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


def set_paddle_flags(**kwargs):
    for key, value in kwargs.items():
        if os.environ.get(key, None) is None:
            os.environ[key] = str(value)


# NOTE(paddle-dev): All of these flags should be
# set before `import paddle`. Otherwise, it would
# not take any effect.
set_paddle_flags(
    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
)

import program
from paddle import fluid
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.save_load import init_model
from paddle_serving_client.io import save_model


def main():
    startup_prog, eval_program, place, config, _ = program.preprocess()

    feeded_var_names, target_vars, fetches_var_name = program.build_export(
        config, eval_program, startup_prog)
    eval_program = eval_program.clone(for_test=True)
    exe = fluid.Executor(place)
    exe.run(startup_prog)

    init_model(config, eval_program, exe)

    save_inference_dir = config['Global']['save_inference_dir']
    if not os.path.exists(save_inference_dir):
        os.makedirs(save_inference_dir)
    serving_client_dir = "{}/serving_client_dir".format(save_inference_dir)
    serving_server_dir = "{}/serving_server_dir".format(save_inference_dir)

    feed_dict = {
        x: eval_program.global_block().var(x)
        for x in feeded_var_names
    }
    fetch_dict = {x.name: x for x in target_vars}
    save_model(serving_server_dir, serving_client_dir, feed_dict, fetch_dict,
               eval_program)
    print(
        "paddle serving model saved in {}/serving_server_dir and {}/serving_client_dir".
        format(save_inference_dir, save_inference_dir))
    print("save success, output_name_list:", fetches_var_name)


if __name__ == '__main__':
    main()