# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# 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 abc import ABCMeta, abstractmethod
from logging import getLogger
from pathlib import Path
from typing import Union, Optional

from deeppavlov.core.commands.utils import expand_path

log = getLogger(__name__)


class Serializable(metaclass=ABCMeta):
    """Abstract base class that expresses the interface for all models that can serialize data to a path."""

    def __init__(self, save_path: Optional[Union[str, Path]], load_path: Optional[Union[str, Path]] = None,
                 mode: str = 'infer',
                 *args, **kwargs) -> None:

        if save_path:
            self.save_path = expand_path(save_path)
            self.save_path.parent.mkdir(parents=True, exist_ok=True)
        else:
            self.save_path = None

        if load_path:
            self.load_path = expand_path(load_path)
            if mode != 'train' and self.save_path and self.load_path != self.save_path:
                log.warning("Load path '{}' differs from save path '{}' in '{}' mode for {}."
                            .format(self.load_path, self.save_path, mode, self.__class__.__name__))
        elif mode != 'train' and self.save_path:
            self.load_path = self.save_path
            log.warning("No load path is set for {} in '{}' mode. Using save path instead"
                        .format(self.__class__.__name__, mode))
        else:
            self.load_path = None
            log.warning("No load path is set for {}!".format(self.__class__.__name__))

    @abstractmethod
    def save(self, *args, **kwargs):
        pass

    @abstractmethod
    def load(self, *args, **kwargs):
        pass
