# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 os
import numpy as np

from ..dist_utils import allreduce_params, master_only
from .hook import HOOKS, Hook


@HOOKS.register_module()
class CheckpointHook(Hook):
    """Save checkpoints periodically.

    Args:
        interval (int): The saving period. If ``by_epoch=True``, interval
            indicates epochs, otherwise it indicates iterations.
            Default: -1, which means "never".
        by_epoch (bool): Saving checkpoints by epoch or by iteration.
            Default: True.
        save_optimizer (bool): Whether to save optimizer state_dict in the
            checkpoint. It is usually used for resuming experiments.
            Default: True.
        out_dir (str, optional): The directory to save checkpoints. If not
            specified, ``runner.work_dir`` will be used by default.
        max_keep_ckpts (int, optional): The maximum checkpoints to keep.
            In some cases we want only the latest few checkpoints and would
            like to delete old ones to save the disk space.
            Default: -1, which means unlimited.
        save_last (bool): Whether to force the last checkpoint to be saved
            regardless of interval.
        sync_buffer (bool): Whether to synchronize buffers in different
            gpus. Default: False.
    """
    def __init__(self,
                 interval=-1,
                 by_epoch=True,
                 save_optimizer=True,
                 out_dir=None,
                 max_keep_ckpts=-1,
                 save_last=True,
                 sync_buffer=False,
                 **kwargs):
        self.interval = interval
        self.by_epoch = by_epoch
        self.save_optimizer = save_optimizer
        self.out_dir = out_dir
        self.max_keep_ckpts = max_keep_ckpts
        self.save_last = save_last
        self.args = kwargs
        self.sync_buffer = sync_buffer

    def before_run(self, runner):
        if not self.out_dir:
            self.out_dir = runner.work_dir

    def after_train_epoch(self, runner):
        if not self.by_epoch:
            return

        aver_fps = runner.log_buffer.val_history['fps'][5:-1]
        aver_fps = np.average(aver_fps)
        runner.logger.info(
            f'Average FPS: {aver_fps} (ignore the first 5 steps)')

        # save checkpoint for following cases:
        # 1. every ``self.interval`` epochs
        # 2. reach the last epoch of training
        if self.every_n_epochs(
                runner, self.interval) or (self.save_last
                                           and self.is_last_epoch(runner)):
            runner.logger.info(
                f'Saving checkpoint at {runner.epoch + 1} epochs')

            if self.sync_buffer:
                allreduce_params(runner.model.buffers())
            self._save_checkpoint(runner)

    @master_only
    def _save_checkpoint(self, runner):
        """Save the current checkpoint and delete unwanted checkpoint."""
        runner.save_checkpoint(self.out_dir,
                               save_optimizer=self.save_optimizer,
                               **self.args)
        if runner.meta is not None:
            if self.by_epoch:
                cur_ckpt_filename = self.args.get(
                    'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
            else:
                cur_ckpt_filename = self.args.get(
                    'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
            runner.meta.setdefault('hook_msgs', dict())
            runner.meta['hook_msgs']['last_ckpt'] = os.path.join(
                self.out_dir, cur_ckpt_filename)
        # remove other checkpoints
        if self.max_keep_ckpts > 0:
            if self.by_epoch:
                name = 'epoch_{}.pth'
                current_ckpt = runner.epoch + 1
            else:
                name = 'iter_{}.pth'
                current_ckpt = runner.iter + 1
            redundant_ckpts = range(
                current_ckpt - self.max_keep_ckpts * self.interval, 0,
                -self.interval)
            filename_tmpl = self.args.get('filename_tmpl', name)
            for _step in redundant_ckpts:
                ckpt_path = os.path.join(self.out_dir,
                                         filename_tmpl.format(_step))
                if os.path.exists(ckpt_path):
                    os.remove(ckpt_path)
                else:
                    break

    def after_train_iter(self, runner):
        if self.by_epoch:
            return

        # save checkpoint for following cases:
        # 1. every ``self.interval`` iterations
        # 2. reach the last iteration of training
        if self.every_n_iters(
                runner, self.interval) or (self.save_last
                                           and self.is_last_iter(runner)):
            runner.logger.info(
                f'Saving checkpoint at {runner.iter + 1} iterations')
            if self.sync_buffer:
                allreduce_params(runner.model.buffers())
            self._save_checkpoint(runner)