#!/usr/bin/env python
# -*- coding: utf-8 -*-
######################################################################
#   Copyright (c) 2019 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.
######################################################################
"""
File: utils.py
"""

from __future__ import print_function

import os
import six
import ast
import copy

import numpy as np
import paddle.fluid as fluid


def init_checkpoint(exe, init_checkpoint_path, main_program):
    assert os.path.exists(
        init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path
    fluid.io.load_persistables(
        exe, init_checkpoint_path, main_program=main_program)
    print("Load model from {}".format(init_checkpoint_path))


def init_pretraining_params(exe, pretraining_params_path, main_program):
    assert os.path.exists(pretraining_params_path
                          ), "[%s] cann't be found." % pretraining_params_path

    def existed_params(var):
        if not isinstance(var, fluid.framework.Parameter):
            return False
        return os.path.exists(os.path.join(pretraining_params_path, var.name))

    fluid.io.load_vars(
        exe,
        pretraining_params_path,
        main_program=main_program,
        predicate=existed_params)
    print("Load pretraining parameters from {}".format(pretraining_params_path))