import datetime
import urllib.parse
import pandas as pd
from typing import Any, Dict

from creation_helpers import (
    get_all_pybaseball_ids,
    sanitize_name,
    CLUB_LIST,
    FRANCHISE_LIST,
    pd_players_df,
    mlbteam_and_franchise,
    NEW_PLAYER_COST,
    RARITY_BASE_COSTS,
    should_update_player_description,
    calculate_rarity_cost_adjustment,
    DEFAULT_STARTER_OPS,
    DEFAULT_RELIEVER_OPS,
)
from db_calls import db_post, db_get, db_put, db_patch
from defenders import calcs_defense as cde
from . import calcs_pitcher as cpi
from exceptions import logger
from rarity_thresholds import get_pitcher_thresholds


def get_pitching_stats(
    file_path: str = None,
    start_date: datetime.datetime = None,
    end_date: datetime.datetime = None,
    ignore_limits: bool = False,
):
    print("Reading pitching stats...")
    min_vl = 20 if not ignore_limits else 1
    min_vr = 40 if not ignore_limits else 1
    if file_path is not None:
        vl_basic = pd.read_csv(f"{file_path}vlhh-basic.csv").query(f"TBF >= {min_vl}")
        vr_basic = pd.read_csv(f"{file_path}vrhh-basic.csv").query(f"TBF >= {min_vr}")
        total_basic = pd.merge(
            vl_basic, vr_basic, on="playerId", suffixes=("_vL", "_vR")
        )

        vl_rate = pd.read_csv(f"{file_path}vlhh-rate.csv").query(f"TBF >= {min_vl}")
        vr_rate = pd.read_csv(f"{file_path}vrhh-rate.csv").query(f"TBF >= {min_vr}")
        total_rate = pd.merge(vl_rate, vr_rate, on="playerId", suffixes=("_vL", "_vR"))

        return pd.merge(total_basic, total_rate, on="playerId", suffixes=("", "_rate"))

    else:
        raise LookupError(
            "Date-based stat pulls not implemented, yet. Please provide batting csv files."
        )
        # vrb_url = f'https://www.fangraphs.com/leaders/splits-leaderboards?splitArr=6&splitArrPitch=&position=P' \
        #           f'&autoPt=false&splitTeams=false&statType=player&statgroup=1' \
        #           f'&startDate={start_date.year}-{start_date.month}-{start_date.day}' \
        #           f'&endDate={end_date.year}-{end_date.month}-{end_date.day}' \
        #           f'&players=&filter=&groupBy=season&sort=4,1&wxTemperature=&wxPressure=&wxAirDensity=' \
        #           f'&wxElevation=&wxWindSpeed='
        # vrr_url = f'https://www.fangraphs.com/leaders/splits-leaderboards?splitArr=6&splitArrPitch=&position=P' \
        #           f'&autoPt=false&splitTeams=false&statType=player&statgroup=3' \
        #           f'&startDate={start_date.year}-{start_date.month}-{start_date.day}' \
        #           f'&endDate={end_date.year}-{end_date.month}-{end_date.day}' \
        #           f'&players=&filter=&groupBy=season&sort=4,1&wxTemperature=&wxPressure=&wxAirDensity=' \
        #           f'&wxElevation=&wxWindSpeed='
        # vlb_url = f'https://www.fangraphs.com/leaders/splits-leaderboards?splitArr=5&splitArrPitch=&position=P' \
        #           f'&autoPt=false&splitTeams=false&statType=player&statgroup=1' \
        #           f'&startDate={start_date.year}-{start_date.month}-{start_date.day}' \
        #           f'&endDate={end_date.year}-{end_date.month}-{end_date.day}' \
        #           f'&players=&filter=&groupBy=season&sort=4,1&wxTemperature=&wxPressure=&wxAirDensity=' \
        #           f'&wxElevation=&wxWindSpeed='
        # vlr_url = f'https://www.fangraphs.com/leaders/splits-leaderboards?splitArr=5&splitArrPitch=&position=P' \
        #           f'&autoPt=false&splitTeams=false&statType=player&statgroup=3' \
        #           f'&startDate={start_date.year}-{start_date.month}-{start_date.day}' \
        #           f'&endDate={end_date.year}-{end_date.month}-{end_date.day}' \
        #           f'&players=&filter=&groupBy=season&sort=4,1&wxTemperature=&wxPressure=&wxAirDensity=' \
        #           f'&wxElevation=&wxWindSpeed='
        #
        # soup = BeautifulSoup(requests.get(vrb_url).text, 'html.parser')
        # time.sleep(3)
        # table = soup.find('a', {'class': 'data-export'})


