fuzzy_match = fuzz.token_set_ratio(a, b) / 100.00

    first_word_match = first_word_exact_match(a, b)

    return max(fuzzy_match, first_word_match)

def records_match(r1: Dict, r2: Dict) -> Tuple[bool, str]:

    """Do these records represent the same transaction?"""

    date_score = date_proximity(r1['date'], r2['date'])

    if r1['date'] == r2['date']:

        date_message = ''

    elif date_score > 0.0:

        diff = abs((r1['date'] - r2['date']).days)

        date_message = f'+/- {diff} days'

    else:

        date_message = 'date mismatch'

    if r1['amount'] == r2['amount']:

        amount_score, amount_message = 2.0, ''

    else:

        amount_score, amount_message = 0.0, 'amount mismatch'

    # We never consider payee if there's a check_id in the books.

    check_message = ''

    payee_message = ''

    if r2['check_id']:

        payee_score = 0.0

        if r1['check_id'] and r2['check_id'] and r1['check_id'] == r2['check_id']:

            check_score = 1.0

        else:

            check_message = 'check-id mismatch'

            check_score = 0.0

    else:

        check_score = 0.0

        payee_score = payee_match(r1['payee'], r2['payee'])

        if payee_score > 0.8:

            payee_message = ''

        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 = []

    # We need a realised list and should be a copy so we can safely delete

    # items.

    books_trans = list(books_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:

            if best_match_score <= 0.8:

                best_match_note.append('only one decent match')

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

            del books_trans[best_match_index]

        else:

            matches.append(([r1], [], ['no match']))

    for r2 in books_trans:

        matches.append(([], [r2], ['no match']))

    return matches