"""StatsBomb event stream data to SPADL converter."""

import warnings
from typing import Any, Optional, cast

import numpy as np
import numpy.typing as npt
import pandas as pd  # type: ignore
from pandera.typing import DataFrame

from . import config as spadlconfig
from .base import _add_dribbles, _fix_clearances, _fix_direction_of_play
from .schema import SPADLSchema


def convert_to_actions(
    events: pd.DataFrame,
    home_team_id: int,
    xy_fidelity_version: Optional[int] = None,
    shot_fidelity_version: Optional[int] = None,
) -> DataFrame[SPADLSchema]:
    """
    Convert StatsBomb events to SPADL actions.

    Parameters
    ----------
    events : pd.DataFrame
        DataFrame containing StatsBomb events from a single game.
    home_team_id : int
        ID of the home team in the corresponding game.
    xy_fidelity_version : int, optional
        Whether low or high fidelity coordinates are used in the event data.
        If not specified, the fidelity version is inferred from the data.
    shot_fidelity_version : int, optional
        Whether low or high fidelity coordinates are used in the event data
        for shots. If not specified, the fidelity version is inferred from the
        data.

    Returns
    -------
    actions : pd.DataFrame
        DataFrame with corresponding SPADL actions.

    """
    actions = pd.DataFrame()

    # Determine xy_fidelity_version and shot_fidelity_version
    infered_xy_fidelity_version, infered_shot_fidelity_version = _infer_xy_fidelity_versions(
        events
    )
    if xy_fidelity_version is None:
        xy_fidelity_version = infered_xy_fidelity_version
        warnings.warn(
            f"Inferred xy_fidelity_version={infered_xy_fidelity_version}."
            + " If this is incorrect, please specify the correct version"
            + " using the xy_fidelity_version argument"
        )
    else:
        assert xy_fidelity_version in [1, 2], "xy_fidelity_version must be 1 or 2"
    if shot_fidelity_version is None:
        if xy_fidelity_version == 2:
            shot_fidelity_version = 2
        else:
            shot_fidelity_version = infered_shot_fidelity_version
            warnings.warn(
                f"Inferred shot_fidelity_version={infered_shot_fidelity_version}."
                + " If this is incorrect, please specify the correct version"
                + " using the shot_fidelity_version argument"
            )
    else:
        assert shot_fidelity_version in [1, 2], "shot_fidelity_version must be 1 or 2"

    events = events.copy()
    events = _insert_interception_passes(events)
    events["extra"].fillna({}, inplace=True)

    actions["game_id"] = events.game_id
    actions["original_event_id"] = events.event_id
    actions["period_id"] = events.period_id
    actions["time_seconds"] = pd.to_timedelta(events.timestamp).dt.total_seconds()
    actions["team_id"] = events.team_id
    actions["player_id"] = events.player_id

    # split (end)location column into x and y columns
    end_location = events[["location", "extra"]].apply(_get_end_location, axis=1)
    # convert StatsBomb coordinates to spadl coordinates
    actions.loc[events.type_name == "Shot", ["start_x", "start_y"]] = _convert_locations(
        events.loc[events.type_name == "Shot", "location"],
        shot_fidelity_version,
    )
    actions.loc[events.type_name != "Shot", ["start_x", "start_y"]] = _convert_locations(
        events.loc[events.type_name != "Shot", "location"],
        shot_fidelity_version,
    )
    actions.loc[events.type_name == "Shot", ["end_x", "end_y"]] = _convert_locations(
        end_location.loc[events.type_name == "Shot"],
        shot_fidelity_version,
    )
    actions.loc[events.type_name != "Shot", ["end_x", "end_y"]] = _convert_locations(
        end_location.loc[events.type_name != "Shot"],
        shot_fidelity_version,
    )

    actions[["type_id", "result_id", "bodypart_id"]] = events[["type_name", "extra"]].apply(
        _parse_event, axis=1, result_type="expand"
    )

    actions = (
        actions[actions.type_id != spadlconfig.actiontypes.index("non_action")]
        .sort_values(["game_id", "period_id", "time_seconds"], kind="mergesort")
        .reset_index(drop=True)
    )
    actions = _fix_direction_of_play(actions, home_team_id)
    actions = _fix_clearances(actions)

    actions["action_id"] = range(len(actions))
    actions = _add_dribbles(actions)

    return cast(DataFrame[SPADLSchema], actions)


Location = tuple[float, float]


def _insert_interception_passes(df_events: pd.DataFrame) -> pd.DataFrame:
    """Insert interception actions before passes.

