- paper checks are entered in the books when written, but may not be cashed until months later (reconcile errors)

 - balance checks are manually updated in svn/Financial/Ledger/sanity-check-balances.yaml

 - jumping to an individual transaction in a large ledger isn't trivial - Emacs grep mode is the current best option

 - Pam and other staff don't use Emacs

 - auditors would prefer Bradley didn't perform reconciliation, ideally not Rosanne either

 - transactions are entered manually and reconciled after the fact, but importing from statements may be useful in some cases

Q. How are reconciliation reports created currently? How do you read them?

 - by hand from copying and pasting from the helper tool output

Problem is potentially similar to diff-ing, but in the books, transaction order isn't super significant.

TODO/ISSUES:

 - AMEX statement doesn't provide bank balance or running total

"""

import argparse

import collections

import copy

import csv

import datetime

import decimal

import io

import logging

import os

import re

import sys

from typing import Callable, Dict, List, Tuple, TextIO

from beancount import loader

from beancount.query.query import run_query

if not sys.warnoptions:

    import warnings

    # Disable annoying warning from thefuzz prompting for a C extension. The

    # current pure-Python implementation isn't a bottleneck for us.

    warnings.filterwarnings('ignore', category=UserWarning, module='thefuzz.fuzz')

from thefuzz import fuzz  # type: ignore

logger = logging.getLogger()

logger.setLevel(logging.DEBUG)

# Console logging.

logger.addHandler(logging.StreamHandler())

JUNK_WORDS = [

    'software',

    'freedom',

    'conservancy',

    'conse',

    'payment',

    'echeck',

    'bill',

    'debit',

    'wire',

    'credit',

    "int'l",

    "in.l",

    'llc',

    'online',

    'donation',

    'usd',

    'inc',

]

JUNK_WORDS_RES = [re.compile(word, re.IGNORECASE) for word in JUNK_WORDS]

ZERO_RE = re.compile('^0+')

def remove_duplicate_words(text):

    unique_words = []

    known_words = set()

    for word in text.split():

        if word.lower() not in known_words:

            unique_words.append(word)

            known_words.add(word.lower())

    return ' '.join(unique_words)

def remove_payee_junk(payee: str) -> str:

    for r in JUNK_WORDS_RES:

        payee = r.sub('', payee)

    payee = ZERO_RE.sub('', payee)

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

    payee = re.sub(r'\.0\.\d+', ' ', payee)

    payee = payee.replace('.0', ' ')

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

    payee = re.sub(re.escape('.com'), ' ', payee, flags=re.IGNORECASE)

    payee = re.sub(re.escape('.net'), ' ', payee, flags=re.IGNORECASE)

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

    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,

    }

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

    return {

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

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

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

        'check_id': row['Serial Num'].lstrip('0'),

        'line': line,

    }

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(records: list[dict]) -> str:

    if len(records) == 1:

        record = records[0]

        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

    else:

        raise NotImplementedError

def sort_records(records: List) -> List:

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

def first_word_exact_match(a, b):

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

        return 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;

def payee_match(a, b):

    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

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

    match_output = []

    for r1, r2, note in matches:

        note = ', '.join(note)

        note = ': ' + note if note else note

        if r1 and r2:

            if show_reconciled_matches:

                match_output.append([r1[0]['date'], f'{format_record(r1)}  →  {format_record(r2)}  ✓ Matched{note}'])

        elif r1:

            match_output.append([r1[0]['date'], f'{format_record(r1)}  →  {" ":^59}  ✗ NOT IN BOOKS ({os.path.basename(csv_statement)}:{r1[0]["line"]})'])

        else:

            match_output.append([r2[0]['date'], f'{" ":^59}  →  {format_record(r2)}  ✗ NOT ON STATEMENT ({os.path.basename(r2[0]["filename"])}:{r2[0]["line"]})'])

    return match_output

def date_proximity(d1, d2):

    diff = abs((d1 - d2).days)

    if diff > 60:

        return 0

    else:

        return 1.0 - (diff / 60.0)

def metadata_for_match(match, statement_filename, csv_filename):

    # Can we really ever have multiple statement entries? Probably not.

    statement_filename = get_repo_relative_path(statement_filename)

    csv_filename = get_repo_relative_path(csv_filename)

    metadata = []

    statement_entries, books_entries, _ = match

    for books_entry in books_entries:

        for statement_entry in statement_entries:

            if not books_entry['bank_statement']:

                metadata.append((books_entry['filename'], books_entry['line'], f'    bank-statement: {statement_filename}'))

                metadata.append((books_entry['filename'], books_entry['line'], f'    bank-statement-csv: {csv_filename}:{statement_entry["line"]}'))

    return metadata

# TODO: Is there a way to pull the side-effecting code out of this function?

def write_metadata_to_books(metadata_to_apply: List[Tuple[str, int, str]]) -> None:

    """Insert reconciliation metadata in the books files.

