# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.

"""tan_run"""
import numpy as np
from akg.utils import kernel_exec as utils
from tests.common.test_op import tan
from tests.common.tensorio import compare_tensor
from tests.common.gen_random import random_gaussian
from tests.common.base import get_rtol_atol

def tan_run(shape, dtype, attrs=None):
    """tan_run implementation"""
    if attrs is None:
        attrs = {}
    mod = utils.op_build_test(tan.tan, [shape], [dtype], kernel_name='tan', attrs=attrs)
    args, exp_output, input_x = gen_data(shape, dtype)

    acu_output = utils.mod_launch(mod, args, expect=exp_output)
    # compare result
    rtol, atol = get_rtol_atol("tan", dtype)
    testcase_result = compare_tensor(acu_output, exp_output, rtol=rtol, atol=atol, equal_nan=True)
    return input_x, acu_output, exp_output, testcase_result


def gen_data(shape, dtype):
    """generate data for test"""
    # Result_Numpy
    input_x = random_gaussian(shape, miu=3, sigma=0.3).astype(dtype)
    exp_output = np.tan(input_x)
    if dtype == "int32":
        exp_output = np.round(exp_output).astype(dtype)
    # inputs and output to hold the data
    output = np.full(shape, np.nan, dtype)
    args = [input_x, output]
    return args, exp_output, input_x
