# Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings

import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import HOOKS, build_runner
from mmcv.utils import build_from_cfg
from mmseg.core import DistEvalHook, EvalHook
from mmseg.datasets import build_dataloader, build_dataset
from mmseg.utils import get_root_logger

from mmrazor.core.distributed_wrapper import DistributedDataParallelWrapper
from mmrazor.core.optimizer import build_optimizers


def set_random_seed(seed, deterministic=False):
    """Set random seed.

    Args:
        seed (int): Seed to be used.
        deterministic (bool): Whether to set the deterministic option for
            CUDNN backend, i.e., set ``torch.backends.cudnn.deterministic``
            to True and ``torch.backends.cudnn.benchmark`` to False.
            Default: False.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    if deterministic:
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False


def train_segmentor(model,
                    dataset,
                    cfg,
                    distributed=False,
                    validate=False,
                    timestamp=None,
                    meta=None):
    """Copy from mmsegmentation and modify some codes.

    This is an ugly implementation, and will be deprecated in the future. In
    the future, there will be only one train api and no longer distinguish
    between mmclassificaiton, mmsegmentation or mmdetection.
    """
    logger = get_root_logger(cfg.log_level)

    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds,
            cfg.data.samples_per_gpu,
            cfg.data.workers_per_gpu,
            # cfg.gpus will be ignored if distributed
            len(cfg.gpu_ids),
            dist=distributed,
            seed=cfg.seed,
            drop_last=True) for ds in dataset
    ]

    # put model on gpus
    if distributed:
        find_unused_parameters = cfg.get('find_unused_parameters', False)
        if cfg.get('use_ddp_wrapper', False):
            # Difference from mmsegmentation.
            # In some algorithms, the ``optimizer.step()`` is executed in
            # ``train_step``. To rebuilt reducer buckets rightly, there need to
            # use DistributedDataParallelWrapper.
            model = DistributedDataParallelWrapper(
                model,
                device_ids=[torch.cuda.current_device()],
                broadcast_buffers=False,
                find_unused_parameters=find_unused_parameters)
        else:
            # Sets the ``find_unused_parameters`` parameter in
            # torch.nn.parallel.DistributedDataParallel
            model = MMDistributedDataParallel(
                model.cuda(),
                device_ids=[torch.cuda.current_device()],
                broadcast_buffers=False,
                find_unused_parameters=find_unused_parameters)
    else:
        model = MMDataParallel(
            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)

    # build optimizers
    # Difference from mmdetection.
    # In some algorithms, there will be multi optimizers.
    optimizer = build_optimizers(model, cfg.optimizer)

    # build runner
    if cfg.get('runner') is None:
        cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
        warnings.warn(
            'config is now expected to have a ``runner`` section, '
            'please set ``runner`` in your config.', UserWarning)

    runner = build_runner(
        cfg.runner,
        default_args=dict(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=cfg.work_dir,
            logger=logger,
            meta=meta))

    # register hooks
    runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config,
                                   cfg.get('momentum_config', None))

    # an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp

    # register eval hooks
    if validate:
        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
        val_dataloader = build_dataloader(
            val_dataset,
            samples_per_gpu=1,
            workers_per_gpu=cfg.data.workers_per_gpu,
            dist=distributed,
            shuffle=False)
        eval_cfg = cfg.get('evaluation', {})
        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
        eval_hook = DistEvalHook if distributed else EvalHook
        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
        runner.register_hook(
            eval_hook(val_dataloader, **eval_cfg), priority='LOW')

    # user-defined hooks
    if cfg.get('custom_hooks', None):
        custom_hooks = cfg.custom_hooks
        assert isinstance(custom_hooks, list), \
            f'custom_hooks expect list type, but got {type(custom_hooks)}'
        for hook_cfg in cfg.custom_hooks:
            assert isinstance(hook_cfg, dict), \
                'Each item in custom_hooks expects dict type, but got ' \
                f'{type(hook_cfg)}'
            hook_cfg = hook_cfg.copy()
            priority = hook_cfg.pop('priority', 'NORMAL')
            hook = build_from_cfg(hook_cfg, HOOKS)
            runner.register_hook(hook, priority=priority)

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow)
