def setup(mode, P):
    fname = f'{P.dataset}_{P.model}_unsup_{mode}_{P.res}'

    if mode == 'simclr':
        from .simclr import train
    elif mode == 'simclr_CSI':
        from .simclr_CSI import train
        fname += f'_shift_{P.shift_trans_type}_resize_factor{P.resize_factor}_color_dist{P.color_distort}'
        if P.shift_trans_type == 'gauss':
            fname += f'_gauss_sigma{P.gauss_sigma}'
        elif P.shift_trans_type == 'randpers':
            fname += f'_distortion_scale{P.distortion_scale}'
        elif P.shift_trans_type == 'sharp':
            fname += f'_sharpness_factor{P.sharpness_factor}'
        elif P.shift_trans_type == 'sharp':
            fname += f'_nmean_{P.noise_mean}_nstd_{P.noise_std}'
    else:
        raise NotImplementedError()

    if P.one_class_idx is not None:
        fname += f'_one_class_{P.one_class_idx}'

    if P.suffix is not None:
        fname += f'_{P.suffix}'

    return train, fname


def update_comp_loss(loss_dict, loss_in, loss_out, loss_diff, batch_size):
    loss_dict['pos'].update(loss_in, batch_size)
    loss_dict['neg'].update(loss_out, batch_size)
    loss_dict['diff'].update(loss_diff, batch_size)


def summary_comp_loss(logger, tag, loss_dict, epoch):
    logger.scalar_summary(f'{tag}/pos', loss_dict['pos'].average, epoch)
    logger.scalar_summary(f'{tag}/neg', loss_dict['neg'].average, epoch)
    logger.scalar_summary(f'{tag}', loss_dict['diff'].average, epoch)