payee = ' '.join([i for i in payee.split(' ') if len(i) > 2])

    payee = payee.replace('-', ' ')

    payee = remove_duplicate_words(payee)

    payee.strip()

    return payee

# NOTE: Statement doesn't seem to give us a running balance or a final total.

def read_transactions_from_csv(f: TextIO, standardize_statement_record: Callable) -> list:

    reader = csv.DictReader(f)

    return sort_records([standardize_statement_record(row, reader.line_num) for row in reader])

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

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

    return {

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

        'amount': -1 * decimal.Decimal(row['Amount']),

        # Descriptions have too much noise, so taking just the start

        # significantly assists the fuzzy matching.

        'payee': remove_payee_junk(row['Description'] or '')[:20],

        'check_id': '',

        'line': line,

    }

        elif payee_score > 0.4:

            payee_message = 'partial payee match'

        else:

            payee_message = 'payee mismatch'

    overall_score = (date_score + amount_score + check_score + payee_score) / 4

    overall_message = [m for m in [date_message, amount_message, check_message, payee_message] if m]

    return overall_score, overall_message

def match_statement_and_books(statement_trans: list, books_trans: list):

    """

    Runs through all the statement transactions to find a matching transaction

    in the books. If found, the books transaction is marked off so that it can

    only be matched once. Some transactions will be matched, some will be on the

    statement but not the books and some on the books but not the statement.

    """

    matches = []

    remaining_books_trans = []

    remaining_statement_trans = []

    for r1 in statement_trans:

        best_match_score = 0

        best_match_index = None

        best_match_note = ''

        matches_found = 0

        for i, r2 in enumerate(books_trans):

            score, note = records_match(r1, r2)

            if score >= 0.5 and score >= best_match_score:

                matches_found += 1

                best_match_score = score

                best_match_index = i

                best_match_note = note

        if best_match_score > 0.5 and matches_found == 1 and 'check-id mismatch' not in best_match_note or best_match_score > 0.8:

            matches.append(([r1], [books_trans[best_match_index]], best_match_note))

            # Don't try to make a second match against this books entry.

            del books_trans[best_match_index]

        else:

            remaining_statement_trans.append(r1)

    for r2 in books_trans:

        remaining_books_trans.append(r2)

    return matches, remaining_statement_trans, remaining_books_trans

def format_matches(matches, csv_statement: str, show_reconciled_matches):

    match_output = []