"""Reconcile an AMEX/FR CSV statement against the books and print differences.

Beancount users often write importers to create bookkeeping entries direct from
a bank statement or similar. That approach automates data entry and
reconciliation in one step. In some cases though, it's useful to manually enter
transactions and reconcile them later on. This workflow helpful in cases like
writing a paper check when there's a time lag between committing to making a
payment and the funds being debited. That's the workflow we're using here.

Run like this:

$ python3 -m pip install thefuzz
$ python3 conservancy_beancount/reconcile/statement_reconciler.py \
  --beancount-file=$HOME/conservancy/beancount/books/2021.beancount \
  --csv-statement=$HOME/conservancy/confidential/2021-09-10_AMEX_activity.csv \
  --account=Liabilities:CreditCard:AMEX

Conservancy currently enter data by hand rather than using Beancount importers.
This tool is still somewhat like an importer in that it needs to extract
transaction details from a third-party statement. Instead of creating
directives, it just checks to see that similar directives are already present.

Problems in scope:
 - errors in the books take hours to find during reconciliation ("you're entering a world of pain")
 - adding statement/reconciliation metadata to books is manual and prone to mistakes
 - Beancount doesn't provide any infrastructure for programmatically updating the books, only appending
 - after updates to the books files, beancount must be restarted to reflect updates
 - updates also invalidate the cache meaning restart takes several minutes
 - 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.

"""
import argparse
import collections
import copy
import csv
import datetime
import decimal
import io
import itertools
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
from colorama import Fore, Style  # type: ignore

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: str) -> str:
    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


def read_transactions_from_csv(f: TextIO, standardize_statement_record: Callable) -> 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_statement_record(row, i) for i, row in enumerate(reader, 2)])


# CSV reconciliation report.
# Merge helper script?


def standardize_amex_record(row: Dict, line: int) -> Dict:
    """Turn an AMEX CSV row into a standard dict format representing a transaction."""
    # NOTE: Statement doesn't seem to give us a running balance or a final total.
    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 validate_amex_csv(sample: str, account: str) -> None:
    required_cols = {'Date', 'Amount', 'Description', 'Card Member'}
    reader = csv.DictReader(io.StringIO(sample))
    if reader.fieldnames and not required_cols.issubset(reader.fieldnames):
        sys.exit(f"This CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}")


def validate_fr_csv(sample: str, account: str) -> None:
    required_cols = {'Date', 'Amount', 'Detail', 'Serial Num'}
    reader = csv.DictReader(io.StringIO(sample))
    if reader.fieldnames and not required_cols.issubset(reader.fieldnames):
        sys.exit(f"This CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}")


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(record: dict) -> str:
    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]:
    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:
    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:
    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 = ''
    # Sometimes we get unrelated numbers in the statement column with check-ids,
    # so we can't match based on the existence of a statement check-id.
    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[Dict], books_trans: List[Dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:
    """
    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 = 0.0
        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.
            if best_match_index is not None:
                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


# TODO: Return list of tuples (instead of list of lists).

def format_matches(matches: List, csv_statement: str, show_reconciled_matches: bool) -> List[List]:
    match_output = []
    for r1s, r2s, note in matches:
        note = ', '.join(note)
        note = ': ' + note if note else note
        if r1s and r2s:
            if show_reconciled_matches or not all(x['bank_statement'] for x in r2s):
                if len(r2s) == 1:
                    match_output.append([r1s[0]['date'], f'{format_record(r1s[0])}  →  {format_record(r2s[0])}  ✓ Matched{note}'])
                else:
                    match_output.extend(format_multirecord(r1s, r2s, note))
        elif r1s:
            match_output.append([r1s[0]['date'], Fore.RED + Style.BRIGHT + f'{format_record(r1s[0])}  →  {" ":^59}  ✗ NOT IN BOOKS ({os.path.basename(csv_statement)}:{r1s[0]["line"]})' + Style.RESET_ALL])
        else:
            match_output.append([r2s[0]['date'], Fore.RED + Style.BRIGHT + f'{" ":^59}  →  {format_record(r2s[0])}  ✗ NOT ON STATEMENT ({os.path.basename(r2s[0]["filename"])}:{r2s[0]["line"]})' + Style.RESET_ALL])
    return match_output


