import traceback
from itertools import batched
from concurrent.futures import ThreadPoolExecutor

from django.core.management import BaseCommand
from django.conf import settings
import clickhouse_connect
from vacancies.main.vector_store import add_vectors, extract_features, qdrant_client
from vacancies.conf.settings import CLICKHOUSE_HOST, CLICKHOUSE_PORT

clickhouse_client = clickhouse_connect.create_client(host=CLICKHOUSE_HOST, port=CLICKHOUSE_PORT)

query = """
SELECT id, chat_username, telegram_id, message, timestamp
FROM telegram_parser_chatmessage
WHERE timestamp >= now() - INTERVAL 30 DAY
  AND length(message) > 150
  AND arrayCount(x -> position(message, x) > 0, [
    'ваканси', 'ищем', 'требуется', 'разработчик', 'будет плюсом',
    'зарплат', 'оклад', 'з/п', 'руб', 'опыт',
    'требовани', 'обязанности', 'условия', 'офис',
    'удаленн', 'гибкий график', 'полный день', 'занятост',
    'резюме', 'собеседовани', 'junior', 'middle', 'senior', 'ждем', 'компани',
    'заниматься', 'формат', 'занятость', 'вилка', 'должност', 'контакт'
  ]) >= 5
  AND arrayCount(x -> position(lower(message), x) > 0, [
    'о себе', 'обо мне', 'умею', '#ищу', '#резюме', 'университет', 'колледж'
  ]) = 0
  AND id NOT IN %(exist_points)s
"""


class Command(BaseCommand):
    help = "Collect vacancies from telegram messages"

    def handle(self, *args, **options):
        next_page_offset = 0
        exist_points_ids = [-1]
        while next_page_offset is not None:
            response = qdrant_client.scroll(
                collection_name="vacancies",
                limit=100_000,
                offset=next_page_offset,
                with_payload=False,
                with_vectors=False,
                timeout=30,
            )
            exist_points_ids.extend([point.id for point in response[0]])
            next_page_offset = response[1]
        exist_points_set = tuple(set(exist_points_ids))

        result_rows = clickhouse_client.query(query, parameters={"exist_points": exist_points_set}).result_rows
        with ThreadPoolExecutor(max_workers=settings.COLLECT_VACANCIES_BATCH_SIZE) as pool:
            pool.map(self._process_batch, batched(result_rows, settings.COLLECT_VACANCIES_BATCH_SIZE))

    def _process_batch(self, result_rows):
        try:
            for index, row in enumerate(result_rows):
                (id, chat_username, telegram_id, message, timestamp) = row

                link = f"https://t.me/{chat_username}/{telegram_id}"
                print(f"Processing {index+1}/{len(result_rows)} link: {link}")
                features = extract_features(message)

                add_vectors(
                    "vacancies",
                    id,
                    features.model_dump(),
                    {'content': message, 'features_json': features.model_dump(), "link": link, "timestamp": timestamp},
                )
        except Exception as exc:
            traceback.print_exception(exc)