# Copyright (c) Open-MMLab. All rights reserved.
import apex
import os.path as osp
import time
from tempfile import TemporaryDirectory

import torch
from torch.optim import Optimizer

import mmcv
from mmcv.parallel import is_module_wrapper
from mmcv.runner.checkpoint import weights_to_cpu, get_state_dict

def save_checkpoint(model, filename, optimizer=None, meta=None, amp=False):
    """Save checkpoint to file.

    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
    ``optimizer``. By default ``meta`` will contain version and time info.

    Args:
        model (Module): Module whose params are to be saved.
        filename (str): Checkpoint filename.
        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
        meta (dict, optional): Metadata to be saved in checkpoint.
    """
    if meta is None:
        meta = {}
    elif not isinstance(meta, dict):
        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())

    if is_module_wrapper(model):
        model = model.module

    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
        # save class name to the meta
        meta.update(CLASSES=model.CLASSES)

    checkpoint = {
        'meta': meta,
        'state_dict': weights_to_cpu(get_state_dict(model))
    }
    # save optimizer state dict in the checkpoint
    if isinstance(optimizer, Optimizer):
        checkpoint['optimizer'] = optimizer.state_dict()
    elif isinstance(optimizer, dict):
        checkpoint['optimizer'] = {}
        for name, optim in optimizer.items():
            checkpoint['optimizer'][name] = optim.state_dict()

    # save amp state dict in the checkpoint
    if amp:
        checkpoint['amp'] = apex.amp.state_dict()

    if filename.startswith('pavi://'):
        try:
            from pavi import modelcloud
            from pavi.exception import NodeNotFoundError
        except ImportError:
            raise ImportError(
                'Please install pavi to load checkpoint from modelcloud.')
        model_path = filename[7:]
        root = modelcloud.Folder()
        model_dir, model_name = osp.split(model_path)
        try:
            model = modelcloud.get(model_dir)
        except NodeNotFoundError:
            model = root.create_training_model(model_dir)
        with TemporaryDirectory() as tmp_dir:
            checkpoint_file = osp.join(tmp_dir, model_name)
            with open(checkpoint_file, 'wb') as f:
                torch.save(checkpoint, f)
                f.flush()
            model.create_file(checkpoint_file, name=model_name)
    else:
        mmcv.mkdir_or_exist(osp.dirname(filename))
        # immediately flush buffer
        with open(filename, 'wb') as f:
            torch.save(checkpoint, f)
            f.flush()
