"""
Feature Materializer — computes derived attention features from wiki + gdelt raw data.

Materializes into attention_features_daily for the given ticker + event_date.
"""

import logging
import statistics
from datetime import date, timedelta
from typing import Optional

from sqlalchemy import select, func
from sqlalchemy.dialects.postgresql import insert as pg_insert
from sqlalchemy.ext.asyncio import AsyncSession

from app.models.attention import (
    AttentionFeaturesDaily,
    CompanyEntityMap,
    GdeltArticleRaw,
    WikiPageviewsDaily,
)

logger = logging.getLogger(__name__)


async def _get_wiki_views_series(
    db: AsyncSession,
    wiki_title: str,
    target_date: date,
    lookback: int,
) -> list[int]:
    """Return list of view counts for [target_date - lookback, target_date - 1]."""
    start = target_date - timedelta(days=lookback)
    end = target_date - timedelta(days=1)
    result = await db.execute(
        select(WikiPageviewsDaily.views)
        .where(
            WikiPageviewsDaily.wiki_title == wiki_title,
            WikiPageviewsDaily.date >= start,
            WikiPageviewsDaily.date <= end,
        )
        .order_by(WikiPageviewsDaily.date)
    )
    return list(result.scalars().all())


async def materialize_features(
    db: AsyncSession,
    ticker: str,
    event_date: date,
) -> AttentionFeaturesDaily:
    """Compute and upsert daily attention features for ticker on event_date.

    Returns the upserted AttentionFeaturesDaily row.
    """
    ticker = ticker.upper()

    entity_result = await db.execute(
        select(CompanyEntityMap).where(CompanyEntityMap.ticker == ticker)
    )
    entity = entity_result.scalars().first()
    wiki_title = entity.wiki_title if entity else None

    # --- Wiki features ---
    wiki_views: Optional[int] = None
    wiki_spike_10d: Optional[float] = None
    wiki_zscore_20d: Optional[float] = None

    if wiki_title:
        # Fetch today's views
        today_result = await db.execute(
            select(WikiPageviewsDaily.views).where(
                WikiPageviewsDaily.wiki_title == wiki_title,
                WikiPageviewsDaily.date == event_date,
            )
        )
        wiki_views = today_result.scalars().first()

        if wiki_views is not None:
            # Spike: views / median of previous 10 days
            prev_10 = await _get_wiki_views_series(db, wiki_title, event_date, 10)
            if prev_10:
                median_10 = statistics.median(prev_10)
                wiki_spike_10d = wiki_views / median_10 if median_10 > 0 else None

            # Z-score over previous 20 days
            prev_20 = await _get_wiki_views_series(db, wiki_title, event_date, 20)
            if len(prev_20) >= 3:
                mean_20 = statistics.mean(prev_20)
                stdev_20 = statistics.stdev(prev_20)
                if stdev_20 > 0:
                    wiki_zscore_20d = (wiki_views - mean_20) / stdev_20

    # --- GDELT features ---
    # 1-day: articles published on event_date only
    day_start = event_date
    day_end = event_date

    count_1d_result = await db.execute(
        select(func.count()).where(
            GdeltArticleRaw.matched_ticker == ticker,
            func.date(GdeltArticleRaw.published_at) >= day_start,
            func.date(GdeltArticleRaw.published_at) <= day_end,
        )
    )
    gdelt_article_count_1d = count_1d_result.scalar() or 0

    # 3-day window: event_date ± 1
    window_start = event_date - timedelta(days=1)
    window_end = event_date + timedelta(days=1)

    count_3d_result = await db.execute(
        select(func.count()).where(
            GdeltArticleRaw.matched_ticker == ticker,
            func.date(GdeltArticleRaw.published_at) >= window_start,
            func.date(GdeltArticleRaw.published_at) <= window_end,
        )
    )
    gdelt_article_count_3d = count_3d_result.scalar() or 0

    # Unique domains in 3-day window
    domains_result = await db.execute(
        select(func.count(func.distinct(GdeltArticleRaw.domain))).where(
            GdeltArticleRaw.matched_ticker == ticker,
            GdeltArticleRaw.domain.isnot(None),
            func.date(GdeltArticleRaw.published_at) >= window_start,
            func.date(GdeltArticleRaw.published_at) <= window_end,
        )
    )
    gdelt_unique_domains_3d = domains_result.scalar() or 0

    # US articles in 3-day window
    us_result = await db.execute(
        select(func.count()).where(
            GdeltArticleRaw.matched_ticker == ticker,
            GdeltArticleRaw.sourcecountry == "US",
            func.date(GdeltArticleRaw.published_at) >= window_start,
            func.date(GdeltArticleRaw.published_at) <= window_end,
        )
    )
    gdelt_us_article_count_3d = us_result.scalar() or 0

    # Upsert
    stmt = (
        pg_insert(AttentionFeaturesDaily)
        .values(
            ticker=ticker,
            date=event_date,
            wiki_views=wiki_views,
            wiki_spike_10d=wiki_spike_10d,
            wiki_zscore_20d=wiki_zscore_20d,
            gdelt_article_count_1d=gdelt_article_count_1d,
            gdelt_article_count_3d=gdelt_article_count_3d,
            gdelt_unique_domains_3d=gdelt_unique_domains_3d,
            gdelt_us_article_count_3d=gdelt_us_article_count_3d,
        )
        .on_conflict_do_update(
            constraint="uq_attention_features_daily",
            set_=dict(
                wiki_views=wiki_views,
                wiki_spike_10d=wiki_spike_10d,
                wiki_zscore_20d=wiki_zscore_20d,
                gdelt_article_count_1d=gdelt_article_count_1d,
                gdelt_article_count_3d=gdelt_article_count_3d,
                gdelt_unique_domains_3d=gdelt_unique_domains_3d,
                gdelt_us_article_count_3d=gdelt_us_article_count_3d,
            ),
        )
        .returning(AttentionFeaturesDaily)
    )

    await db.execute(stmt)
    await db.commit()

    # Always re-fetch with populate_existing=True to avoid SQLAlchemy identity-map
    # returning stale pre-upsert values (same pattern as resolve_entity).
    row_result = await db.execute(
        select(AttentionFeaturesDaily)
        .where(
            AttentionFeaturesDaily.ticker == ticker,
            AttentionFeaturesDaily.date == event_date,
        )
        .execution_options(populate_existing=True)
    )
    row = row_result.scalars().first()

    logger.info(
        "Materialized features for %s on %s: wiki=%s gdelt_1d=%d gdelt_3d=%d",
        ticker, event_date, wiki_views, gdelt_article_count_1d, gdelt_article_count_3d,
    )
    return row