import aenum
from typing import List, Tuple, Set

import psycopg2.extras

from hanabi import hanab_game
import utils
from database import conn_manager
import games_db_interface
from log_setup import logger


class GameOutcome(aenum.Enum):
    _init_ = 'value string'
    win = 0, 'Win'
    discard_crit = 1, 'Discard Critical'
    bomb_crit = 2, 'Bomb Critical'
    strikeout = 3, 'Strikeout'
    bottom_deck = 4, 'Bottom Deck'
    vote_to_kill = 5, 'Vote to Kill'
    out_of_pace = 6, 'Out of Pace'


class GameAnalysisResult:
    def __init__(self,
                 outcomes: Set[GameOutcome],
                 bdrs: List[Tuple[hanab_game.DeckCard, int]],
                 lost_crits: List[hanab_game.DeckCard]
                 ):
        self.outcomes = outcomes
        self.bdrs = bdrs
        self.lost_crits = lost_crits


def analyze_replay(instance: hanab_game.HanabiInstance, actions: List[hanab_game.Action]) -> GameAnalysisResult:
    # List of bdrs
    bdrs = []
    # This is the default value if we find no other reason why the game was lost (or won)
    outcomes = set()
    lost_crits = []
    game = hanab_game.GameState(instance)

    def handle_lost_card(card, game, play: bool):
        if not game.is_trash(card):
            if game.is_critical(card):
                outcomes.add(GameOutcome.bomb_crit if play else GameOutcome.discard_crit)
                lost_crits.append(card)
            elif card.rank != 1:
                if card in game.deck[game.progress:]:
                    bdrs.append((card, game.draw_pile_size))
            else:
                if game.deck[game.progress:].count(card) == 2:
                    bdrs.append((card, game.draw_pile_size))

    for action in actions:
        if action.type == hanab_game.ActionType.Discard:
            discarded_card = instance.deck[action.target]
            handle_lost_card(discarded_card, game, False)
        if action.type == hanab_game.ActionType.Play:
            played_card = instance.deck[action.target]
            if not game.is_playable(played_card) and not game.is_trash(played_card):
                bombed_card = instance.deck[action.target]
                handle_lost_card(bombed_card, game, True)
        game.make_action(action)
        if game.pace < 0:
            outcomes.add(GameOutcome.out_of_pace)

    if game.strikes == 3:
        outcomes.add(GameOutcome.strikeout)
    elif actions[-1].type in [hanab_game.ActionType.EndGame, hanab_game.ActionType.VoteTerminate]:
        outcomes.add(GameOutcome.vote_to_kill)
    if game.score == 5 * instance.num_suits:
        outcomes.add(GameOutcome.win)

    return GameAnalysisResult(outcomes, bdrs, lost_crits)


def analyze_game_and_store_stats(game_id: int):
    logger.verbose("Analysing game {} for BDRs and lost crits".format(game_id))
    instance, actions = games_db_interface.load_game(game_id)
    analysis = analyze_replay(instance, actions)

    cur = conn_manager.get_new_cursor()
    cur.execute(
        "INSERT INTO game_statistics (game_id, num_bottom_deck_risks, num_crits_lost) "
        "VALUES (%s, %s, %s) "
        "ON CONFLICT (game_id) DO UPDATE "
        "SET (num_bottom_deck_risks, num_crits_lost) = (EXCLUDED.num_bottom_deck_risks, EXCLUDED.num_crits_lost)",
        (game_id, len(analysis.bdrs), len(analysis.lost_crits))
    )
    psycopg2.extras.execute_values(
        cur,
        "INSERT INTO game_outcomes (game_id, outcome) VALUES %s",
        ((game_id, outcome.value) for outcome in analysis.outcomes)
    )
    conn_manager.get_connection().commit()