async def pd_pitchingcards_df(cardset_id: int):
    bc_query = await db_get(
        "pitchingcards", params=[("cardset_id", cardset_id), ("short_output", True)]
    )
    if bc_query["count"] == 0:
        raise ValueError("No pitching cards returned from Paper Dynasty API")
    return pd.DataFrame(bc_query["cards"]).rename(
        columns={"id": "pitchingcard_id", "player": "player_id"}
    )


async def pd_pitchingcardratings_df(
    cardset_id: int, season: int, pitching_cards: pd.DataFrame = None
):
    vl_query = await db_get(
        "pitchingcardratings",
        params=[("cardset_id", cardset_id), ("vs_hand", "L"), ("short_output", True)],
    )
    vr_query = await db_get(
        "pitchingcardratings",
        params=[("cardset_id", cardset_id), ("vs_hand", "R"), ("short_output", True)],
    )
    if 0 in [vl_query["count"], vr_query["count"]]:
        raise ValueError("No pitching card ratings returned from Paper Dynasty API")
    vl = pd.DataFrame(vl_query["ratings"])
    vr = pd.DataFrame(vr_query["ratings"])
    ratings = pd.merge(vl, vr, on="pitchingcard", suffixes=("_vL", "_vR")).rename(
        columns={"pitchingcard": "pitchingcard_id"}
    )

    def get_total_ops(df_data):
        ops_vl = df_data["obp_vL"] + df_data["slg_vL"]
        ops_vr = df_data["obp_vR"] + df_data["slg_vR"]
        return (ops_vr + ops_vl + max(ops_vl, ops_vr)) / 3

    ratings["total_OPS"] = ratings.apply(get_total_ops, axis=1)

    # Get season-appropriate rarity thresholds
    thresholds = get_pitcher_thresholds(season)

    # Need starter_rating to determine rarity - merge with pitching cards if provided
    if pitching_cards is not None:
        ratings = pd.merge(
            ratings,
            pitching_cards[["pitchingcard_id", "starter_rating"]],
            on="pitchingcard_id",
            how="left",
        )

        def new_rarity_id(df_data):
            if pd.isna(df_data.get("starter_rating")):
                return 5  # Default to Common if no starter rating
            if df_data["starter_rating"] > 3:
                return thresholds.get_rarity_for_starter(df_data["total_OPS"])
            else:
                return thresholds.get_rarity_for_reliever(df_data["total_OPS"])

        ratings["new_rarity_id"] = ratings.apply(new_rarity_id, axis=1)

        # Drop starter_rating as it will be re-merged from pitching_cards in post_player_updates
        ratings = ratings.drop(columns=["starter_rating"])

    return ratings


def match_player_lines(
    all_pitching: pd.DataFrame,
    all_players: pd.DataFrame,
    df_p: pd.DataFrame,
    is_custom: bool = False,
):
    def get_pids(df_data):
        return get_all_pybaseball_ids(
            [df_data["playerId"]], "fangraphs", is_custom, df_data["Name_vL"]
        )

    print("Now pulling mlbam player IDs...")
    ids_and_names = all_pitching.apply(get_pids, axis=1)
    player_data = (
        ids_and_names.merge(
            all_players, how="left", left_on="key_bbref", right_on="bbref_id"
        )
        .query("key_mlbam == key_mlbam")
        .set_index("key_bbref", drop=False)
    )
    print("Matched mlbam to pd players.")