    This function converts passes that are also interceptions (type 64) in the
    StatsBomb event data into two separate events, first an interception and
    then a pass.

    Parameters
    ----------
    df_events : pd.DataFrame
        StatsBomb event dataframe

    Returns
    -------
    pd.DataFrame
        StatsBomb event dataframe in which passes that were also denoted as
        interceptions in the StatsBomb notation are transformed into two events.
    """

    def is_interception_pass(x: dict) -> bool:  # type: ignore
        return x.get("extra", {}).get("pass", {}).get("type", {}).get("name") == "Interception"

    df_events_interceptions = df_events[df_events.apply(is_interception_pass, axis=1)].copy()

    if not df_events_interceptions.empty:
        df_events_interceptions["type_name"] = "Interception"
        df_events_interceptions["extra"] = [
            {"interception": {"outcome": {"id": 16, "name": "Success In Play"}}}
        ] * len(df_events_interceptions)

        df_events = pd.concat([df_events_interceptions, df_events], ignore_index=True)
        df_events = df_events.sort_values(["timestamp"], kind="mergesort")
        df_events = df_events.reset_index(drop=True)

    return df_events


def _infer_xy_fidelity_versions(events: pd.DataFrame) -> tuple[int, int]:
    """Find out if x and y are integers disguised as floats."""
    mask_shot = events.type_name == "Shot"
    mask_other = events.type_name != "Shot"
    locations = events.location.apply(pd.Series)
    mask_valid_location = locations.notna().any(axis=1)
    high_fidelity_shots = (locations.loc[mask_valid_location & mask_shot] % 1 != 0).any(axis=None)
    high_fidelity_other = (locations.loc[mask_valid_location & mask_other] % 1 != 0).any(axis=None)
    xy_fidelity_version = 2 if high_fidelity_other else 1
    shot_fidelity_version = 2 if high_fidelity_shots else xy_fidelity_version
    return shot_fidelity_version, xy_fidelity_version


def _convert_locations(locations: pd.Series, fidelity_version: int) -> npt.NDArray[np.float32]:
    """Convert StatsBomb locations to spadl coordinates.

    StatsBomb coordinates are cell-based, using a 120x80 grid, so 1,1 is the
    top-left square 'yard' of the field (in landscape), even though 0,0 is the
    true coordinate of the corner flag.

    Some matches have metadata like "xy_fidelity_version" : "2", which means
    the grid has higher granularity. In this case 0.1,0.1 is the top left
    cell.
    """
    # [1, 120] x [1, 80]
    # +-----+------+
    # | 1,1 | 2, 1 |
    # +-----+------+
    # | 1,2 | 2,2  |
    # +-----+------+
    cell_side = 0.1 if fidelity_version == 2 else 1.0
    cell_relative_center = cell_side / 2
    coordinates = np.empty((len(locations), 2), dtype=float)
    for i, loc in enumerate(locations):
        if isinstance(loc, list) and len(loc) == 2:
            coordinates[i, 0] = (loc[0] - cell_relative_center) / 120 * spadlconfig.field_length
            coordinates[i, 1] = (
                spadlconfig.field_width
                - (loc[1] - cell_relative_center) / 80 * spadlconfig.field_width
            )
        elif isinstance(loc, list) and len(loc) == 3:
            # A coordinate in the goal frame, only used for the end location of
            # Shot events. The y-coordinates and z-coordinates are always detailed
            # to a tenth of a yard.
            coordinates[i, 0] = (loc[0] - cell_relative_center) / 120 * spadlconfig.field_length
            coordinates[i, 1] = (
                spadlconfig.field_width - (loc[1] - 0.05) / 80 * spadlconfig.field_width
            )
    coordinates[:, 0] = np.clip(coordinates[:, 0], 0, spadlconfig.field_length)
    coordinates[:, 1] = np.clip(coordinates[:, 1], 0, spadlconfig.field_width)
    return coordinates


