# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os
from pathlib import Path

from ...utils.misc import abspath
from ..image_classification import ClsTrainer
from .model_list import MODELS


class FaceRecTrainer(ClsTrainer):
    """Face Recognition Model Trainer"""

    entities = 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.update_dataset_cfg()
        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 != "":
            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)

    def update_dataset_cfg(self):
        train_dataset_dir = abspath(
            os.path.join(self.global_config.dataset_dir, "train")
        )
        val_dataset_dir = abspath(os.path.join(self.global_config.dataset_dir, "val"))
        train_list_path = abspath(os.path.join(train_dataset_dir, "label.txt"))
        val_list_path = abspath(os.path.join(val_dataset_dir, "pair_label.txt"))

        ds_cfg = [
            f"DataLoader.Train.dataset.name=ClsDataset",
            f"DataLoader.Train.dataset.image_root={train_dataset_dir}",
            f"DataLoader.Train.dataset.cls_label_path={train_list_path}",
            f"DataLoader.Eval.dataset.name=FaceEvalDataset",
            f"DataLoader.Eval.dataset.dataset_root={val_dataset_dir}",
            f"DataLoader.Eval.dataset.pair_label_path={val_list_path}",
        ]
        self.pdx_config.update(ds_cfg)