# 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.

import logging


def initial_logger():
    FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
    logging.basicConfig(level=logging.INFO, format=FORMAT)
    logger = logging.getLogger(__name__)
    return logger


import importlib


def create_module(module_str):
    tmpss = module_str.split(",")
    assert len(tmpss) == 2, "Error formate\
        of the module path: {}".format(module_str)
    module_name, function_name = tmpss[0], tmpss[1]
    somemodule = importlib.import_module(module_name, __package__)
    function = getattr(somemodule, function_name)
    return function


def get_check_global_params(mode):
    check_params = ['use_gpu', 'max_text_length', 'image_shape',\
        'image_shape', 'character_type', 'loss_type']
    if mode == "train_eval":
        check_params = check_params + [\
            'train_batch_size_per_card', 'test_batch_size_per_card']
    elif mode == "test":
        check_params = check_params + ['test_batch_size_per_card']
    return check_params


def get_check_reader_params(mode):
    check_params = []
    if mode == "train_eval":
        check_params = ['TrainReader', 'EvalReader']
    elif mode == "test":
        check_params = ['TestReader']
    return check_params


from paddle import fluid


def create_multi_devices_program(program, loss_var_name):
    build_strategy = fluid.BuildStrategy()
    build_strategy.memory_optimize = False
    build_strategy.enable_inplace = True
    exec_strategy = fluid.ExecutionStrategy()
    exec_strategy.num_iteration_per_drop_scope = 1
    compile_program = fluid.CompiledProgram(program).with_data_parallel(
        loss_name=loss_var_name,
        build_strategy=build_strategy,
        exec_strategy=exec_strategy)
    return compile_program