blink/candidate_ranking/utils.py [91:141]:
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def remove_module_from_state_dict(state_dict):
    new_state_dict = OrderedDict()
    for key, value in state_dict.items():
        name = "".join(key.split(".module"))
        new_state_dict[name] = value
    return new_state_dict


def save_model(model, tokenizer, output_dir):
    """Saves the model and the tokenizer used in the output directory."""
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    model_to_save = model.module if hasattr(model, "module") else model
    output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
    output_config_file = os.path.join(output_dir, CONFIG_NAME)
    torch.save(model_to_save.state_dict(), output_model_file)
    model_to_save.config.to_json_file(output_config_file)
    tokenizer.save_vocabulary(output_dir)


def get_logger(output_dir=None):
    if output_dir != None:
        os.makedirs(output_dir, exist_ok=True)
        logging.basicConfig(
            format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
            datefmt="%m/%d/%Y %H:%M:%S",
            level=logging.INFO,
            handlers=[
                logging.FileHandler(
                    "{}/log.txt".format(output_dir), mode="a", delay=False
                ),
                logging.StreamHandler(sys.stdout),
            ],
        )
    else:
        logging.basicConfig(
            format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
            datefmt="%m/%d/%Y %H:%M:%S",
            level=logging.INFO,
            handlers=[logging.StreamHandler(sys.stdout)],
        )

    logger = logging.getLogger('Blink')
    logger.setLevel(10)
    return logger


def write_to_file(path, string, mode="w"):
    with open(path, mode) as writer:
        writer.write(string)
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elq/candidate_ranking/utils.py [43:93]:
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def remove_module_from_state_dict(state_dict):
    new_state_dict = OrderedDict()
    for key, value in state_dict.items():
        name = "".join(key.split(".module"))
        new_state_dict[name] = value
    return new_state_dict


def save_model(model, tokenizer, output_dir):
    """Saves the model and the tokenizer used in the output directory."""
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    model_to_save = model.module if hasattr(model, "module") else model
    output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
    output_config_file = os.path.join(output_dir, CONFIG_NAME)
    torch.save(model_to_save.state_dict(), output_model_file)
    model_to_save.config.to_json_file(output_config_file)
    tokenizer.save_vocabulary(output_dir)


def get_logger(output_dir=None):
    if output_dir != None:
        os.makedirs(output_dir, exist_ok=True)
        logging.basicConfig(
            format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
            datefmt="%m/%d/%Y %H:%M:%S",
            level=logging.INFO,
            handlers=[
                logging.FileHandler(
                    "{}/log.txt".format(output_dir), mode="a", delay=False
                ),
                logging.StreamHandler(sys.stdout),
            ],
        )
    else:
        logging.basicConfig(
            format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
            datefmt="%m/%d/%Y %H:%M:%S",
            level=logging.INFO,
            handlers=[logging.StreamHandler(sys.stdout)],
        )

    logger = logging.getLogger('Blink')
    logger.setLevel(10)
    return logger


def write_to_file(path, string, mode="w"):
    with open(path, mode) as writer:
        writer.write(string)
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