    Takes a list of edits to make as tuples of form (filename, lineno, metadata):

    [

        ('2021/main.beancount', 4245, '    bank-statement: statement.pdf'),

        ('2021/main.beancount', 1057, '    bank-statement: statement.pdf'),

        ('2021/payroll.beancount', 257, '    bank-statement: statement.pdf'),

        ...,

    ]

    """

    file_contents: dict[str, list] = {}

    file_offsets: dict[str, int] = collections.defaultdict(int)

    # Load each books file into memory and insert the relevant metadata lines.

    # Line numbers change as we do this, so we keep track of the offset for each

    # file. Changes must be sorted by line number first or else the offsets will

    # break because we're jumping around making edits.

    for filename, line, metadata in sorted(metadata_to_apply):

        if filename not in file_contents:

            with open(filename, 'r') as f:

                file_contents[filename] = f.readlines()

        # Insert is inefficient, but fast enough for now in practise.

        file_contents[filename].insert(line + file_offsets[filename], metadata.rstrip() + '\n')

        file_offsets[filename] += 1

    # Writes each updated file back to disk.

    for filename, contents in file_contents.items():

        with open(filename, 'w') as f:

            f.writelines(contents)

            print(f'Wrote {filename}.')

def get_repo_relative_path(path):

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

def parse_path(path):

    if not os.path.exists(path):

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

    return path

def parse_repo_relative_path(path):

    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(f'$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_args(argv):

    parser = argparse.ArgumentParser(description='Reconciliation helper')

    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('--grep-output-filename')

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

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

    parser.add_argument('--statement-balance', type=decimal.Decimal, 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")

    parser.add_argument('--non-interactive', action='store_true', help="Don't prompt to write to the books")

    return parser.parse_args(args=argv[1:])

def totals(matches):

    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 main(args):

    # TODO: Should put in a sanity check to make sure the statement you're feeding

    # in matches the account you've provided.

    # TODO: Can we open the files first, then pass the streams on to the rest of the program?

    if 'AMEX' in args.account:

        standardize_statement_record = standardize_amex_record

    else:

        standardize_statement_record = standardize_fr_record

    with open(args.csv_statement) as f:

        statement_trans = read_transactions_from_csv(f, standardize_statement_record)

    begin_date = statement_trans[0]['date']

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

    # Do we traverse and filter the in-memory entries list and filter that, or do we

    # use Beancount Query Language (BQL) to get a list of transactions? Currently

    # using BQL.

    #

    # 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)

    books_balance_query = f"""SELECT sum(COST(position)) AS aa WHERE account = "{args.account}"

        AND date <= {end_date.isoformat()}"""

    result_types, result_rows = run_query(entries, options, books_balance_query, numberify=True)

    books_balance = result_rows[0][0] if result_rows else 0

    books_balance_reconciled_query = f"""SELECT sum(COST(position)) AS aa WHERE account = "{args.account}"

        AND date <= {end_date.isoformat()} AND META('bank-statement') != NULL"""

    result_types, result_rows = run_query(entries, options, books_balance_reconciled_query, numberify=True)

    books_balance_reconciled = result_rows[0][0] if result_rows else 0

    # 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_types, result_rows = run_query(entries, options, query)

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

    matches = match_statement_and_books(statement_trans, books_trans)

    match_output = format_matches(matches, args.csv_statement, args.show_reconciled_matches)

    # assert books_balance == books_balance_reconciled + total_matched + total_missing_from_statement

    total_matched, total_missing_from_books, total_missing_from_statement = totals(matches)

    out = io.StringIO()

    print('-' * 155)

    print(f'{"Statement transaction":<52}            {"Books transaction":<58}   Notes')

    print('-' * 155)