def analyze_all_games():
    """
    Runs analysis on replays of all games
    @warning: This assumes that detailed game data has been fetched from the server already
    """
    logger.info("Analysing replays of all games.")
    cur = conn_manager.get_new_cursor()
    cur.execute(
        "SELECT id FROM games "
        "LEFT OUTER JOIN game_statistics "
        "  ON games.id = game_statistics.game_id "
        "WHERE game_statistics.game_id IS NULL "
        "ORDER BY games.id"
    )
    for (game_id, ) in cur.fetchall():
        analyze_game_and_store_stats(game_id)


def update_user_statistics():
    """
    Update the cumulative user statistics for this user, assuming that the corresponding game statistics have
    been computed already.
    @param user_ids:
    @return:
    """
    # Note that some of these statistics could be computed by updating them on each new game insertion.
    # However, it would be tedious to ensure that *every* new game triggers an update of these statistics.
    # Also, this would be error-prone, since doing a mistake once means that values will be off forever
    # (unless the DB is reset).
    # Since it is cheap to accumulate some values over the whole DB, we therefore recreate the statistics as a whole,
    # reusing only the individual results (that never change and therefore can only be missing, but never wrong)
    cur = conn_manager.get_new_cursor()

    # Update total number of moves
    for clue_starved in [True, False]:
        rating_type = utils.get_rating_type(clue_starved)
        # We insert 0 here to ensure that we have an entry for each player
        # Note that this will immediately be changed by the next query in case it is nonzero,
        # so the zero value never shows up in the database if it was nonzero before.
        cur.execute(
            "INSERT INTO user_statistics"
            "  (user_id, variant_type, total_game_moves, games_played, games_won, current_streak, maximum_streak, total_bdr, total_crits_lots)"
            "  ("
            "  SELECT id, %s, 0, 0, 0, 0, 0, 0, 0 FROM users"
            "  )"
            "ON CONFLICT (user_id, variant_type) DO UPDATE "
            "SET"
            "  (total_game_moves, games_played, games_won, current_streak, maximum_streak, total_bdr, total_crits_lots)"
            "  ="
            "  (EXCLUDED.total_game_moves, EXCLUDED.games_played, EXCLUDED.games_won, EXCLUDED.current_streak, EXCLUDED.maximum_streak, EXCLUDED.total_bdr, EXCLUDED.total_crits_lots)",
            (rating_type,)
        )

