import os
import os.path as osp
import re
from typing import Tuple

import torch

from colossalai.context import Config
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc

__all__ = [
    'get_checkpoint_path',
    'get_latest_checkpoint_path',
    'get_latest_checkpoint_pattern',
    'save_checkpoint',
    'load_checkpoint'
]


def unwrap_config(config: Config):
    '''
    unwrap Config objects to normal dicts
    '''
    config_dict = dict()
    for k, v in config.items():
        if isinstance(v, dict):
            config_dict[k] = unwrap_config(v)
        else:
            config_dict[k] = v

    return config_dict


def _get_ranks_name():
    # tensor parallel
    tp_local_rank = 0
    if gpc.is_initialized(ParallelMode.TENSOR):
        tp_local_rank = gpc.get_local_rank(ParallelMode.TENSOR)

    # pipeline parallel
    pp_local_rank = 0
    if gpc.is_initialized(ParallelMode.PIPELINE):
        pp_local_rank = gpc.get_local_rank(ParallelMode.PIPELINE)

    ranks_name = f'tp{tp_local_rank}-pp{pp_local_rank}'
    return ranks_name


def _get_standard_checkpoint_filename(epoch: int, suffix: str = ''):
    ranks_name = _get_ranks_name()
    return f'epoch{epoch}-{ranks_name}{suffix}.pt'


def get_checkpoint_path(checkpoint_dir: str, epoch: int, suffix: str = ''):
    '''This is a function to generate the checkpoint path from the (checkpoint_dir, epoch, suffix, gpu_parallel_rank) tuple.
    This is useful during generation and recuperation of the checkpoint.

    :param checkpoint_dir: set up a directory for saving checkpoints
    :type checkpoint_dir: str
    :param epoch: epoch number (indicate how many epochs have you trained this model)
    :type epoch: int
    :param suffix: additional notation to specify the model or checkpoint, defaults to ''
    :type suffix: str, optional
    :return: checkpoint path to be generated 
    :rtype: path
    '''
    ckpt_filename = _get_standard_checkpoint_filename(epoch, suffix)
    return os.path.join(checkpoint_dir, ckpt_filename)


def _ensure_directory_exists(filename: str):
    # ensure the directory exists
    dir = os.path.dirname(filename)
    if not os.path.exists(dir):
        os.makedirs(dir)


def get_latest_checkpoint_pattern(suffix: str = ''):
    '''Generate Regular expression of latest checkpoint's pattern

    :param suffix: additional notation to specify the model or checkpoint, defaults to ''
    :type suffix: str, optional
    :return: checkpoint pattern
    :rtype: regular expression
    '''
    ranks_name = _get_ranks_name()
    ckpt_pattern = re.compile(f'epoch(\d+)-{ranks_name}{suffix}\.pt')
    return ckpt_pattern


def get_latest_checkpoint_path(checkpoint_dir: str, suffix: str = ''):
    '''This is a function to retrieve the latest checkpoint path from the (checkpoint_dir, suffix, gpu_parallel_rank) tuple.
    This is useful during recuperation of the checkpoint, especially when you do not know the epoch number.

    :param checkpoint_dir: directory for saving checkpoints
    :type checkpoint_dir: str
    :param suffix: additional notation to specify the model or checkpoint, defaults to ''
    :type suffix: str, optional
    :raises FileNotFoundError: raise error when we cannot find the latest checkpoint file with inputs given
    :return: the latest checkpoint path to be retrieved 
    :rtype: path
    '''
    CKPT_NAME_PAT = get_latest_checkpoint_pattern(suffix=suffix)

    last_epoch = -1
    assert osp.isdir(checkpoint_dir), f'{checkpoint_dir} is not a directory'

    for filename in os.listdir(checkpoint_dir):
        ret = CKPT_NAME_PAT.match(filename)
        if ret:
            epoch = int(ret[0].split('-')[0].lstrip('epoch'))
            if epoch > last_epoch:
                last_epoch = epoch

