#   Copyright (c) 2018 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 paddle import enable_static
from paddle.fluid import core
from paddle.fluid.tests.unittests.eager_op_test import OpTest


class TestConcatAxis0OneDNNOp(OpTest):
    def setUp(self):
        self.op_type = "concat"
        self.mkldnn_data_type = "float32"
        self.init_axis()
        self.init_shape()
        self.init_test_data()
        self.configure_datatype()
        self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
        self.attrs = {
            'axis': self.axis,
            'use_mkldnn': True,
            'mkldnn_data_type': self.mkldnn_data_type,
        }

        self.output = np.concatenate(
            (self.x0, self.x1, self.x2), axis=self.axis
        ).astype(self.dtype)

        self.outputs = {'Out': self.output}

    def configure_datatype(self):
        self.mkldnn_data_type = "float32"
        self.dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(core.CPUPlace(), check_dygraph=False)

    def test_check_grad(self):
        self.check_grad(['x0'], 'Out', check_dygraph=False)
        self.check_grad(['x1'], 'Out', check_dygraph=False)
        self.check_grad(['x2'], 'Out', check_dygraph=False)

    def init_test_data(self):
        self.x0 = np.random.random(self.x0_shape).astype(np.float32)
        self.x1 = np.random.random(self.x1_shape).astype(np.float32)
        self.x2 = np.random.random(self.x2_shape).astype(np.float32)

    def init_axis(self):
        self.axis = 0

    def init_shape(self):
        self.x0_shape = [2, 2, 1, 50]
        self.x1_shape = [1, 2, 1, 50]
        self.x2_shape = [3, 2, 1, 50]


class TestConcatAxis1OneDNNOp(TestConcatAxis0OneDNNOp):
    def init_axis(self):
        self.axis = 1

    def init_shape(self):
        self.x0_shape = [1, 1, 5, 50]
        self.x1_shape = [1, 2, 5, 50]
        self.x2_shape = [1, 3, 5, 50]


class TestConcatAxis2OneDNNOp(TestConcatAxis0OneDNNOp):
    def init_axis(self):
        self.axis = 2

    def init_shape(self):
        self.x0_shape = [2, 3, 4, 50]
        self.x1_shape = [2, 3, 5, 50]
        self.x2_shape = [2, 3, 6, 50]


class TestConcatAxis3OneDNNOp(TestConcatAxis0OneDNNOp):
    def init_axis(self):
        self.axis = 3

    def init_shape(self):
        self.x0_shape = [5, 3, 5, 5]
        self.x1_shape = [5, 3, 5, 6]
        self.x2_shape = [5, 3, 5, 7]


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