import _csv
import csv
import datetime
import io
import logging
import urllib.parse
from collections.abc import Iterable, Mapping
from pathlib import Path
from typing import Any

from frozendict import frozendict

from . import csv_import, data

logger = logging.getLogger(__name__)


def csv_safe_value(v: Any) -> str:
    if isinstance(v, urllib.parse.ParseResult):
        return v.geturl()
    if isinstance(v, datetime.datetime):
        if v.tzinfo is None:
            raise RuntimeError(v)
    return str(v)


def equals_without_fields(
    a: Mapping[str, Any],
    b: Mapping[str, Any],
    fields: Iterable[str] = frozenset(),
) -> bool:
    a = dict(a)
    b = dict(b)

    for f in fields:
        del a[f], b[f]

    return frozendict(a) == frozendict(b)


def deduplicate_by_ignoring_certain_fields(
    dicts: list[frozendict[str, Any]],
    deduplicate_ignore_columns: Iterable[str],
) -> list[frozendict[str, Any]]:
    """Removes duplicates that occur when ignoring certain columns.

    Output order is stable.
    """
    to_remove = set()
    for idx1, first in enumerate(dicts):
        for idx2, second in enumerate(dicts[idx1 + 1 :], idx1 + 1):
            if equals_without_fields(first, second, deduplicate_ignore_columns):
                to_remove.add(idx2)
            del idx2, second
        del idx1, first

    to_remove_ls = sorted(to_remove)
    del to_remove
    while to_remove_ls:
        del dicts[to_remove_ls.pop()]

    return dicts


def deduplicate_dicts(
    dicts: list[frozendict[str, Any]],
    deduplicate_mode: data.DeduplicateMode,
    deduplicate_ignore_columns: list[str],
) -> tuple[list[frozendict[str, Any]], list[str]]:
    if not isinstance(deduplicate_ignore_columns, list):
        raise TypeError(deduplicate_ignore_columns)

    fieldnames = []
    for d in dicts:
        for k in d.keys():
            if k not in fieldnames:
                fieldnames.append(k)
            del k
        del d

    if deduplicate_mode == data.DeduplicateMode.ONLY_LATEST:
        while len(dicts) > 1 and equals_without_fields(
            dicts[-1],
            dicts[-2],
            deduplicate_ignore_columns,
        ):
            del dicts[-1]
    elif deduplicate_mode == data.DeduplicateMode.BY_ALL_COLUMNS:
        dicts = deduplicate_by_ignoring_certain_fields(
            dicts,
            deduplicate_ignore_columns,
        )
    elif deduplicate_mode != data.DeduplicateMode.NONE:
        dicts = list(set(dicts))

    dicts = sorted(dicts, key=lambda d: tuple(str(d.get(fn, '')) for fn in fieldnames))
    return dicts, fieldnames


def normalize_dict(d: dict[str, Any] | frozendict[str, Any]) -> frozendict[str, Any]:
    return frozendict(
        {
            k: csv_import.csv_str_to_value(str(v))
            for k, v in d.items()
            if csv_import.csv_str_to_value(str(v)) is not None
        },
    )


def extend_csv_file(
    csv_file: Path,
    new_dicts: list[dict[str, Any] | frozendict[str, Any]],
    deduplicate_mode: data.DeduplicateMode,
    deduplicate_ignore_columns: list[str],
) -> dict:
    if not isinstance(deduplicate_ignore_columns, list):
        raise TypeError(deduplicate_ignore_columns)

    try:
        original_dicts = csv_import.load_csv_file(csv_file)
    except (FileNotFoundError, _csv.Error):
        logger.info('Creating file: %s', csv_file)
        original_dicts = []

    original_num_dicts = len(original_dicts)
    dicts = [normalize_dict(d) for d in original_dicts] + [
        normalize_dict(d) for d in new_dicts
    ]
    del new_dicts

    dicts, fieldnames = deduplicate_dicts(
        dicts,
        deduplicate_mode,
        deduplicate_ignore_columns,
    )

    csvfile_in_memory = io.StringIO()
    writer = csv.DictWriter(
        csvfile_in_memory,
        fieldnames=fieldnames,
        dialect=csv_import.CSV_DIALECT,
    )
    writer.writeheader()
    for d in dicts:
        writable_d = {k: csv_safe_value(v) for k, v in d.items()}
        writer.writerow(writable_d)
        del d, writable_d
    output_csv = csvfile_in_memory.getvalue()
    del writer, csvfile_in_memory

    csv_file.parent.mkdir(parents=True, exist_ok=True)
    with open(csv_file, 'w') as csvfile:
        csvfile.write(output_csv)
        del csvfile
    logger.info(
        'Extended CSV "%s" from %d to %d lines',
        csv_file,
        original_num_dicts,
        len(dicts),
    )

    return {
        'extended': original_num_dicts != len(dicts),
        'input_lines': original_num_dicts,
        'output_lines': len(dicts),
        'dicts': dicts,
    }