def _get_end_location(q: tuple[Location, dict[str, Any]]) -> Location:
    start_location, extra = q
    for event in ["pass", "shot", "carry"]:
        if event in extra and "end_location" in extra[event]:
            return extra[event]["end_location"]
    return start_location


def _parse_event(q: tuple[str, dict[str, Any]]) -> tuple[int, int, int]:
    t, x = q
    events = {
        "Pass": _parse_pass_event,
        "Dribble": _parse_dribble_event,
        "Carry": _parse_carry_event,
        "Foul Committed": _parse_foul_event,
        "Duel": _parse_duel_event,
        "Interception": _parse_interception_event,
        "Shot": _parse_shot_event,
        "Own Goal Against": _parse_own_goal_event,
        "Goal Keeper": _parse_goalkeeper_event,
        "Clearance": _parse_clearance_event,
        "Miscontrol": _parse_miscontrol_event,
    }
    parser = events.get(t, _parse_event_as_non_action)
    a, r, b = parser(x)
    actiontype = spadlconfig.actiontypes.index(a)
    result = spadlconfig.results.index(r)
    bodypart = spadlconfig.bodyparts.index(b)
    return actiontype, result, bodypart


def _parse_event_as_non_action(_extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "non_action"
    r = "success"
    b = "foot"
    return a, r, b


def _parse_pass_event(extra: dict[str, Any]) -> tuple[str, str, str]:  # noqa: C901
    a = "pass"  # default
    b = "foot"  # default
    p = extra.get("pass", {})
    ptype = p.get("type", {}).get("name")
    height = p.get("height", {}).get("name")
    cross = p.get("cross")
    if ptype == "Free Kick":
        if height == "High Pass" or cross:
            a = "freekick_crossed"
        else:
            a = "freekick_short"
    elif ptype == "Corner":
        if height == "High Pass" or cross:
            a = "corner_crossed"
        else:
            a = "corner_short"
    elif ptype == "Goal Kick":
        a = "goalkick"
    elif ptype == "Throw-in":
        a = "throw_in"
        b = "other"
    elif cross:
        a = "cross"
    else:
        a = "pass"

    pass_outcome = extra.get("pass", {}).get("outcome", {}).get("name")
    if pass_outcome in ["Incomplete", "Out"]:
        r = "fail"
    elif pass_outcome == "Pass Offside":
        r = "offside"
    elif pass_outcome in ["Injury Clearance", "Unknown"]:
        # discard passes that are not part of the play
        a = "non_action"
        r = "success"
    else:
        r = "success"

    bp = extra.get("pass", {}).get("body_part", {}).get("name")
    if bp is not None:
        if "Head" in bp:
            b = "head"
        elif bp == "Left Foot":
            b = "foot_left"
        elif bp == "Right Foot":
            b = "foot_right"
        elif "Foot" in bp or bp == "Drop Kick":
            b = "foot"
        else:
            b = "other"

    return a, r, b


def _parse_dribble_event(extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "take_on"

    dribble_outcome = extra.get("dribble", {}).get("outcome", {}).get("name")
    if dribble_outcome == "Incomplete":
        r = "fail"
    elif dribble_outcome == "Complete":
        r = "success"
    else:
        r = "success"

    b = "foot"

    return a, r, b


def _parse_carry_event(_extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "dribble"
    r = "success"
    b = "foot"
    return a, r, b


def _parse_foul_event(extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "foul"

    foul_card = extra.get("foul_committed", {}).get("card", {}).get("name", "")
    if "Yellow" in foul_card:
        r = "yellow_card"
    elif "Red" in foul_card:
        r = "red_card"
    else:
        r = "fail"

