# 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 numpy as np
import unittest
import sys

sys.path.append("..")
from op_test import OpTest, skip_check_grad_ci
import paddle

paddle.enable_static()


class TestMLUReciprocal(OpTest):
    def setUp(self):
        self.op_type = "reciprocal"
        self.set_mlu()
        self.init_dtype()

        np.random.seed(1024)
        x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
        out = np.reciprocal(x)

        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
        self.outputs = {'Out': out}

    def test_check_output(self):
        self.check_output_with_place(self.place)

    def test_check_grad(self):
        self.check_grad_with_place(
            self.place, ['X'], 'Out', max_relative_error=0.01
        )

    def set_mlu(self):
        self.__class__.use_mlu = True
        self.place = paddle.MLUPlace(0)

    def init_dtype(self):
        self.dtype = np.float32


class TestMLUReciprocalFp16(TestMLUReciprocal):
    def set_mlu(self):
        self.__class__.use_mlu = True
        self.place = paddle.MLUPlace(0)

    def init_dtype(self):
        self.dtype = np.float16


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