    step_pitching = pd.merge(
        player_data,
        all_pitching,
        left_on="key_fangraphs",
        right_on="playerId",
        sort=False,
    ).set_index("key_bbref", drop=False)
    final_pitching = step_pitching.join(df_p, rsuffix="_r")

    return final_pitching


async def create_new_players(
    final_pitching: pd.DataFrame,
    cardset: dict,
    card_base_url: str,
    release_dir: str,
    player_desc: str,
):
    new_players = []
    new_mlbplayers = {}

    def create_pitchers(df_data):
        f_name = sanitize_name(df_data["name_first"]).title()
        l_name = sanitize_name(df_data["name_last"]).title()
        new_players.append(
            {
                "p_name": f"{f_name} {l_name}",
                "cost": NEW_PLAYER_COST,
                "image": f"{card_base_url}/{df_data['player_id']}/"
                f"pitchingcard{urllib.parse.quote('?d=')}{release_dir}",
                "mlbclub": CLUB_LIST[df_data["Tm_vL"]],
                "franchise": FRANCHISE_LIST[df_data["Tm_vL"]],
                "cardset_id": cardset["id"],
                "set_num": int(float(df_data["key_fangraphs"])),
                "rarity_id": 99,
                "pos_1": "P",
                "description": f"{player_desc}",
                "bbref_id": df_data.name,
                "fangr_id": int(float(df_data["key_fangraphs"])),
                "strat_code": int(float(df_data["key_mlbam"])),
            }
        )
        new_mlbplayers[df_data.name] = {
            "first_name": sanitize_name(df_data["name_first"]).title(),
            "last_name": sanitize_name(df_data["name_last"]).title(),
            "key_mlbam": int(float(df_data["key_mlbam"])),
            "key_fangraphs": int(float(df_data["key_fangraphs"])),
            "key_bbref": df_data["key_bbref"],
            "key_retro": df_data["key_retro"],
        }

    final_pitching[final_pitching["player_id"].isnull()].apply(create_pitchers, axis=1)
    print(f"Creating {len(new_players)} new players...")
    for x in new_players:
        mlb_query = await db_get("mlbplayers", params=[("key_bbref", x["bbref_id"])])
        if mlb_query["count"] > 0:
            x["mlbplayer_id"] = mlb_query["players"][0]["id"]
        else:
            new_mlb = await db_post(
                "mlbplayers/one", payload=new_mlbplayers[x["bbref_id"]]
            )
            x["mlbplayer_id"] = new_mlb["id"]

        this_player = await db_post("players", payload=x)
        final_pitching.at[x["bbref_id"], "player_id"] = this_player["player_id"]
        final_pitching.at[x["bbref_id"], "p_name"] = this_player["p_name"]
    print(
        f"Player IDs linked to pitching stats.\n{len(final_pitching.values)} players remain\n"
    )

    return len(new_players)


def get_stat_df(input_path: str, final_pitching: pd.DataFrame):
    def get_hand(df_data):
        if df_data["Name"][-1] == "*":
            return "L"
        else:
            return "R"

    print("Reading pitching peripheral stats...")
    pit_data = (
        pd.read_csv(f"{input_path}pitching.csv")
        .drop_duplicates(subset=["Name-additional"], keep="first")
        .set_index("Name-additional")
    )
    pit_data["pitch_hand"] = pit_data.apply(get_hand, axis=1)
    pitching_stats = final_pitching.join(pit_data, lsuffix="_l")
    print(f"Stats are tallied\n{len(pitching_stats.values)} players remain\n")

    return pitching_stats


async def calculate_pitching_cards(
    pitching_stats: pd.DataFrame, cardset: dict, season_pct: float, post_pitchers: bool
):
    pitching_cards = []

