return record

def read_fr_csv(f: TextIO) -> list:

    reader = csv.reader(f)

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

    # number due to multi-line records.

    return sort_records(

        standardize_fr_record(i, row) for i, row in enumerate(reader, 1)

        if len(row) == 6 and row[2] not in {'LAST STATEMENT', 'THIS STATEMENT'}

    )

def standardize_beancount_record(row) -> Dict:  # type: ignore[no-untyped-def]

    """Turn a Beancount query result row into a standard dict representing a transaction."""

    return {

        'date': row.date,

        'amount': row.number_cost_position,

        'payee': remove_payee_junk(f'{row.payee or ""} {row.entity or ""} {row.narration or ""}'),

        'check_id': str(row.check_id or ''),

        'filename': row.filename,

        'line': row.line,

        'bank_statement': row.bank_statement,

    }

def format_record(record: dict) -> str:

    """Generate output lines for a standard 1:1 match."""

    if record['payee'] and record['check_id']:

        output = f"{record['date'].isoformat()}: {record['amount']:11,.2f} {record['payee'][:25]} #{record['check_id']}".ljust(59)

    elif record['payee']:

        output = f"{record['date'].isoformat()}: {record['amount']:11,.2f} {record['payee'][:35]}".ljust(59)

    else:

        output = f"{record['date'].isoformat()}: {record['amount']:11,.2f} #{record['check_id']}".ljust(59)

    return output

def format_multirecord(r1s: List[dict], r2s: List[dict], note: str) -> List[list]:

    """Generates output lines for one statement:multiple books transaction match."""

    assert len(r1s) == 1

    assert len(r2s) > 1

    match_output = []

    match_output.append([r1s[0]['date'], f'{format_record(r1s[0])}  →  {format_record(r2s[0])}  ✓ Matched{note}'])

    for r2 in r2s[1:]:

        match_output.append([r1s[0]['date'], f'{r1s[0]["date"].isoformat()}:             ↳                                    →  {format_record(r2)}  ✓ Matched{note}'])

    return match_output

def sort_records(records: List) -> List:

    return sorted(records, key=lambda x: (x['date'], x['amount']))

def first_word_exact_match(a: str, b: str) -> float:

    """Score a payee match based first word.

    We get a whole lot of good matches this way. Helps in the

    situation where the first word or two of a transaction description

    is useful and the rest is garbage.

    """

    if len(a) == 0 or len(b) == 0:

        return 0.0

    first_a = a.split()[0].strip()

    first_b = b.split()[0].strip()

    if first_a.casefold() == first_b.casefold():

        return min(1.0, 0.2 * len(first_a))

    else:

        return 0.0

def payee_match(a: str, b: str) -> float:

    """Score a match between two payees."""

    fuzzy_match = float(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[float, List[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 = ''

    CSV and PDF statement metadata should be relative to

    CONSERVANCY_REPOSITORY ie. without regards to exactly where on

    your computer all the files live.

    """

    return os.path.relpath(path, start=os.getenv('CONSERVANCY_REPOSITORY'))

def parse_path(path: str) -> str:

    """Validate that a file exists for use in argparse."""

    if not os.path.exists(path):

        raise argparse.ArgumentTypeError(f'File {path} does not exist.')

    return path

def parse_repo_relative_path(path: str) -> str:

    """Validate that a file exists and is within $CONSERVANCY_REPOSITORY.

    For use with argparse.

    """

    if not os.path.exists(path):

        raise argparse.ArgumentTypeError(f'File {path} does not exist.')

    repo = os.getenv('CONSERVANCY_REPOSITORY')

    if not repo:

        raise argparse.ArgumentTypeError('$CONSERVANCY_REPOSITORY is not set.')

    if not path.startswith(repo):

        raise argparse.ArgumentTypeError(f'File {path} does not share a common prefix with $CONSERVANCY_REPOSITORY {repo}.')

    return path

def parse_decimal_with_separator(number_text: str) -> decimal.Decimal:

    """decimal.Decimal can't parse numbers with thousands separator."""

    number_text = number_text.replace(',', '')

    return decimal.Decimal(number_text)

def parse_arguments(argv: List[str]) -> argparse.Namespace:

    parser = argparse.ArgumentParser(prog=PROGNAME, description='Reconciliation helper')

    cliutil.add_version_argument(parser)

    cliutil.add_loglevel_argument(parser)

    parser.add_argument('--beancount-file', required=True, type=parse_path)

    parser.add_argument('--csv-statement', required=True, type=parse_repo_relative_path)

    parser.add_argument('--bank-statement', required=True, type=parse_repo_relative_path)

    parser.add_argument('--account', required=True, help='eg. Liabilities:CreditCard:AMEX')

