def validate_chase_csv(sample: str) -> None:

    required_cols = {'Date', 'Description', 'Account', 'Transaction Type', 'Amount'}

    reader = csv.DictReader(io.StringIO(sample))

    if reader.fieldnames and not required_cols.issubset(reader.fieldnames):

        sys.exit(

            f"This Chase CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}. Please use an unmodified statement direct from the institution."

        )

def standardize_chase_record(row: Dict, line: int) -> Dict:

    """Turn an Chase CSV row into a standard dict format representing a transaction."""

    return {

        'date': datetime.datetime.strptime(row['Date'], '%m/%d/%y').date(),

        'amount': -1 * parse_amount(row['Amount']),

        # Descriptions have quite a lot of information, but the format is a little

        # idiosyncratic. We'll need to see more examples before coming up with any ways

        # to handle it in code. Others have used regular expressions to match the

        # various transaction types:

        # https://github.com/mtlynch/beancount-chase-bank/blob/master/beancount_chase/checking.py

        # See also: https://awesome-beancount.com/

        'payee': (row['Description'] or '').replace('ORIG CO NAME:')[:20],

        'check_id': '',

        'line': line,

    }

def read_chase_csv(f: TextIO) -> list:

    reader = csv.DictReader(f)

    # The reader.line_num is the source line number, not the spreadsheet row

    # number due to multi-line records.

    return sort_records(

        [standardize_chase_record(row, i) for i, row in enumerate(reader, 2)]

    )

    elif 'FR' in args.account:

    elif 'Chase' in args.account:

        validate_csv = validate_chase_csv

        read_csv = read_chase_csv

    else:

        sys.exit("This account provided doesn't match one of AMEX, FR or Chase.")

version = 1.20.0