import os
import contextlib
import joblib
from typing import Union
from loguru import _Logger, logger
from itertools import chain

import torch
from yacs.config import CfgNode as CN
from pytorch_lightning.utilities import rank_zero_only


def lower_config(yacs_cfg):
    if not isinstance(yacs_cfg, CN):
        return yacs_cfg
    return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}


def upper_config(dict_cfg):
    if not isinstance(dict_cfg, dict):
        return dict_cfg
    return {k.upper(): upper_config(v) for k, v in dict_cfg.items()}


def log_on(condition, message, level):
    if condition:
        assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL']
        logger.log(level, message)


def get_rank_zero_only_logger(logger: _Logger):
    if rank_zero_only.rank == 0:
        return logger
    else:
        for _level in logger._core.levels.keys():
            level = _level.lower()
            setattr(logger, level,
                    lambda x: None)
        logger._log = lambda x: None
    return logger


def setup_gpus(gpus: Union[str, int]) -> int:
    """ A temporary fix for pytorch-lighting 1.3.x """
    gpus = str(gpus)
    gpu_ids = []
    
    if ',' not in gpus:
        n_gpus = int(gpus)
        return n_gpus if n_gpus != -1 else torch.cuda.device_count()
    else:
        gpu_ids = [i.strip() for i in gpus.split(',') if i != '']
    
    # setup environment variables
    visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
    if visible_devices is None:
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids)
        visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
        logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}')
    else:
        logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.')
    return len(gpu_ids)


def flattenList(x):
    return list(chain(*x))


@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
    """Context manager to patch joblib to report into tqdm progress bar given as argument
    
    Usage:
        with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
            Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))
            
    When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing)
        ret_vals = Parallel(n_jobs=args.world_size)(
                    delayed(lambda x: _compute_cov_score(pid, *x))(param)
                        for param in tqdm(combinations(image_ids, 2),
                                          desc=f'Computing cov_score of [{pid}]',
                                          total=len(image_ids)*(len(image_ids)-1)/2))
    Src: https://stackoverflow.com/a/58936697
    """
    class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)

        def __call__(self, *args, **kwargs):
            tqdm_object.update(n=self.batch_size)
            return super().__call__(*args, **kwargs)

    old_batch_callback = joblib.parallel.BatchCompletionCallBack
    joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback
    try:
        yield tqdm_object
    finally:
        joblib.parallel.BatchCompletionCallBack = old_batch_callback
        tqdm_object.close()

def detect_NaN(feat_0, feat_1):
    logger.info(f'NaN detected in feature')
    logger.info(f"#NaN in feat_0: {torch.isnan(feat_0).int().sum()}, #NaN in feat_1: {torch.isnan(feat_1).int().sum()}")
    feat_0[torch.isnan(feat_0)] = 0
    feat_1[torch.isnan(feat_1)] = 0