#   Copyright (c) 2019 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.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid import Program, program_guard


class TestDiagV2Op(OpTest):
    def setUp(self):
        self.op_type = "diag_v2"
        self.x = np.random.rand(10, 10)
        self.offset = 0
        self.padding_value = 0.0
        self.out = np.diag(self.x, self.offset)

        self.init_config()
        self.inputs = {'X': self.x}
        self.attrs = {
            'offset': self.offset,
            'padding_value': self.padding_value
        }
        self.outputs = {'Out': self.out}

    def test_check_output(self):
        self.check_output()

    def init_config(self):
        pass


class TestDiagV2OpCase1(TestDiagV2Op):
    def init_config(self):
        self.offset = 1
        self.out = np.diag(self.x, self.offset)


class TestDiagV2OpCase2(TestDiagV2Op):
    def init_config(self):
        self.offset = -1
        self.out = np.diag(self.x, self.offset)


class TestDiagV2OpCase3(TestDiagV2Op):
    def init_config(self):
        self.x = np.random.randint(-10, 10, size=(10, 10))
        self.out = np.diag(self.x, self.offset)


class TestDiagV2OpCase4(TestDiagV2Op):
    def init_config(self):
        self.x = np.random.rand(100)
        self.padding_value = 8
        n = self.x.size
        self.out = self.padding_value * np.ones((n, n)) + np.diag(
            self.x, self.offset) - np.diag(self.padding_value * np.ones(n))


class TestDiagV2Error(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):

            def test_diag_v2_type():
                x = [1, 2, 3]
                output = paddle.diag(x)

            self.assertRaises(TypeError, test_diag_v2_type)

            x = paddle.static.data('data', [3, 3])
            self.assertRaises(TypeError, paddle.diag, x, offset=2.5)

            self.assertRaises(TypeError, paddle.diag, x, padding_value=[9])

            x = paddle.static.data('data2', [3, 3, 3])
            self.assertRaises(ValueError, paddle.diag, x)


class TestDiagV2API(unittest.TestCase):
    def setUp(self):
        self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
        self.expected0 = np.diag(self.input_np)
        self.expected1 = np.diag(self.input_np, k=1)
        self.expected2 = np.diag(self.input_np, k=-1)

        self.input_np2 = np.random.rand(100)
        self.offset = 0
        self.padding_value = 8
        n = self.input_np2.size
        self.expected3 = self.padding_value * np.ones(
            (n, n)) + np.diag(self.input_np2, self.offset) - np.diag(
                self.padding_value * np.ones(n))

        self.input_np3 = np.random.randint(-10, 10, size=(100)).astype(np.int64)
        self.padding_value = 8.0
        n = self.input_np3.size
        self.expected4 = self.padding_value * np.ones(
            (n, n)) + np.diag(self.input_np3, self.offset) - np.diag(
                self.padding_value * np.ones(n))

        self.padding_value = -8
        self.expected5 = self.padding_value * np.ones(
            (n, n)) + np.diag(self.input_np3, self.offset) - np.diag(
                self.padding_value * np.ones(n))

    def run_imperative(self):
        x = paddle.to_tensor(self.input_np)
        y = paddle.diag(x)
        self.assertTrue(np.allclose(y.numpy(), self.expected0))

        y = paddle.diag(x, offset=1)
        self.assertTrue(np.allclose(y.numpy(), self.expected1))

        y = paddle.diag(x, offset=-1)
        self.assertTrue(np.allclose(y.numpy(), self.expected2))

        x = paddle.to_tensor(self.input_np2)
        y = paddle.diag(x, padding_value=8)
        self.assertTrue(np.allclose(y.numpy(), self.expected3))

        x = paddle.to_tensor(self.input_np3)
        y = paddle.diag(x, padding_value=8.0)
        self.assertTrue(np.allclose(y.numpy(), self.expected4))

        y = paddle.diag(x, padding_value=-8)
        self.assertTrue(np.allclose(y.numpy(), self.expected5))

    def run_static(self, use_gpu=False):
        x = paddle.data(name='input', shape=[10, 10], dtype='float32')
        x2 = paddle.data(name='input2', shape=[100], dtype='float64')
        x3 = paddle.data(name='input3', shape=[100], dtype='int64')
        result0 = paddle.diag(x)
        result1 = paddle.diag(x, offset=1)
        result2 = paddle.diag(x, offset=-1)
        result3 = paddle.diag(x, name='aaa')
        result4 = paddle.diag(x2, padding_value=8)
        result5 = paddle.diag(x3, padding_value=8.0)
        result6 = paddle.diag(x3, padding_value=-8)

        place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        res0, res1, res2, res4, res5, res6 = exe.run(
            feed={
                "input": self.input_np,
                "input2": self.input_np2,
                'input3': self.input_np3
            },
            fetch_list=[result0, result1, result2, result4, result5, result6])

        self.assertTrue(np.allclose(res0, self.expected0))
        self.assertTrue(np.allclose(res1, self.expected1))
        self.assertTrue(np.allclose(res2, self.expected2))
        self.assertTrue('aaa' in result3.name)
        self.assertTrue(np.allclose(res4, self.expected3))
        self.assertTrue(np.allclose(res5, self.expected4))
        self.assertTrue(np.allclose(res6, self.expected5))

    def test_cpu(self):
        paddle.disable_static(place=paddle.fluid.CPUPlace())
        self.run_imperative()
        paddle.enable_static()

        with fluid.program_guard(fluid.Program()):
            self.run_static()

    def test_gpu(self):
        if not fluid.core.is_compiled_with_cuda():
            return

        paddle.disable_static(place=paddle.fluid.CUDAPlace(0))
        self.run_imperative()
        paddle.enable_static()

        with fluid.program_guard(fluid.Program()):
            self.run_static(use_gpu=True)


class TestDiagOp(OpTest):
    def setUp(self):
        self.op_type = "diag"
        self.init_config()
        self.inputs = {'Diagonal': self.case}

        self.outputs = {'Out': np.diag(self.inputs['Diagonal'])}

    def test_check_output(self):
        self.check_output()

    def init_config(self):
        self.case = np.arange(3, 6)


class TestDiagOpCase1(TestDiagOp):
    def init_config(self):
        self.case = np.array([3], dtype='int32')


class TestDiagError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):

            def test_diag_type():
                x = [1, 2, 3]
                output = fluid.layers.diag(diag=x)

            self.assertRaises(TypeError, test_diag_type)


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