from __future__ import annotations

from pathlib import Path

import pandas as pd
import pytest

from racing_reports.config import Settings
from racing_reports.datastore import DataStore
from racing_reports.utils import slugify


@pytest.fixture()
def sample_store(tmp_path: Path) -> DataStore:
    settings = Settings(cache_dir=tmp_path / "cache", output_dir=tmp_path / "outputs")
    store = DataStore(settings=settings)
    league = "Spanish Segunda Division"
    season = "25-26"
    path = store.preprocessed_path(league, season)
    path.parent.mkdir(parents=True, exist_ok=True)
    rows = []
    for match_id, home, away, home_id, away_id, date in [
        ("m1", "Racing de Santander", "Almería", "racing", "almeria", "2026-04-12"),
        ("m2", "Huesca", "Racing de Santander", "huesca", "racing", "2026-04-19"),
    ]:
        for sequence_id, phase in [
            ("s1", "Build Up against Low Block"),
            ("s2", "Build Up against Medium Block"),
            ("s3", "Build Up against High Block"),
        ]:
            for team, rival, team_id in [(home, away, home_id), (away, home, away_id)]:
                rows.append(
                    {
                        "matchId": match_id,
                        "fecha": date,
                        "home_team_id": home_id,
                        "away_team_id": away_id,
                        "teamId": team_id,
                        "TeamName": team,
                        "TeamRival": rival,
                        "Competencia": league,
                        "Temporada": season,
                        "sequenceId": sequence_id,
                        "phaseLabel": phase,
                        "event_name": "Goal" if sequence_id == "s1" and team == home else "Pass",
                        "x": 88,
                        "y": 50,
                        "xG": 0.08 if sequence_id == "s1" else 0,
                        "pvAdded": 0.03,
                        "goal_int": 1 if sequence_id == "s1" and team == home else 0,
                    }
                )
    pd.DataFrame(rows).to_csv(path, index=False)
    return store