    def create_pitching_card(df_data):
        logger.info(
            f"Creating pitching card for {df_data['name_first']} {df_data['name_last']} / fg ID: {df_data['key_fangraphs']}"
        )
        pow_data = cde.pow_ratings(
            float(df_data["Inn_def"]), df_data["GS"], df_data["G"]
        )
        try:
            pitching_cards.append(
                {
                    "player_id": int(float(df_data["player_id"])),
                    "key_bbref": df_data.name,
                    "key_fangraphs": int(float(df_data["key_fangraphs"])),
                    "key_mlbam": int(float(df_data["key_mlbam"])),
                    "key_retro": df_data["key_retro"],
                    "name_first": df_data["name_first"].title(),
                    "name_last": df_data["name_last"].title(),
                    "balk": cpi.balks(df_data["BK"], df_data["IP"], season_pct),
                    "wild_pitch": cpi.wild_pitches(
                        df_data["WP"], df_data["IP"], season_pct
                    ),
                    "hold": cde.hold_pitcher(
                        df_data["caught_stealing_perc"],
                        int(df_data["pickoffs"]),
                        season_pct,
                    ),
                    "starter_rating": pow_data[0],
                    "relief_rating": pow_data[1],
                    "closer_rating": cpi.closer_rating(
                        int(df_data["GF"]), int(df_data["SV"]), int(df_data["G"])
                    ),
                    "hand": df_data["pitch_hand"],
                    "batting": f"#1W{df_data['pitch_hand']}-C",
                }
            )
        except Exception:
            logger.exception(
                f"Skipping fg ID {df_data['key_fangraphs']} due to exception"
            )

    print("Calculating pitching cards...")
    pitching_stats.apply(create_pitching_card, axis=1)
    print("Cards are complete.\n\nPosting cards now...")
    if post_pitchers:
        resp = await db_put(
            "pitchingcards", payload={"cards": pitching_cards}, timeout=30
        )
        print(
            f"Response: {resp}\n\nMatching pitching card database IDs to player stats..."
        )

    pc_df = await pd_pitchingcards_df(cardset["id"])
    pitching_stats = pitching_stats.merge(pc_df, how="left", on="player_id").set_index(
        "key_bbref", drop=False
    )

    return pitching_stats


async def create_position(
    season_pct: float,
    pitching_stats: pd.DataFrame,
    post_pitchers: bool,
    df_p: pd.DataFrame,
):
    pit_positions = []

    def create_pit_position(df_data):
        if df_data["key_bbref"] in df_p.index:
            logger.debug(f"Running P stats for {df_data['p_name']}")
            pit_positions.append(
                {
                    "player_id": int(df_data["player_id"]),
                    "position": "P",
                    "innings": float(df_p.at[df_data["key_bbref"], "Inn_def"]),
                    "range": cde.range_pitcher(
                        rs_value=int(df_p.at[df_data["key_bbref"], "bis_runs_total"]),
                        season_pct=season_pct,
                    ),
                    "error": cde.get_any_error(
                        pos_code="p",
                        errors=int(df_p.at[df_data["key_bbref"], "E_def"]),
                        chances=int(df_p.at[df_data["key_bbref"], "chances"]),
                        season_pct=season_pct,
                    ),
                }
            )
        else:
            try:
                pit_positions.append(
                    {
                        "player_id": int(float(df_data["player_id"])),
                        "position": "P",
                        "innings": 1,
                        "range": 5,
                        "error": 51,
                    }
                )
            except Exception:
                logger.error(
                    f"Could not create pitcher position for {df_data['key_bbref']}"
                )

    print("Calculating pitcher fielding lines now...")
    pitching_stats.apply(create_pit_position, axis=1)
    print("Fielding is complete.\n\nPosting positions now...")
    if post_pitchers:
        resp = await db_put(
            "cardpositions", payload={"positions": pit_positions}, timeout=30
        )
        print(f"Response: {resp}\n")


async def calculate_pitcher_ratings(pitching_stats: pd.DataFrame, post_pitchers: bool):
    pitching_ratings = []

    def create_pitching_card_ratings(df_data):
        logger.info(f"Calculating pitching card ratings for {df_data.name}")
        try:
            pitching_ratings.extend(cpi.get_pitcher_ratings(df_data))
        except Exception:
            logger.error(
                f"Could not create a pitching card for {df_data['key_fangraphs']}"
            )

    print("Calculating card ratings...")
    pitching_stats.apply(create_pitching_card_ratings, axis=1)
    print("Ratings are complete\n\nPosting ratings now...")
    if post_pitchers:
        resp = await db_put(
            "pitchingcardratings", payload={"ratings": pitching_ratings}, timeout=30
        )
        print(f"Response: {resp}\n\nPulling all positions to set player positions...")


