"""Compare a bank CSV statement with the books.

This tool takes an AMEX or First Republic CSV statement file and compares it
line-by-line with the Beancount books to make sure that everything matches. This
is designed for situations where transactions are entered into the books
directly, rather than being imported from a statement after the fact.

The reconciler will attempt to match transactions based on date, amount, check
number and payee, but is forgiving to differences in dates, the absensce of
check number and inexact matches on payee. Matches are ranked, so where there is
only one decent match for an amount/date this is accepted, but if there are
multiple similar candidates it will refuse to guess.

The reconciler will also attempt to identify where a single statement entry has
been split out into multiple Beancount postings, such as a single bank transfer
representing health insurance for multiple employees.

Run it like this:

$ statement_reconciler \
  --beancount-file 2021.beancount \
  --account Liabilities:CreditCard:AMEX \
  --csv-statement ~/svn/2021-09-10_AMEX_activity.csv \
  --bank-statement ~/svn/2021-09-10_AMEX_activity.pdf

Background:

Regular Beancount users often write automated importers to create bookkeeping
entries direct from a bank statement or similar. That combines data entry and
reconciliation in one step. Conservancy uses a different approach; they manually
entering transactions and reconciling them later on. This workflow is helpful in
cases like writing checks (see below). This is the workflow implented by this
tool.

That said, 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. This is a bit like diff-ing a statement with the books (though we're
only interested in the presence of lines, not so much their order).

Paper checks are entered into the books when written (a.k.a. "posted"), but may
not be cashed until months later sometimes causing reconciliation differences
that live beyond a month. It's worth noting that there are really two dates here
- the posting date and the cleared date. Beancount only allows us to model one,
which is why carrying these reconciliation differences between months feels a
bit awkward.

Problems in scope:

 - errors in the books take hours to find during reconciliation, requiring
   manually comparing statements and the books and are succeptible to mistakes,
   such as not noticing when there are two payments for the same amount on the
   statement, but not in the books (as Bradley likes to quote, "you're entering
   a world of pain")

 - adding statement/reconciliation metadata to books is/was manual and prone to
   mistakes

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

 - not all staff use Emacs

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

 - reconciliation reports are created by hand when there are mismatches

Other related problems we're not dealing with here:

 - after updates to the books files, beancount must be restarted to reflect
   updates

 - updates also invalidate the cache meaning restart takes several minutes

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

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

Current issue:

 - entry_point seems to swallow errors, meaning you get a fairly unhelpful
   message if there's an unhandled error

Future possibilities:

 - allow the reconciler to respect manually-applied metadata - not clear how
   this would work exactly

 - allow interactive matching where the user can specifiy a match

 - consider combining this with helper.py into one more complete tool that both
   reconciles and summarises the unreconciled transactions
"""

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 Dict, List, Optional, Sequence, Tuple, TextIO

from beancount import loader
from beancount.query.query import run_query
from colorama import Fore, Style  # type: ignore

from .. import cliutil
from .. import config as configmod

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

PROGNAME = 'reconcile-statement'
logger = logging.getLogger(__name__)

# Get some interesting feedback on call to RT with this:
# logger.setLevel(logging.DEBUG)
# 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+')
FULL_MATCH_THRESHOLD = 0.8
PARTIAL_MATCH_THRESHOLD = 0.4


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:
    """Clean up payee field to improve quality of fuzzy matching.

    It turns out that bank statement "description" fields are
    difficult to fuzzy match on because they're long and
    noisey. Truncating them (see standardize_XXX_record fns) and
    removing the common junk helps significantly.

    """
    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 parse_amount(amount: str) -> decimal.Decimal:
    """Parse amounts and handle comma separators as seen in some FR statements."""
    return decimal.Decimal(amount.replace('$', '').replace(',', ''))


def validate_amex_csv(sample: 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 AMEX CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}. Please use an unmodified statement direct from the institution.")


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 * parse_amount(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 read_amex_csv(f: TextIO) -> 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_amex_record(row, i) for i, row in enumerate(reader, 2)])


def validate_fr_csv(sample: str) -> None:
    # No column headers in FR statements
    reader = csv.reader(io.StringIO(sample))
    next(reader)  # First row is previous statement ending balance
    row = next(reader)
    date = None
    try:
        date = datetime.datetime.strptime(row[1], '%m/%d/%Y')
    except ValueError:
        pass
    amount_found = '$' in row[4] and '$' in row[5]
    if len(row) != 6 or not date or not amount_found:
        sys.exit("This First Republic CSV doesn't seem to have the 6 columns we're expecting, including a date in column 2 and an amount in columns 5 and 6. Please use an unmodified statement direct from the institution.")


def standardize_fr_record(line, row):
    record = {
        'date': datetime.datetime.strptime(row[1], '%m/%d/%Y').date(),
        'amount': parse_amount(row[4]),
        'payee': remove_payee_junk(row[3] or '')[:20],
        'check_id': row[2].replace('CHECK  ', '') if 'CHECK  ' in row[2] else '',
        'line': line,
    }
    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 = ''
    # 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 > FULL_MATCH_THRESHOLD:
            payee_message = ''
        elif payee_score > PARTIAL_MATCH_THRESHOLD:
            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]]:
    """Match transactions between the statement and books.

    If matched, 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.

    Passes through any unmatched transactions.

    Currently we use the same matching logic for all types of
    statements. It's conceivable that you could have special cases to
    accurately match some types of statements, but that would be more
    work to maintain and test.

    """
    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


def subset_match(statement_trans: List[dict], books_trans: List[dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:
    """Match single statement transactions with multiple books transactions.

    Works similarly to match_statement_and_books in that it returns a
    list of matches and lists of remaining statement and books
    transactions.

    """
    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]
        else:
            remaining_books_trans.append(r2)
    for r1 in statement_trans:
        remaining_statement_trans.append(r1)
    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]:

    """Produce a list of body output lines from the given matches.

    The first column is a date so we can re-sort the list to put the
    missing entries in the right place. The second column is the text
    output.

    """
    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:
                    entry = [r1s[0]['date'], f'{format_record(r1s[0])}  →  {format_record(r2s[0])}  ✓ Matched{note}']
                    if 'payee mismatch' in note:
                        entry[1] = Fore.YELLOW + Style.BRIGHT + entry[1] + Style.RESET_ALL
                    match_output.append(entry)
                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:
    """Scores two days based on how close they are together."""
    ZERO_CUTOFF = 60  # Score will be zero for this many days apart.
    diff = abs(int((d1 - d2).days))
    if diff >= ZERO_CUTOFF:
        return 0.0
    else:
        return 1.0 - (diff / ZERO_CUTOFF)


def metadata_for_match(match: Tuple[List, List, List], statement_filename: str, csv_filename: str) -> List[Tuple[str, int, str]]:
    """Returns the bank-statement metadata that should be applied for a match."""
    # TODO: Our data structure would allow multiple statement entries
    # for a match, but would this ever make sense? 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


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'),
        ...,
    ]

    Beancount doesn't provide any infrastructure for programmatically
    updating the books, only appending in the case of importers. So
    we're on our own here.
    """
    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:
    """Chop off the unique per-person CONSERVANCY_REPOSITORY.

    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:
        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)


entry_point = cliutil.make_entry_point(__name__, PROGNAME)

if __name__ == '__main__':
    exit(entry_point())