    # Most of the statistics are very easy to compute: We just have to accumulate data from other tables
    cur.execute(
        "INSERT INTO user_statistics (user_id, variant_type, total_game_moves, games_played, games_won, total_bdr, total_crits_lots)"
        "  ("
        "  SELECT"
        "    users.id,"
        "    CASE WHEN clue_starved THEN %s ELSE %s END,"
        "    SUM(games.num_turns),"
        "    COUNT(*),"  # This counts the number of rows (per user id), so the number of played game
        "    COUNT(*) FILTER ( WHERE variants.num_suits * 5 = games.score ),"  # Same, but only count wins now
        "    SUM (game_statistics.num_bottom_deck_risks),"  # Simple accumulation of the game stats
        "    SUM (game_statistics.num_crits_lost)"
        "FROM users"
        "  INNER JOIN game_participants "
        "    ON game_participants.user_id = users.id "
        "  INNER JOIN games "
        "    ON game_participants.game_id = games.id "
        "  INNER JOIN variants"
        "    ON variants.id = games.variant_id "
        "  LEFT OUTER JOIN game_statistics"
        "    ON games.id = game_statistics.game_id"
        "  GROUP BY users.id, clue_starved "
        "  ) "
        "ON CONFLICT (user_id, variant_type) DO UPDATE "
        "SET"
        "  (total_game_moves, games_played, games_won, total_bdr, total_crits_lots)"
        "  ="
        "  (EXCLUDED.total_game_moves, EXCLUDED.games_played, EXCLUDED.games_won, EXCLUDED.total_bdr, EXCLUDED.total_crits_lots)",
        (utils.get_rating_type(True), utils.get_rating_type(False))
    )
    # This computes the maximum streak lengths, it's quite complicated.
    # First (in the innermost select clause), we build up an auxiliary table, which consists of some joined data that
    # we are interested in, but most importantly each row gets a 'group_id' entry in such a way that games belonging
    # to the same streak will have the same group_id:
    # In ascending league_id order, this entry counts the number of *losses* up until this point: Therefore, the number
    # does not increase during win streaks, but increases for each loss.
    # Additionally, we subtract 1 from this sum for lost games, so that losses always have the same group id as the last
    # win immediately before them. Therefore, each group (= entries with the same group id) now consists of
    # some consecutive wins, optionally followed by a loss
    # In the second query, we can now use these group ids to add a 'streak_length' to each row by numbering the rows
    # inside their corresponding group (his is what the OVER (PARTITION BY ..., group_id) does.
    # Now, in a third select statement, it is now easy to calculate the maximum streak by taking the maximum of this
    # row ranging over all games, where we grouped by user id and rating type (Clue Starved/Non-CS currently)
    # Finally, we just wrap the computed data into an insert statement to directly store it in the statistics table
    cur.execute(
        "INSERT INTO user_statistics (user_id, variant_type, maximum_streak, current_streak, maximum_streak_last_game)"
        "  ("
        "  SELECT"
        "    user_id,"
        "    CASE WHEN clue_starved THEN %s ELSE %s END,"
        "    COALESCE(MAX(streak_length), 0) AS maximum_streak,"
        "    COALESCE((ARRAY_AGG(streak_length ORDER BY league_id DESC))[1], 0) AS current_streak,"
        "    ("
        "      ARRAY_AGG(league_id ORDER BY streak_length DESC, league_id ASC)"
        "      FILTER ( WHERE streak_length IS NOT NULL)"
        "    )[1] AS maximum_streak_last_game"
        "  FROM"
        "    ("
        "    SELECT"
        "      *,"
        # Note that here we have the extra distinction to only add a streak_length to wins, not losses.
        # Otherwise, a streak of n games would result in a loss that has 'streak' n + 1, which is not what we want.
        "      CASE"
        "        WHEN num_suits * 5 = score"
        "        THEN"
        "          COUNT(*)"
        "            OVER (PARTITION BY user_id, clue_starved, group_id ORDER BY league_id ASC)"
        "        END"
        "      AS streak_length "
        "    FROM"
        "      ("
        "      SELECT"
        "        users.id AS user_id,"
        "        variants.clue_starved,"
        "        variants.num_suits,"
        "        games.score,"
        "        games.league_id,"
        # This count function is the tricky part that labels each game with the group_id of the streak it belongs to
        "        COUNT(*) "
        "          FILTER (WHERE variants.num_suits * 5 != games.score)"
        "          OVER (PARTITION BY users.id, variants.clue_starved ORDER BY games.league_id)"
        "        - CASE WHEN variants.num_suits * 5 != games.score THEN 1 ELSE 0 END"
        "        AS group_id"
        "      FROM users "
        "      INNER JOIN game_participants "
        "        ON game_participants.user_id = users.id "
        "      INNER JOIN games "
        "        ON game_participants.game_id = games.id "
        "      INNER JOIN variants "
        "        ON variants.id = games.variant_id "
        "      ) AS games_grouped_by_streak "
        "    ) AS games_with_streaks "
        "  GROUP BY user_id, clue_starved"
        "  )"
        "ON CONFLICT (user_id, variant_type) DO UPDATE "
        "SET (maximum_streak, current_streak, maximum_streak_last_game) = (EXCLUDED.maximum_streak, EXCLUDED.current_streak, EXCLUDED.maximum_streak_last_game)",
        (utils.get_rating_type(True), utils.get_rating_type(False))
    )
    conn_manager.get_connection().commit()