    if last_epoch == -1:
        ranks_name = _get_ranks_name()
        raise FileNotFoundError(f"Cannot find the latest checkpoint file for {ranks_name} in {checkpoint_dir}")
    else:
        target_file = _get_standard_checkpoint_filename(last_epoch, suffix=suffix)
        path = osp.join(checkpoint_dir, target_file)
        return path


def save_checkpoint(checkpoint_path: str,
                    epoch: int,
                    model: torch.nn.Module,
                    optimizer: torch.optim.Optimizer,
                    lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
                    **kwargs):
    '''Given a directory to store the checkpoints, saves all the training components' parameters or buffers, such as model, optimizer, lr_scheduler and etc. into a checkpoint dictionary. 
    
    This method can be used for both colosalai nn.BaseModel and normal pytorch nn.Module.


    :param checkpoint_path: set up a directory for saving checkpoints
    :type checkpoint_path: str
    :param epoch: epoch number (indicate how many epochs have you trained this model)
    :type epoch: int
    :param model: model to be registered
    :type model: torch.nn.Module
    :param optimizer: optimizer to be registered
    :type optimizer: torch.optim.Optimizer
    :param lr_scheduler: lr_scheduler to be registered, defaults to None
    :type lr_scheduler: torch.optim.lr_scheduler._LRScheduler, optional
    '''
    # for compatibility with normal pytorch nn.Module
    if hasattr(model, 'state_dict_for_save_checkpoint'):
        model_sd = model.state_dict_for_save_checkpoint()
    else:
        model_sd = model.state_dict()

    # ckpt container
    checkpoint = {
        'epoch': epoch,
        'model': model_sd,
        'optimizer': optimizer.state_dict(),
        **kwargs
    }
    if lr_scheduler is not None:
        checkpoint['lr_scheduler'] = lr_scheduler.state_dict()

    _ensure_directory_exists(checkpoint_path)
    torch.save(checkpoint, checkpoint_path)


def load_checkpoint(checkpoint_path: str,
                    model: torch.nn.Module,
                    optimizer: torch.optim.Optimizer,
                    lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
                    finetune: bool = False,
                    strict: bool = True) -> Tuple:
    '''Loads the checkpoint file. 
    If finetune is False, then we intend to continue/resume the training process from the checkpoint given. 
    So we copy parameters and buffers from state_dict into these modules(model, optimizer,lr_scheduler) and its descendants. 
    If finetune is True, then only the weights and buffers of model should be reload.
    If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.
    
    :param checkpoint_path: the exact and matched checkpoint_path directory to retrieve appropriate state_dict
    :type checkpoint_path: str
    :param model: model to reload parameters and buffers
    :type model: torch.nn.Module
    :param optimizer: optimizer to recuperate
    :type optimizer: torch.optim.Optimizer 
    :param lr_scheduler: lr_scheduler to recuperate, defaults to None
    :type lr_scheduler: torch.optim.lr_scheduler._LRScheduler, optional
    :param finetune: whether to finetune the model with new dataset or continue the pre-training, defaults to False
    :type finetune: bool, optional
    :param strict: whether to strictly enforce that the keys in
        :attr:`state_dict` of the checkpoint match the names of
        parameters and buffers in model., defaults to True
    :type strict: bool, optional
    :raises ValueError: raise error if the model/optimizer cannot successfully be recuperated
    :return: (the epoch number of the checkpoint retrieved, the checkpoint retrieved)
    :rtype: Tuple

    '''
    # Load the checkpoint.
    checkpoint = torch.load(checkpoint_path, map_location='cpu')
    try:
        last_epoch = checkpoint.pop('epoch') if not finetune else 0
        model.load_state_dict(checkpoint.pop('model'), strict=strict)
    except KeyError:
        raise ValueError('Checkpoint is corrupted')

    if not finetune:
        try:
            optimizer.load_state_dict(checkpoint.pop('optimizer'))
        except KeyError:
            raise ValueError('Checkpoint is corrupted')

        if lr_scheduler is not None and 'lr_scheduler' in checkpoint:
            lr_scheduler.load_state_dict(checkpoint.pop('lr_scheduler'))

    return last_epoch, checkpoint
