# copyright (c) 2024 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.


import tarfile
from pathlib import Path

from ..base import BaseEvaluator
from .support_models import SUPPORT_MODELS


class TSADEvaluator(BaseEvaluator):
    """ TS Anomaly Detection Model Evaluator """
    support_models = SUPPORT_MODELS

    def update_config(self):
        """update evalution config
        """
        self.pdx_config.update_dataset(self.global_config.dataset_dir,
                                       "TSADDataset")
        self.pdx_config.update_weights(self.eval_config.weight_path)

    def uncompress_tar_file(self):
        """unpackage the tar file containing training outputs and update weight path
        """
        if tarfile.is_tarfile(self.eval_config.weight_path):
            dest_path = Path(self.eval_config.weight_path).parent
            with tarfile.open(self.eval_config.weight_path, 'r') as tar:
                tar.extractall(path=dest_path)
            self.eval_config.weight_path = dest_path.joinpath(
                "best_accuracy.pdparams/best_model/model.pdparams")

    def eval(self):
        """firstly, update evaluation config, then evaluate model, finally return the evaluation result
        """
        self.uncompress_tar_file()
        self.update_config()
        evaluate_result = self.pdx_model.evaluate(**self.get_eval_kwargs())
        assert evaluate_result.returncode == 0, f"Encountered an unexpected error({evaluate_result.returncode}) in \
evaling!"

        return evaluate_result.metrics

    def get_eval_kwargs(self) -> dict:
        """get key-value arguments of model evalution function

        Returns:
            dict: the arguments of evaluation function.
        """
        return {
            "weight_path": self.eval_config.weight_path,
            "device": self.get_device(using_device_number=1)
        }