    # parser.add_argument('--report-group-regex')

    parser.add_argument('--show-reconciled-matches', action='store_true')

    parser.add_argument('--non-interactive', action='store_true', help="Don't prompt to write to the books")    # parser.add_argument('--statement-balance', type=parse_decimal_with_separator, required=True, help="A.K.A \"cleared balance\" taken from the end of the period on the PDF statement. Required because CSV statements don't include final or running totals")

    args = parser.parse_args(args=argv)

    return args

def totals(matches: List[Tuple[List, List, List]]) -> Tuple[decimal.Decimal, decimal.Decimal, decimal.Decimal]:

    """Calculate the totals of transactions matched/not-matched."""

    total_matched = decimal.Decimal(0)

    total_missing_from_books = decimal.Decimal(0)

    total_missing_from_statement = decimal.Decimal(0)

    for statement_entries, books_entries, _ in matches:

        if statement_entries and books_entries:

            total_matched += sum(c['amount'] for c in statement_entries)

        elif statement_entries:

            total_missing_from_books += sum(c['amount'] for c in statement_entries)

        else:

            total_missing_from_statement += sum(c['amount'] for c in books_entries)

    return total_matched, total_missing_from_books, total_missing_from_statement

def process_unmatched(statement_trans: List[dict], books_trans: List[dict]) -> List[Tuple[List, List, List]]:

    """Format the remaining unmatched transactions to be added to one single list of matches."""

    matches: List[Tuple[List, List, List]] = []

    for r1 in statement_trans:

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

    for r2 in books_trans:

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

    return matches

def format_output(matches, begin_date, end_date, csv_statement, show_reconciled_matches) -> str:

    with io.StringIO() as out:

        match_output = format_matches(matches, csv_statement, show_reconciled_matches)

        _, total_missing_from_books, total_missing_from_statement = totals(matches)

        print('-' * 155, file=out)

        statement_heading = f'Statement transactions {begin_date} to {end_date}'

        print(f'{statement_heading:<52}            {"Books transactions":<58}   Notes', file=out)

        print('-' * 155, file=out)

        for _, output in sorted(match_output, key=lambda x: x[0]):

            print(output, file=out)

        print('-' * 155, file=out)

        print(f'Sub-total not on statement: {total_missing_from_statement:12,.2f}', file=out)

        print(f'Sub-total not in books:     {total_missing_from_books:12,.2f}', file=out)

        print(f'Total:                      {total_missing_from_statement + total_missing_from_books:12,.2f}', file=out)

        print('-' * 155, file=out)

        return out.getvalue()

def main(arglist: Optional[Sequence[str]] = None,

         stdout: TextIO = sys.stdout,

         stderr: TextIO = sys.stderr,

         config: Optional[configmod.Config] = None,

         ) -> int:

    args = parse_arguments(arglist)

    cliutil.set_loglevel(logger, args.loglevel)

    if config is None:

        config = configmod.Config()

        config.load_file()

    # Validate and normalise the statement into our standard

    # transaction data structure.

    if 'AMEX' in args.account:

        validate_csv = validate_amex_csv

        read_csv = read_amex_csv

    else:

        validate_csv = validate_fr_csv

        read_csv = read_fr_csv

    with open(args.csv_statement) as f:

        sample = f.read(200)

        # Validate should return true/false and a message.

        validate_csv(sample)

        f.seek(0)

        # TODO: Needs a custom read_transactions_from_csv for each of AMEX and

        # FR since AMEX has a header row and FR doesn't.

        statement_trans = read_csv(f)

    # Dates are taken from the beginning/end of the statement.

    begin_date = statement_trans[0]['date']

    end_date = statement_trans[-1]['date']

    # Query for the Beancount books data for this above period.

    #

    # There are pros and cons for using Beancount's in-memory entries

    # list directly and also for using Beancount Query Language (BQL)

    # to get a list of transactions? Using BQL because it's

    # convenient, but we don't have access to the full transaction

    # entry objects. Feels a bit strange that these approaches are so

    # disconnected.

    #

    # beancount.query.query_compile.compile() and

    # beancount.query.query_execute.filter_entries() look useful in this respect,

    # but I'm not clear on how to use compile(). An example would help.

    entries, _, options = loader.load_file(args.beancount_file)

    # String concatenation looks bad, but there's no SQL injection possible here

    # because BQL can't write back to the Beancount files. I hope!

    query = f"""

        SELECT filename,

        META("lineno") AS line,

        META("bank-statement") AS bank_statement,

        date,

        number(cost(position)),

        payee,

        ENTRY_META("entity") as entity,

        ANY_META("check-id") as check_id,

        narration

        WHERE account = "{args.account}"

            AND date >= {begin_date}

            AND date <= {end_date}"""

    _, result_rows = run_query(entries, options, query)

    books_trans = sort_records([standardize_beancount_record(row) for row in result_rows])

    # Apply two passes of matching, one for standard matches and one

    # for subset matches.

    matches, remaining_statement_trans, remaining_books_trans = match_statement_and_books(statement_trans, books_trans)

    subset_matches, remaining_statement_trans, remaining_books_trans = subset_match(

        remaining_statement_trans, remaining_books_trans)

    matches.extend(subset_matches)

