#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
#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.

from paddle import nn
import copy
from collections import OrderedDict

from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
from .metrics import DistillationTopkAcc
from .metrics import GoogLeNetTopkAcc
from .metrics import HammingDistance, AccuracyScore


class CombinedMetrics(nn.Layer):
    def __init__(self, config_list):
        super().__init__()
        self.metric_func_list = []
        assert isinstance(config_list, list), (
            'operator config should be a list')
        for config in config_list:
            assert isinstance(config,
                              dict) and len(config) == 1, "yaml format error"
            metric_name = list(config)[0]
            metric_params = config[metric_name]
            if metric_params is not None:
                self.metric_func_list.append(
                    eval(metric_name)(**metric_params))
            else:
                self.metric_func_list.append(eval(metric_name)())

    def __call__(self, *args, **kwargs):
        metric_dict = OrderedDict()
        for idx, metric_func in enumerate(self.metric_func_list):
            metric_dict.update(metric_func(*args, **kwargs))
        return metric_dict


def build_metrics(config):
    metrics_list = CombinedMetrics(copy.deepcopy(config))
    return metrics_list