# Copyright 2019 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.

import numpy as np
from akg.utils import kernel_exec as utils
from test_op import not_equal
from gen_random import random_gaussian


def not_equal_run(shapes, dtype, kernel_name, attrs, cce_path="./"):
    if 'tuning' in attrs.keys():
        t = attrs.get("tuning", False)
        kernel_name = attrs.get("kernel_name", False)
        mod = utils.op_build_test(not_equal.not_equal, shapes, [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
        if t:
            benchMark1, inputs1, output1 = gen_data(shapes)
            return mod, benchMark1, inputs1 + [output1]
        else:
            return mod
    else:
        mod = utils.op_build_test(not_equal.not_equal, shapes, [dtype, dtype], kernel_name=kernel_name, attrs=attrs)
        benchMark1, inputs1, output1 = gen_data(shapes)
        output1 = utils.mod_launch(mod, inputs1 + [output1], expect=benchMark1)

        # Also test the case where the inputs are equal
        inputs2 = []
        inputs2.append(inputs1[0])
        inputs2.append(inputs1[0])
        benchMark2 = np.logical_not(np.equal(inputs2[0], inputs2[1]))
        output2 = np.full(benchMark2.shape, 0, bool)
        output2 = utils.mod_launch(mod, inputs2 + [output2], expect=benchMark2)

        testPass = (np.array_equal(output1, benchMark1) and np.array_equal(output2, benchMark2))
        return (inputs1, inputs2), (output1, output2), (benchMark1, benchMark2), testPass


def gen_data(shapes):
    inputs1 = []
    for i in range(len(shapes)):
        shape = shapes[i]
        input = random_gaussian(shape, miu=1, sigma=0.1).astype(np.float16)
        inputs1.append(input)
    """
    inputs.append(np.ones(shapes[0], dtype=np.float16))
    for i in range(16):
        inputs[0][i]=0
    inputs.append(np.zeros(shapes[0], dtype=np.float16))
    """
    if len(inputs1) != 2:
        raise RuntimeError("inputs num should be 2")
    benchMark1 = np.logical_not(np.equal(inputs1[0], inputs1[1]))
    output1 = np.full(benchMark1.shape, 0, bool)
    return benchMark1, inputs1, output1
