.TestCase):
    def setUp(self):
        self.places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            self.places.append(fluid.CUDAPlace(0))

    def check_static_result(self, place):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            input = fluid.data(name="input", shape=[4, 4], dtype="float64")
            result = paddle.inverse(x=input)

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

            exe = fluid.Executor(place)
            try:
                fetches = exe.run(fluid.default_main_program(),
                                  feed={"input": input_np},
                                  fetch_list=[result])
            except fluid.core.EnforceNotMet as ex:
                print("The mat is singular")
                pass

    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 fluid.dygraph.guard(place):
                input_np = np.ones([4, 4]).astype("float64")
                input = fluid.dygraph.to_variable(input_np)
                try:
                    result = paddle.inverse(input)
                except fluid.core.EnforceNotMet as ex:
                    print("The mat is singular")
                    pass


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