**Apache Airflow version**:
2.0.1

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: 4GB RAM, Processor - Intel(R) Core(TM) i5-8250U CPU @ 1.60GHz
- **OS** (e.g. from /etc/os-release): 18.04.5 LTS (Bionic Beaver)
- **Kernel** (e.g. `uname -a`): Linux 4.15.0-136-generic
- **Install tools**: bare metal installation as per commands given [here](https://airflow.apache.org/docs/apache-airflow/stable/installation.html)

**What happened**:

On running `airflow scheduler -p` got following error:-
```
Traceback (most recent call last):
  File "/home/vineet/Documents/projects/venv/bin/airflow", line 8, in <module>
    sys.exit(main())
  File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/__main__.py", line 40, in main
    args.func(args)
  File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/cli/cli_parser.py", line 48, in command
    return func(*args, **kwargs)
  File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/utils/cli.py", line 86, in wrapper
    metrics = _build_metrics(f.__name__, args[0])
  File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/utils/cli.py", line 118, in _build_metrics
    full_command[idx + 1] = "*" * 8
IndexError: list assignment index out of range
```
As per docs, `-p` flag is a valid flag and gives correct result for `airflow scheduler --do-pickle`

**How to reproduce it**:
Install `airflow` 2.0.1 and run `airflow scheduler -p`
airflow/utils/cli.py
if not args:
        raise ValueError("Args should be set")
    if not isinstance(args[0], Namespace):
        raise ValueError(
            "1st positional argument should be argparse.Namespace instance," f"but is {type(args[0])}"
        )
def action_logging(f: T) -> T:
    """
    Decorates function to execute function at the same time submitting action_logging
    but in CLI context. It will call action logger callbacks twice,
    one for pre-execution and the other one for post-execution.
    Action logger will be called with below keyword parameters:
        sub_command : name of sub-command
        start_datetime : start datetime instance by utc
        end_datetime : end datetime instance by utc
        full_command : full command line arguments
        user : current user
        log : airflow.models.log.Log ORM instance
        dag_id : dag id (optional)
        task_id : task_id (optional)
        execution_date : execution date (optional)
        error : exception instance if there's an exception
    :param f: function instance
    :return: wrapped function
    """
    @functools.wraps(f)
    def wrapper(*args, **kwargs):