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//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// 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.
//*****************************************************************************
#include <algorithm>
#include <cinttypes>
#include <cmath>
#include <cstdlib>
#include <random>
#include <string>
// clang-format off
#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
#endif
#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#endif
// clang-format on
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
NGRAPH_TEST(${BACKEND_NAME}, erf)
{
Shape shape{8};
auto A = make_shared<op::Parameter>(element::f32, shape);
auto f = make_shared<Function>(make_shared<op::Erf>(A), ParameterVector{A});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, shape);
copy_data(a, vector<float>{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f});
auto result = backend->create_tensor(element::f32, shape);
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a});
EXPECT_TRUE(test::all_close_f(vector<float>{erf(-4.0f),
erf(-3.0f),
erf(-2.0f),
erf(-1.0f),
erf(0.0f),
erf(1.0f),
erf(2.0f),
erf(3.0f)},
read_vector<float>(result)));
}