# 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 json
import shutil
import paddle
from pathlib import Path

from ..base import BaseTrainer, BaseTrainDeamon
from .trainer import ClsTrainer, ClsTrainDeamon
from .model_list import ML_MODELS
from ...utils.config import AttrDict


class MLClsTrainer(ClsTrainer, BaseTrainer):
    """ Multi Label Image Classification Model Trainer """
    entities = ML_MODELS

    def update_config(self):
        """update training config
        """
        if self.train_config.log_interval:
            self.pdx_config.update_log_interval(self.train_config.log_interval)
        if self.train_config.eval_interval:
            self.pdx_config.update_eval_interval(
                self.train_config.eval_interval)
        if self.train_config.save_interval:
            self.pdx_config.update_save_interval(
                self.train_config.save_interval)

        self.pdx_config.update_dataset(self.global_config.dataset_dir,
                                       "MLClsDataset")
        if self.train_config.num_classes is not None:
            self.pdx_config.update_num_classes(self.train_config.num_classes)
        if self.train_config.pretrain_weight_path and self.train_config.pretrain_weight_path != "":
            self.pdx_config.update_pretrained_weights(
                self.train_config.pretrain_weight_path)

        label_dict_path = Path(self.global_config.dataset_dir).joinpath(
            "label.txt")
        if label_dict_path.exists():
            self.dump_label_dict(label_dict_path)
        if self.train_config.batch_size is not None:
            self.pdx_config.update_batch_size(self.train_config.batch_size)
        if self.train_config.learning_rate is not None:
            self.pdx_config.update_learning_rate(
                self.train_config.learning_rate)
        if self.train_config.epochs_iters is not None:
            self.pdx_config._update_epochs(self.train_config.epochs_iters)
        if self.train_config.warmup_steps is not None:
            self.pdx_config.update_warmup_epochs(self.train_config.warmup_steps)
        if self.global_config.output is not None:
            self.pdx_config._update_output_dir(self.global_config.output)