async def post_player_updates(
    cardset: Dict[str, Any],
    player_description: str,
    card_base_url: str,
    release_dir: str,
    is_liveseries: bool,
    post_players: bool,
    season: int,
) -> int:
    p_data = await pd_players_df(cardset["id"])
    p_data.set_index("player_id", drop=False)

    # Use LEFT JOIN to keep all pitchers, even those without ratings
    pitching_cards = await pd_pitchingcards_df(cardset["id"])
    pitching_ratings = await pd_pitchingcardratings_df(
        cardset["id"], season, pitching_cards
    )

    total_ratings = pd.merge(
        pitching_cards,
        pitching_ratings,
        on="pitchingcard_id",
        how="left",  # Keep all pitching cards
    )

    # Assign default rarity (Common/5) for pitchers without ratings
    if "new_rarity_id" not in total_ratings.columns:
        total_ratings["new_rarity_id"] = 5
    elif total_ratings["new_rarity_id"].isna().any():
        total_ratings["new_rarity_id"] = total_ratings["new_rarity_id"].fillna(5)

    # Assign default total_OPS for pitchers without ratings (Common reliever default)
    if "total_OPS" in total_ratings.columns:
        missing_ops = total_ratings[total_ratings["total_OPS"].isna()]
        if not missing_ops.empty:
            logger.warning(
                f"pitchers.creation.post_player_updates - {len(missing_ops)} pitchers missing total_OPS, assigning default 0.702: {missing_ops[['player_id', 'pitchingcard_id']].to_dict('records')}"
            )
        total_ratings["total_OPS"] = total_ratings["total_OPS"].fillna(0.702)

    player_data = pd.merge(p_data, total_ratings, on="player_id").set_index(
        "player_id", drop=False
    )
    del total_ratings

    # p_query = await db_get('mlbplayers')
    # mlb_players = pd.DataFrame(p_query['players'])

    def get_pids(df_data):
        # if df_data['key_mlbam'] in
        return get_all_pybaseball_ids([df_data["bbref_id"]], "bbref")

    ids_and_names = player_data.apply(get_pids, axis=1)
    player_data = (
        ids_and_names.merge(
            player_data, how="left", left_on="key_bbref", right_on="bbref_id"
        )
        .query("key_mlbam == key_mlbam")
        .set_index("key_bbref", drop=False)
    )

    player_updates = {}  # { <player_id> : [ (param pairs) ] }
    sp_rarity_group = player_data.query(
        "rarity == new_rarity_id and starter_rating >= 4"
    ).groupby("rarity")
    sp_average_ops = sp_rarity_group["total_OPS"].mean().to_dict()
    rp_rarity_group = player_data.query(
        "rarity == new_rarity_id and starter_rating < 4"
    ).groupby("rarity")
    rp_average_ops = rp_rarity_group["total_OPS"].mean().to_dict()

    # Fill in missing rarity averages with defaults
    for rarity, default_ops in DEFAULT_STARTER_OPS.items():
        if rarity not in sp_average_ops:
            sp_average_ops[rarity] = default_ops

    for rarity, default_ops in DEFAULT_RELIEVER_OPS.items():
        if rarity not in rp_average_ops:
            rp_average_ops[rarity] = default_ops

    def get_player_updates(df_data):
        def avg_ops(rarity_id, starter_rating):
            if starter_rating >= 4:
                return sp_average_ops[rarity_id]
            else:
                return rp_average_ops[rarity_id]

        params = []