    # Add the remaining unmatched to make one big list of matches, successful or not.

    unmatched = process_unmatched(remaining_statement_trans, remaining_books_trans)

    matches.extend(unmatched)

    # Print out results of our matching.

    print(format_output(matches, begin_date, end_date, args.csv_statement, args.show_reconciled_matches))

    # Write statement metadata back to the books.

    metadata_to_apply = []

    for match in matches:

import datetime

import decimal

import io

import os

import tempfile

import textwrap

from conservancy_beancount.reconcile.statement_reconciler import (

    date_proximity,

    format_output,

    match_statement_and_books,

    metadata_for_match,

    payee_match,

    read_amex_csv,

    read_fr_csv,

    remove_duplicate_words,

    remove_payee_junk,

    subset_match,

    totals,

    write_metadata_to_books,

)

# These data structures represent individual transactions as taken from the

# statement ("S") or the books ("B").

# Statement transaction examples.

S1 = {

    'date': datetime.date(2022, 1, 1),

    'amount': decimal.Decimal('10.00'),

    'payee': 'Patreon         / Patreon   / 123456/ ST-A1B2C3D4G5H6       /',

    'check_id': '',

    'line': 222,

}

S2 = {

    'date': datetime.date(2022, 1, 2),

    'amount': decimal.Decimal('20.00'),

    'payee': 'BT*LINODE           PHILADELPHIA        P',

    'check_id': '',

    'line': 333,

}

S3 = {

    'date': datetime.date(2022, 1, 3),

    'amount': decimal.Decimal('30.00'),

    'payee': 'USPS PO 4067540039 0PORTLAND            OR',

    'check_id': '',

    'line': 444,

}

S4 = {

    'date': datetime.date(2022, 8, 11),

    'amount': decimal.Decimal('-2260.00'),

    'payee': 'Trust 0000000362 210',

    'check_id': '',

    'line': 555,

}

# Books transaction examples.

B1 = {

    'date': datetime.date(2022, 1, 1),

    'amount': decimal.Decimal('10.00'),

    'payee': 'Patreon',

    'check_id': '',

    'filename': '2022/imports.beancount',

    'line': 777,

    'bank_statement': '',

}

    assert subset_match(statement_trans, books_trans) == (

        [([S4], [B4A, B4B, B4C], [])],  # Matched

        [S1],  # No match: preserved intact

        [B2, B3_next_day, B3_next_week]  # No match: preserved intact

    )

def test_handles_amex_csv():

    CSV = """Date,Receipt,Description,Card Member,Account #,Amount,Extended Details,Appears On Your Statement As,Address,City/State,Zip Code,Country,Reference,Category\n08/19/2021,,Gandi.net           San Francisco,RODNEY R BROWN,-99999,28.15,"00000009999 00000009999999999999\nGandi.net\nSan Francisco\n00000009999999999999",Gandi.net           San Francisco,"NEPTUNUSSTRAAT 41-63\nHOOFDDORP",,2132 JA,NETHERLANDS (THE),'999999999999999999',Merchandise & Supplies-Internet Purchase\n"""

    expected = [

        {

            'date': datetime.date(2021, 8, 19),

            'amount': decimal.Decimal('-28.15'),

            'payee': 'Gandi San Francisco',

            'check_id': '',

            'line': 2,

        },

    ]

    assert read_amex_csv(io.StringIO(CSV)) == expected

def test_handles_fr_csv():

    CSV = """"DD99999999999","03/31/2022","LAST STATEMENT","","","$1,000.00"\n"9999999999999","04/01/2022","INCOMING WIRE","GONDOR S.S. A111111111BCDE0F","$6.50","$1,006.50"\n"DD99999999999","04/18/2022","CHECK  3741","","$-4.50","$1,002.00"\n"DD99999999999","04/30/2022","THIS STATEMENT","","","$102.00"\n"""

    expected = [

        {

            'date': datetime.date(2022, 4, 1),

            'amount': decimal.Decimal('6.50'),

            'payee': 'GONDOR S.S. A1111111',

            'check_id': '',

            'line': 2,

        },

        {

            'date': datetime.date(2022, 4, 18),

            'amount': decimal.Decimal('-4.50'),

            'payee': '',

            'check_id': '3741',

            'line': 3,

        },

    ]

    assert read_fr_csv(io.StringIO(CSV)) == expected

def test_format_output():

    statement = [S1]

    books = [B1]

    matches, _, _ = match_statement_and_books(statement, books)

    output = format_output(matches, datetime.date(2022, 1, 1), datetime.date(2022, 2, 1), 'test.csv', True)

    assert '2022-01-01:       10.00 Patreon         / Patreon   / 12345  →  2022-01-01:       10.00 Patreon                              ✓ Matched' in output