    b = "foot"

    return a, r, b


def _parse_duel_event(extra: dict[str, Any]) -> tuple[str, str, str]:
    if extra.get("duel", {}).get("type", {}).get("name") == "Tackle":
        a = "tackle"
        duel_outcome = extra.get("duel", {}).get("outcome", {}).get("name")
        if duel_outcome in ["Lost In Play", "Lost Out"]:
            r = "fail"
        elif duel_outcome in ["Success in Play", "Won"]:
            r = "success"
        else:
            r = "success"

        b = "foot"
        return a, r, b
    return _parse_event_as_non_action(extra)


def _parse_interception_event(extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "interception"
    interception_outcome = extra.get("interception", {}).get("outcome", {}).get("name")
    if interception_outcome in ["Lost In Play", "Lost Out"]:
        r = "fail"
    elif interception_outcome == "Won":
        r = "success"
    else:
        r = "success"
    b = "foot"
    return a, r, b


def _parse_shot_event(extra: dict[str, Any]) -> tuple[str, str, str]:
    extra_type = extra.get("shot", {}).get("type", {}).get("name")
    if extra_type == "Free Kick":
        a = "shot_freekick"
    elif extra_type == "Penalty":
        a = "shot_penalty"
    else:
        a = "shot"

    shot_outcome = extra.get("shot", {}).get("outcome", {}).get("name")
    if shot_outcome == "Goal":
        r = "success"
    elif shot_outcome in ["Blocked", "Off T", "Post", "Saved", "Wayward"]:
        r = "fail"
    else:
        r = "fail"

    bp = extra.get("shot", {}).get("body_part", {}).get("name")
    if bp is None:
        b = "foot"
    elif "Head" in bp:
        b = "head"
    elif bp == "Left Foot":
        b = "foot_left"
    elif bp == "Right Foot":
        b = "foot_right"
    elif "Foot" in bp:
        b = "foot"
    else:
        b = "other"

    return a, r, b


def _parse_own_goal_event(_extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "bad_touch"
    r = "owngoal"
    b = "foot"
    return a, r, b


def _parse_goalkeeper_event(extra: dict[str, Any]) -> tuple[str, str, str]:  # noqa: C901
    extra_type = extra.get("goalkeeper", {}).get("type", {}).get("name")
    if extra_type == "Shot Saved":
        a = "keeper_save"
    elif extra_type in ("Collected", "Keeper Sweeper"):
        a = "keeper_claim"
    elif extra_type == "Punch":
        a = "keeper_punch"
    else:
        a = "non_action"

    goalkeeper_outcome = extra.get("goalkeeper", {}).get("outcome", {}).get("name", "x")
    if goalkeeper_outcome in [
        "Claim",
        "Clear",
        "Collected Twice",
        "In Play Safe",
        "Success",
        "Touched Out",
    ]:
        r = "success"
    elif goalkeeper_outcome in ["In Play Danger", "No Touch"]:
        r = "fail"
    else:
        r = "success"

    bp = extra.get("goalkeeper", {}).get("body_part", {}).get("name")
    if bp is None:
        b = "other"
    elif "Head" in bp:
        b = "head"
    elif bp == "Left Foot":
        b = "foot_left"
    elif bp == "Right Foot":
        b = "foot_right"
    elif "Foot" in bp or bp == "Drop Kick":
        b = "foot"
    else:
        b = "other"

    return a, r, b


def _parse_clearance_event(extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "clearance"
    r = "success"
    bp = extra.get("clearance", {}).get("body_part", {}).get("name")
    if bp is None:
        b = "foot"
    elif "Head" in bp:
        b = "head"
    elif bp == "Left Foot":
        b = "foot_left"
    elif bp == "Right Foot":
        b = "foot_right"
    elif "Foot" in bp:
        b = "foot"
    else:
        b = "other"
    return a, r, b


def _parse_miscontrol_event(_extra: dict[str, Any]) -> tuple[str, str, str]:
    a = "bad_touch"
    r = "fail"
    b = "foot"
    return a, r, b