from . import DAILY_DIR
from serenitas.analytics.utils import get_fx
from bs4 import BeautifulSoup
from io import BytesIO
from pandas.tseries.offsets import BDay
import logging
import pandas as pd
import pathlib
import requests
import xlrd
import zipfile

from urllib.parse import urlsplit, parse_qs, urlunsplit, urljoin
from xlrd import open_workbook, xldate_as_tuple

logger = logging.getLogger(__name__)


def download_from_secure_id(
    secure_id: str,
    brand: str,
    path: pathlib.Path,
    base_url="https://secmail.bankofamerica.com",
):
    password = {
        "ghorel@lmcg.com": "v4vdMvH9Qe9t",
        "nyops@lmcg.com": "Serenitas21",
    }
    payload = {}
    with requests.Session() as session:
        r = session.get(
            urljoin(base_url, "formpostdir/securereader"),
            params={"id": secure_id, "brand": brand},
            verify="secmail-bankofamerica-com-chain.pem",
        )
        soup = BeautifulSoup(r.content, features="lxml")
        form = soup.find(id="dialog")
        if "messagenotfound" in form["action"]:
            raise ValueError("message not found")
        for inp in form.find_all("input"):
            payload[inp["name"]] = inp["value"]
        payload["dialog:password"] = password[payload["dialog:username"]]
        r = session.post(base_url + form["action"], data=payload)
        soup = BeautifulSoup(r.content, features="lxml")
        form = soup.find(id="readTB")
        payload = {
            "readTB": "readTB",
            "readTB:downloadZipButton": "readTB:downloadZipButton",
        }
        for inp in form.find_all("input"):
            if "ViewState" in inp["name"]:
                payload["javax.faces.ViewState"] = inp["value"]
        r = session.post(urljoin(base_url, "securereader/read.jsf"), data=payload)
        if r.headers["content-type"] == "application/octet-stream":
            with zipfile.ZipFile(BytesIO(r.content)) as z:
                for f in z.namelist():
                    if not f.endswith("html") and not (path / f).exists():
                        z.extract(f, path=path)


paths = {
    "Serenitas": ["NYops", "Margin Calls Baml"],
    "BowdSt": ["BowdoinOps", "Margin BoA"],
    "Selene": ["SeleneOps", "Margin BoA"],
}


def read_secure_message(msg, dest, logger):
    soup = BeautifulSoup(msg.body, features="lxml")
    a = soup.find("a")
    url = urlsplit(a["href"])
    query = parse_qs(url.query)
    base_url = urlunsplit(url[:2] + ("",) * 3)
    try:
        download_from_secure_id(query["id"][0], query["brand"][0], dest, base_url)
    except ValueError as e:
        if logger:
            logger.error(e)


def download_files(em, d=None, count=20, *, fund="Serenitas", **kwargs):
    DATA_DIR = DAILY_DIR / fund / "BoA_reports"
    if fund not in paths:
        return
    emails = em.get_msgs(path=paths[fund], count=count)

    for msg in emails:
        match msg.sender.name:
            case "us_otc_client_valuation@baml.com" | "us_otc_client_valuation@bofa.com" if msg.body.body_type == "HTML":
                read_secure_message(msg, DATA_DIR, logger)

            case "us_otc_client_valuation@baml.com" | "us_otc_client_valuation@bofa.com" | "bank_of_america_collateral_operations@bankofamerica.com":
                for attach in msg.attachments:
                    if attach.name.endswith("xls") or attach.name.endswith("pdf"):
                        p = DATA_DIR / attach.name
                        if not p.exists():
                            p.write_bytes(attach.content)
            case _:
                continue


def load_excel_old(fname):
    wb = open_workbook(fname)
    s = wb.sheet_by_index(0)
    headers = s.row_values(6, 0)
    i = 7
    rows = []
    while s.cell(i, 0).value != "":
        r = []
        for e in s.row_slice(i, 0):
            if e.ctype == xlrd.XL_CELL_DATE:
                r.append(pd.Timestamp(*xldate_as_tuple(e.value, wb.datemode)))
            else:
                r.append(e.value)
        rows.append(r)
        i += 1
    df = pd.DataFrame.from_records(rows, columns=headers)
    for col in ["Original Notional", "Notional"]:
        df[col] = pd.to_numeric(df[col].str.replace(",", ""))
    return df


