# Copyright (c) 2020 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import pickle

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))

from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.utility import print_dict
import tools.program as program


def main():
    global_config = config['Global']
    # build dataloader
    config['Eval']['dataset']['name'] = config['Train']['dataset']['name']
    config['Eval']['dataset']['data_dir'] = config['Train']['dataset'][
        'data_dir']
    config['Eval']['dataset']['label_file_list'] = config['Train']['dataset'][
        'label_file_list']
    eval_dataloader = build_dataloader(config, 'Eval', device, logger)

    # build post process
    post_process_class = build_post_process(config['PostProcess'],
                                            global_config)

    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
        char_num = len(getattr(post_process_class, 'character'))
        config['Architecture']["Head"]['out_channels'] = char_num

    #set return_features = True
    config['Architecture']["Head"]["return_feats"] = True

    model = build_model(config['Architecture'])

    best_model_dict = load_dygraph_params(config, model, logger, None)
    if len(best_model_dict):
        logger.info('metric in ckpt ***************')
        for k, v in best_model_dict.items():
            logger.info('{}:{}'.format(k, v))

    # get features from train data
    char_center = program.get_center(model, eval_dataloader, post_process_class)

    #serialize to disk
    with open("train_center.pkl", 'wb') as f:
        pickle.dump(char_center, f)
    return


if __name__ == '__main__':
    config, device, logger, vdl_writer = program.preprocess()
    main()