# 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.

from __future__ import print_function

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


class TestRollOp(OpTest):

    def setUp(self):
        self.python_api = paddle.roll
        self.op_type = "roll"
        self.init_dtype_type()
        self.inputs = {'X': np.random.random(self.x_shape).astype(self.dtype)}
        self.attrs = {'shifts': self.shifts, 'axis': self.axis}
        self.outputs = {
            'Out':
            np.roll(self.inputs['X'], self.attrs['shifts'], self.attrs['axis'])
        }

    def init_dtype_type(self):
        self.dtype = np.float64
        self.x_shape = (100, 4, 5)
        self.shifts = [101, -1]
        self.axis = [0, -2]

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

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out', check_eager=True)


class TestRollOpCase2(TestRollOp):

    def init_dtype_type(self):
        self.dtype = np.float32
        self.x_shape = (100, 10, 5)
        self.shifts = [8, -1]
        self.axis = [-1, -2]


class TestRollAPI(unittest.TestCase):

    def input_data(self):
        self.data_x = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0],
                                [7.0, 8.0, 9.0]])

    def test_roll_op_api(self):
        self.input_data()

        paddle.enable_static()
        # case 1:
        with program_guard(Program(), Program()):
            x = fluid.layers.data(name='x', shape=[-1, 3])
            z = paddle.roll(x, shifts=1)
            exe = fluid.Executor(fluid.CPUPlace())
            res, = exe.run(feed={'x': self.data_x},
                           fetch_list=[z.name],
                           return_numpy=False)
            expect_out = np.array([[9.0, 1.0, 2.0], [3.0, 4.0, 5.0],
                                   [6.0, 7.0, 8.0]])
            np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        # case 2:
        with program_guard(Program(), Program()):
            x = fluid.layers.data(name='x', shape=[-1, 3])
            z = paddle.roll(x, shifts=1, axis=0)
            exe = fluid.Executor(fluid.CPUPlace())
            res, = exe.run(feed={'x': self.data_x},
                           fetch_list=[z.name],
                           return_numpy=False)
        expect_out = np.array([[7.0, 8.0, 9.0], [1.0, 2.0, 3.0],
                               [4.0, 5.0, 6.0]])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

    def test_dygraph_api(self):
        self.input_data()
        # case 1:
        with fluid.dygraph.guard():
            x = fluid.dygraph.to_variable(self.data_x)
            z = paddle.roll(x, shifts=1)
            np_z = z.numpy()
        expect_out = np.array([[9.0, 1.0, 2.0], [3.0, 4.0, 5.0],
                               [6.0, 7.0, 8.0]])
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 2:
        with fluid.dygraph.guard():
            x = fluid.dygraph.to_variable(self.data_x)
            z = paddle.roll(x, shifts=1, axis=0)
            np_z = z.numpy()
        expect_out = np.array([[7.0, 8.0, 9.0], [1.0, 2.0, 3.0],
                               [4.0, 5.0, 6.0]])
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

    def test_roll_op_false(self):
        self.input_data()

        def test_axis_out_range():
            with program_guard(Program(), Program()):
                x = fluid.layers.data(name='x', shape=[-1, 3])
                z = paddle.roll(x, shifts=1, axis=10)
                exe = fluid.Executor(fluid.CPUPlace())
                res, = exe.run(feed={'x': self.data_x},
                               fetch_list=[z.name],
                               return_numpy=False)

        self.assertRaises(ValueError, test_axis_out_range)

    def test_shifts_as_tensor_dygraph(self):
        with fluid.dygraph.guard():
            x = paddle.arange(9).reshape([3, 3])
            shape = paddle.shape(x)
            shifts = shape // 2
            axes = [0, 1]
            out = paddle.roll(x, shifts=shifts, axis=axes).numpy()
            expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
            np.testing.assert_allclose(out, expected_out, rtol=1e-05)

    def test_shifts_as_tensor_static(self):
        with program_guard(Program(), Program()):
            x = paddle.arange(9).reshape([3, 3]).astype('float32')
            shape = paddle.shape(x)
            shifts = shape // 2
            axes = [0, 1]
            out = paddle.roll(x, shifts=shifts, axis=axes)
            expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])

            exe = fluid.Executor(fluid.CPUPlace())
            [out_np] = exe.run(fetch_list=[out])
            np.testing.assert_allclose(out_np, expected_out, rtol=1e-05)

            if paddle.is_compiled_with_cuda():
                exe = fluid.Executor(fluid.CPUPlace())
                [out_np] = exe.run(fetch_list=[out])
                np.testing.assert_allclose(out_np, expected_out, rtol=1e-05)


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