from io import StringIO
import lxml.html
import pandas as pd
import requests
from typing import List
from queries.footballguys import constants as fbgc


def url_to_pandas(url) -> List[pd.DataFrame]:
    page = requests.get(url)
    table = pd.read_html(StringIO(page.text.replace("<br>", "-")))
    return table


def create_html_table_from_header_body(header_html_str: str, body_html_str: str):
    return f"""
<table>
{header_html_str}
{body_html_str}
</table>
"""


def extract_snaps_to_pandas(url: str):
    root = lxml.html.document_fromstring(requests.get(url).text)
    table_element_list = root.xpath("""//*[@id="stats_snapcounts_data"]/div/table""")
    assert isinstance(table_element_list, list)
    table_element = table_element_list[0]
    assert isinstance(table_element, lxml.html.HtmlElement)
    table_child_list = table_element.getchildren()
    assert len(table_child_list) % 2 == 0  # check is even
    half_len = int(len(table_child_list) / 2)
    df_list = []
    for i in range(half_len):
        table_html = create_html_table_from_header_body(
            lxml.html.tostring(table_child_list[2 * i]), lxml.html.tostring(table_child_list[2 * i + 1])
        ).replace("\\n", "")
        df = pd.read_html(table_html)[0]
        # First column contains name and is initially labeled as each position, example "Quarterback"
        # Insert column at front called POS and fill with current first column label
        position_name = df.columns[0]
        df.insert(0, "POS", position_name)
        df.rename(columns={position_name: "name"}, inplace=True)
        df_list.append(df)
    return df_list


def add_snap_off_def_column(team_snap_df: pd.DataFrame):
    off_def = team_snap_df["POS"].apply(lambda x: fbgc.POSITIONS_TO_OFFENSE_DEFENSE[x])
    team_snap_df.insert(0, "OFF/DEF", off_def)


def add_snap_position_column(
    team_snap_df_list: List[pd.DataFrame],
    position_name_array: List[str] = fbgc.SNAP_PAGE_POSITON_ORDER,
):
    # blank player names between positions, so we can use cumsum
    # 8/22/23 - We are currently failing here because snap counts are incorrectly not split by position atm
    assert len(team_snap_df_list) == len(position_name_array)
    for pos_idx, pos_df in enumerate(team_snap_df_list):
        pos_df.insert(0, "POS", position_name_array[pos_idx])


def set_multilevel_columns(df):
    new_cols = [tuple(x.split("-")) if "-" in x else (x, x) for x in df.columns]
    df.columns = pd.MultiIndex.from_tuples(new_cols)


def parse_snaps(team_short_name: str, base_url: str = fbgc.BASE_URL, year: int = fbgc.YEAR) -> pd.DataFrame:
    print(f"Attempting to parse snaps for {team_short_name}")
    team_snap_df_list = parse_team_page(team_short_name, base_url, "snap-counts", year)
    team_snap_df = pd.concat(team_snap_df_list)
    # add_snap_off_def_column(team_snap_df)
    split_snap_count_percents(team_snap_df)
    team_snap_df.dropna(subset=["name"], inplace=True)
    # set_multilevel_columns(team_snap_df)
    return team_snap_df


def add_targets_position(team_df: pd.DataFrame):
    # fill blanks up by reversing index, fill down, and re-reversing
    positions = team_df.name.apply(lambda x: x.replace(" Totals", "") if " Totals" in x else None)[::-1].ffill()[::-1]
    team_df.insert(0, "POS", positions)


def parse_targets(team_short_name: str, base_url: str = fbgc.BASE_URL, year: int = fbgc.YEAR) -> pd.DataFrame:
    # snaps are index 2
    print(f"Attempting to parse targets for {team_short_name}")
    team_df = parse_team_page(team_short_name, base_url, "targets", year)[0]
    add_targets_position(team_df)
    return team_df[team_df.name.notna()]


def parse_redzone(team_short_name: str, base_url: str = fbgc.BASE_URL, year: int = fbgc.YEAR) -> pd.DataFrame:
    # snaps are index 3
    print(f"Attempting to parse redzone for {team_short_name}")
    team_df = parse_team_page(team_short_name, base_url, "redzone", year)[0]
    add_targets_position(team_df)
    return team_df[team_df.name.notna()]


def split_snap_count_percents(team_snap_df: pd.DataFrame):
    for week in range(1, 18):
        if f"Wk {week}" not in team_snap_df.columns:
            continue
        # if values are all NaN column will be dtype float 64 and should skip
        if team_snap_df[f"Wk {week}"].dtype == float:
            team_snap_df[f"{week}-count"] = 0
            team_snap_df[f"{week}-%"] = 0.0
        else:
            week_split = team_snap_df[f"Wk {week}"].astype(str).str.split("-")
            week_count = week_split.apply(lambda x: 0 if len(x) == 1 or x[0] == "" else int(x[0]))
            week_pct = week_split.apply(lambda x: 0.0 if len(x) == 1 else float(x[1].strip("%")) / 100.0)
            team_snap_df[f"{week}-count"] = week_count
            team_snap_df[f"{week}-%"] = week_pct
        team_snap_df.drop(columns=f"Wk {week}", inplace=True)


def parse_team_page(
    team_short_name: str,
    base_url: str,
    stat_name: str,
    year: int,
) -> List[pd.DataFrame]:
    url = f"{base_url}/{stat_name}/teams?team={team_short_name}&year={year}"
    if stat_name == "snap-counts":
        all_tables = extract_snaps_to_pandas(url)
    else:
        all_tables = url_to_pandas(url)
    return all_tables