import os
import logging
import wandb
import torch

def set_logging(name=None, verbose=True):
    for handler in logging.root.handlers[:]:
        logging.root.removeHandler(handler)
    # Sets level and returns logger
    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
    logging.basicConfig(format="%(message)s", level=logging.INFO if (verbose and rank in (-1, 0)) else logging.WARNING)
    return logging.getLogger(name)

LOGGER = set_logging(__name__)  # define globally (used in train.py, val.py, detect.py, etc.)

LOGGERS = ('csv', 'tb', 'wandb')

CUDA = True if torch.cuda.is_available() else False
DEVICE = 'cuda' if CUDA else 'cpu'

LOGGER_WANDB = 'wandb'
LOGGER_TENSORBOARD = 'tb'


class Loggers():
    def __init__(self, hyp):
        self.type = hyp['logger']['type']
        self.epochs = hyp['train']['epochs']
        self.wandb = None
        self.writer = None
        if self.type == LOGGER_WANDB:
            if hyp['logger']['project'] == '':
                project = 'ComicTextDetector'
            else:
                project = hyp['logger']['project']
            if hyp['logger']['run_id'] == '':
                self.wandb = wandb.init(project=project, config=hyp, resume='allow')
            else:
                self.wandb = wandb.init(project=project, config=hyp, resume='must', id=hyp['logger']['run_id'])
        elif self.type == LOGGER_TENSORBOARD:
            from torch.utils.tensorboard import SummaryWriter
            self.writer = SummaryWriter(hyp['data']['save_dir'])

    def on_train_batch_end(self, metrics):
        # Callback runs on train batch end
        if self.wandb:
            self.wandb.log(metrics)
        pass

    def on_train_epoch_end(self, epoch, metrics):
        LOGGER.info(f'fin epoch {epoch}/{self.epochs}, metrics: {metrics}')
        if self.type == LOGGER_WANDB:
            self.wandb.log(metrics)
        elif self.type == LOGGER_TENSORBOARD:
            for key in metrics.keys():
                self.writer.add_scalar(key, metrics[key], epoch)

    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
        # Callback runs on model save event
        if self.wandb:
            if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
                self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)