def date_proximity(d1: datetime.date, d2: datetime.date) -> float:
    diff = abs(int((d1 - d2).days))
    if diff > 60:
        return 0.0
    else:
        return 1.0 - (diff / 60.0)


def metadata_for_match(match: Tuple[List, List, List], statement_filename: str, csv_filename: str) -> List[Tuple[str, int, str]]:
    # 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: str) -> str:
    return os.path.relpath(path, start=os.getenv('CONSERVANCY_REPOSITORY'))


def parse_path(path: str) -> str:
    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:
    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_args(argv: List[str]) -> argparse.Namespace:
    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: List[Tuple[List, List, List]]) -> Tuple[decimal.Decimal, decimal.Decimal, decimal.Decimal]:
    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 subset_match(statement_trans: List[dict], books_trans: List[dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:
    matches = []
    remaining_books_trans = []
    remaining_statement_trans = []

    groups = itertools.groupby(books_trans, key=lambda x: (x['date'], x['payee']))
    for _, group in groups:
        best_match_score = 0.0
        best_match_index = None
        best_match_note = []
        matches_found = 0

        group_items = list(group)
        total = sum(x['amount'] for x in group_items)
        r2 = copy.copy(group_items[0])
        r2['amount'] = total
        for i, r1 in enumerate(statement_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(([statement_trans[best_match_index]], group_items, best_match_note))
            if best_match_index is not None:
                del statement_trans[best_match_index]
            for item in group_items:
                # TODO: Why?
                books_trans.remove(item)
        else:
            remaining_books_trans.append(r2)
    for r1 in statement_trans:
        remaining_statement_trans.append(r1)
    return matches, remaining_statement_trans, remaining_books_trans


def process_unmatched(statement_trans: List[dict], books_trans: List[dict]) -> List[Tuple[List, List, List]]:
    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 main(args: argparse.Namespace) -> None:
    # 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:
        validate_csv = validate_amex_csv
        standardize_statement_record = standardize_amex_record
    else:
        validate_csv = validate_fr_csv
        standardize_statement_record = standardize_fr_record

    with open(args.csv_statement) as f:
        sample = f.read(200)
        validate_csv(sample, args.account)
        f.seek(0)
        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_rows = run_query(entries, options, books_balance_query, numberify=True)
    books_balance = 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_rows = run_query(entries, options, query)

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

    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)
    unmatched = process_unmatched(remaining_statement_trans, remaining_books_trans)
    matches.extend(unmatched)

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

    _, total_missing_from_books, total_missing_from_statement = totals(matches)

    print('-' * 155)
    print(f'{"Statement transaction":<52}            {"Books transaction":<58}   Notes')
    print('-' * 155)
    for _, output in sorted(match_output, key=lambda x: x[0]):
        print(output)
    print('-' * 155)
    print(f'Statement period {begin_date} to {end_date}')
    print(f'Statement/cleared balance:    {args.statement_balance:12,.2f}    (as provided by you)')
    print(f'Books balance:                {books_balance:12,.2f}    (all transactions, includes unreconciled)')
    print(f'Total not on statement:       {total_missing_from_statement:12,.2f}')
    print(f'Total not on books:           {total_missing_from_books:12,.2f}')
    print('-' * 155)

    # Write statement metadata back to books
    metadata_to_apply = []
    for match in matches:
        metadata_to_apply.extend(metadata_for_match(match, args.bank_statement, args.csv_statement))
    if metadata_to_apply and not args.non_interactive:
        print('Mark matched transactions as reconciled in the books? (y/N) ', end='')
        if input().lower() == 'y':
            write_metadata_to_books(metadata_to_apply)


if __name__ == '__main__':
    args = parse_args(sys.argv)
    main(args)