        # Check if description should be updated using extracted business logic
        if should_update_player_description(
            cardset_name=cardset["name"],
            player_cost=df_data["cost"],
            current_description=df_data["description"],
            new_description=player_description,
        ):
            params = [("description", f"{player_description}")]
            logger.debug(
                f"pitchers.creation.post_player_updates - Setting description for player_id={df_data['player_id']}: "
                f"'{df_data['description']}' -> '{player_description}' (cost={df_data['cost']}, cardset={cardset['name']})"
            )
        else:
            logger.debug(
                f"pitchers.creation.post_player_updates - Skipping description update for player_id={df_data['player_id']}: "
                f"current='{df_data['description']}', proposed='{player_description}' (cost={df_data['cost']}, cardset={cardset['name']})"
            )

        if is_liveseries:
            team_data = mlbteam_and_franchise(int(float(df_data["key_mlbam"])))

            if (
                df_data["mlbclub"] != team_data["mlbclub"]
                and team_data["mlbclub"] is not None
            ):
                params.extend([("mlbclub", team_data["mlbclub"])])
            if (
                df_data["franchise"] != team_data["franchise"]
                and team_data["franchise"] is not None
            ):
                params.extend([("franchise", team_data["franchise"])])

        # if release_directory not in df_data['image']:
        params.extend(
            [
                (
                    "image",
                    f"{card_base_url}/{df_data['player_id']}/pitchingcard"
                    f"{urllib.parse.quote('?d=')}{release_dir}",
                )
            ]
        )

        if df_data["cost"] == NEW_PLAYER_COST:
            params.extend(
                [
                    (
                        "cost",
                        round(
                            RARITY_BASE_COSTS[df_data["new_rarity_id"]]
                            * df_data["total_OPS"]
                            / avg_ops(
                                df_data["new_rarity_id"], df_data["starter_rating"]
                            )
                        ),
                    ),
                    ("rarity_id", df_data["new_rarity_id"]),
                ]
            )

        elif df_data["rarity"] != df_data["new_rarity_id"]:
            # Calculate adjusted cost for rarity change using lookup table
            new_cost = calculate_rarity_cost_adjustment(
                old_rarity=df_data["rarity"],
                new_rarity=df_data["new_rarity_id"],
                old_cost=df_data["cost"],
            )
            params.extend([("cost", new_cost), ("rarity_id", df_data["new_rarity_id"])])

        if len(params) > 0:
            if df_data.player_id not in player_updates.keys():
                player_updates[df_data.player_id] = params
            else:
                player_updates[df_data.player_id].extend(params)

    player_data.apply(get_player_updates, axis=1)

    print(f"Sending {len(player_updates)} player updates to PD database...")
    if post_players:
        for x in player_updates:
            await db_patch("players", object_id=x, params=player_updates[x])

    return len(player_updates)


async def run_pitchers(
    cardset: dict,
    input_path: str,
    card_base_url: str,
    season: int,
    release_directory: str,
    player_description: str,
    season_pct: float,
    post_players: bool,
    post_pitchers: bool,
    is_liveseries: bool,
    ignore_limits: bool,
    pull_fielding: bool = True,
    is_custom: bool = False,
):
    print("Pulling PD player IDs...")
    pd_players = await pd_players_df(cardset["id"])

    all_stats = get_pitching_stats(file_path=input_path, ignore_limits=ignore_limits)
    print(f"Processed {len(all_stats.values)} pitchers\n")

    print("Pulling pitcher defense...")
    if pull_fielding:
        df_p = cde.get_bbref_fielding_df("p", season)
    else:
        df_p = pd.DataFrame()

    pit_step1 = match_player_lines(all_stats, pd_players, df_p, is_custom)
    if post_players:
        new_pitchers = await create_new_players(
            pit_step1, cardset, card_base_url, release_directory, player_description
        )
    else:
        new_pitchers = 0

    pitching_stats = get_stat_df(input_path, pit_step1)
    del all_stats, pit_step1

    pitching_stats = await calculate_pitching_cards(
        pitching_stats, cardset, season_pct, post_pitchers
    )

    await create_position(season_pct, pitching_stats, post_pitchers, df_p)
    await calculate_pitcher_ratings(pitching_stats, post_pitchers)
    await post_player_updates(
        cardset,
        player_description,
        card_base_url,
        release_directory,
        is_liveseries,
        post_players,
        season,
    )

    return {
        "tot_pitchers": len(pitching_stats.index),
        "new_pitchers": new_pitchers,
        "pitching_stats": pitching_stats,
    }