def load_excel(fname):
    wb = open_workbook(fname)
    s = wb.sheet_by_index(0)
    headers = s.row_values(13, 0)
    i = 19
    rows = []
    while s.cell(i, 0).ctype != xlrd.XL_CELL_DATE:
        content = s.cell(i, 0).value
        if content.startswith("NOP"):
            break
        if (
            content == ""
            or content.startswith("Credit")
            or content.startswith("FX")
            or content.startswith("Unspecified")
            or content.startswith("Equity - Option")
        ):
            i += 1
            continue
        r = []
        for e in s.row_slice(i, 0):
            if e.ctype == xlrd.XL_CELL_DATE:
                r.append(pd.Timestamp(*xldate_as_tuple(e.value, wb.datemode)))
            else:
                r.append(e.value)
        rows.append(r)
        i += 1
    df = pd.DataFrame.from_records(rows, columns=headers)
    if not df.empty:
        df["fx"] = df[["Market Value Date", "Ccy1"]].apply(lambda x: get_fx(*x), axis=1)
        df["Market Value Amount"] /= df["fx"]
    df = df.rename(
        columns={
            "Contract ID ": "Trade ID",
            "Market Value Amount": "local_nav",
            "Market Value Amount in CA Ccy": "base_nav",
            "Trade Date ": "Trade Date",
            "Cpty Independent Amount": "ia",
        }
    )
    df[["local_nav", "base_nav"]] *= -1.0
    return df


def collateral(d, dawn_trades, *, fund="Serenitas", **kwargs):
    match fund:
        case "Serenitas":
            tag = "TSLP"
        case "BowdSt":
            tag = "TLLC"
        case "Selene":
            tag = "INC"
    report_date = d + BDay()
    REPORTS_DIR = DAILY_DIR / fund / "BoA_reports"
    try:
        fname = next(
            REPORTS_DIR.glob(f"Collat_LMCG_INVESTMEN{tag}_CSA_{report_date:%m%d%Y}_*")
        )
    except StopIteration:
        raise ValueError(f"no collateral data for date {report_date}")
    df = pd.read_excel(fname, skiprows=6, skipfooter=6)
    if df.empty:
        collateral = 0
        logger.warning("empty collateral file")
    else:
        df = df.drop(0, axis=0)
        try:
            collateral = float(df.Notional)
        except TypeError:
            collateral = df.Notional.sum()
    try:
        fname = next(
            REPORTS_DIR.glob(f"301__LMCG_INVESTMEN{tag}_CSA_{report_date:%m%d%Y}_*")
        )
    except StopIteration:
        raise ValueError(f"no trade-level data for date {report_date}")
    df = load_excel(fname)
    df = df[["Trade ID", "base_nav", "ia"]]
    df = df.merge(
        dawn_trades.drop("ia", axis=1),
        how="left",
        left_on="Trade ID",
        right_on="cpty_id",
    )
    missing_ids = df.loc[df.cpty_id.isnull(), "Trade ID"]
    if not missing_ids.empty:
        raise ValueError(f"{missing_ids.tolist()} not in the database")
    df = df[["folder", "base_nav", "ia"]]
    # we put all the FX collateral in TCSH for now
    df.loc[df.folder == "M_CSH_CASH", "folder"] = "TCSH"
    df = df.groupby("folder").sum()
    df = (df["ia"] - df["base_nav"]).to_frame(name="Amount")
    df["Currency"] = "USD"
    df = df.reset_index()
    df.columns = ["Strategy", "Amount", "Currency"]
    df.Amount *= -1
    df.loc[df.Strategy == "M_CSH_CASH", "Strategy"] = "TCSH"
    df.loc[len(df.index)] = ["M_CSH_CASH", -collateral - df.Amount.sum(), "USD"]
    df["date"] = d
    return df.set_index("Strategy")