# Copyright (c) OpenMMLab. All rights reserved.
import logging
import random
from typing import List, Optional, Tuple

import numpy as np
import torch
from torch.utils.data import DataLoader

from mmengine.device import is_cuda_available, is_musa_available
from mmengine.dist import get_rank, sync_random_seed
from mmengine.logging import print_log
from mmengine.utils import digit_version, is_list_of
from mmengine.utils.dl_utils import TORCH_VERSION


def calc_dynamic_intervals(
    start_interval: int,
    dynamic_interval_list: Optional[List[Tuple[int, int]]] = None
) -> Tuple[List[int], List[int]]:
    """Calculate dynamic intervals.

    Args:
        start_interval (int): The interval used in the beginning.
        dynamic_interval_list (List[Tuple[int, int]], optional): The
            first element in the tuple is a milestone and the second
            element is a interval. The interval is used after the
            corresponding milestone. Defaults to None.

    Returns:
        Tuple[List[int], List[int]]: a list of milestone and its corresponding
        intervals.
    """
    if dynamic_interval_list is None:
        return [0], [start_interval]

    assert is_list_of(dynamic_interval_list, tuple)

    dynamic_milestones = [0]
    dynamic_milestones.extend(
        [dynamic_interval[0] for dynamic_interval in dynamic_interval_list])
    dynamic_intervals = [start_interval]
    dynamic_intervals.extend(
        [dynamic_interval[1] for dynamic_interval in dynamic_interval_list])
    return dynamic_milestones, dynamic_intervals


def set_random_seed(seed: Optional[int] = None,
                    deterministic: bool = False,
                    diff_rank_seed: bool = False) -> int:
    """Set random seed.

    Args:
        seed (int, optional): 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.
            Defaults to False.
        diff_rank_seed (bool): Whether to add rank number to the random seed to
            have different random seed in different threads. Defaults to False.
    """
    if seed is None:
        seed = sync_random_seed()

    if diff_rank_seed:
        rank = get_rank()
        seed += rank

    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    # torch.cuda.manual_seed(seed)
    if is_cuda_available():
        torch.cuda.manual_seed_all(seed)
    elif is_musa_available():
        torch.musa.manual_seed_all(seed)
    # os.environ['PYTHONHASHSEED'] = str(seed)
    if deterministic:
        if torch.backends.cudnn.benchmark:
            print_log(
                'torch.backends.cudnn.benchmark is going to be set as '
                '`False` to cause cuDNN to deterministically select an '
                'algorithm',
                logger='current',
                level=logging.WARNING)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

        if digit_version(TORCH_VERSION) >= digit_version('1.10.0'):
            torch.use_deterministic_algorithms(True)
    return seed


def _get_batch_size(dataloader: dict):
    if isinstance(dataloader, dict):
        if 'batch_size' in dataloader:
            return dataloader['batch_size']
        elif ('batch_sampler' in dataloader
              and 'batch_size' in dataloader['batch_sampler']):
            return dataloader['batch_sampler']['batch_size']
        else:
            raise ValueError('Please set batch_size in `Dataloader` or '
                             '`batch_sampler`')
    elif isinstance(dataloader, DataLoader):
        return dataloader.batch_sampler.batch_size
    else:
        raise ValueError('dataloader should be a dict or a Dataloader '
                         f'instance, but got {type(dataloader)}')

# Copyright (c) OpenMMLab. All rights reserved.
import logging
import random
from typing import List, Optional, Tuple

import numpy as np
import torch
from torch.utils.data import DataLoader

from mmengine.dist import get_rank, sync_random_seed
from mmengine.logging import print_log
from mmengine.utils import digit_version, is_list_of
from mmengine.utils.dl_utils import TORCH_VERSION


def calc_dynamic_intervals(
    start_interval: int,
    dynamic_interval_list: Optional[List[Tuple[int, int]]] = None
) -> Tuple[List[int], List[int]]:
    """Calculate dynamic intervals.

    Args:
        start_interval (int): The interval used in the beginning.
        dynamic_interval_list (List[Tuple[int, int]], optional): The
            first element in the tuple is a milestone and the second
            element is a interval. The interval is used after the
            corresponding milestone. Defaults to None.

    Returns:
        Tuple[List[int], List[int]]: a list of milestone and its corresponding
        intervals.
    """
    if dynamic_interval_list is None:
        return [0], [start_interval]

    assert is_list_of(dynamic_interval_list, tuple)

    dynamic_milestones = [0]
    dynamic_milestones.extend(
        [dynamic_interval[0] for dynamic_interval in dynamic_interval_list])
    dynamic_intervals = [start_interval]
    dynamic_intervals.extend(
        [dynamic_interval[1] for dynamic_interval in dynamic_interval_list])
    return dynamic_milestones, dynamic_intervals


def set_random_seed(seed: Optional[int] = None,
                    deterministic: bool = False,
                    diff_rank_seed: bool = False) -> int:
    """Set random seed.

    Args:
        seed (int, optional): 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.
            Defaults to False.
        diff_rank_seed (bool): Whether to add rank number to the random seed to
            have different random seed in different threads. Defaults to False.
    """
    if seed is None:
        seed = sync_random_seed()

    if diff_rank_seed:
        rank = get_rank()
        seed += rank

    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    # torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    # os.environ['PYTHONHASHSEED'] = str(seed)
    if deterministic:
        if torch.backends.cudnn.benchmark:
            print_log(
                'torch.backends.cudnn.benchmark is going to be set as '
                '`False` to cause cuDNN to deterministically select an '
                'algorithm',
                logger='current',
                level=logging.WARNING)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

        if digit_version(TORCH_VERSION) >= digit_version('1.10.0'):
            torch.use_deterministic_algorithms(True)
    return seed


def _get_batch_size(dataloader: dict):
    if isinstance(dataloader, dict):
        if 'batch_size' in dataloader:
            return dataloader['batch_size']
        elif ('batch_sampler' in dataloader
              and 'batch_size' in dataloader['batch_sampler']):
            return dataloader['batch_sampler']['batch_size']
        else:
            raise ValueError('Please set batch_size in `Dataloader` or '
                             '`batch_sampler`')
    elif isinstance(dataloader, DataLoader):
        return dataloader.batch_sampler.batch_size
    else:
        raise ValueError('dataloader should be a dict or a Dataloader '
                         f'instance, but got {type(dataloader)}')
