# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.


import abc
import time
import datetime
import importlib
from pathlib import Path
from typing import Type, Iterable
from concurrent.futures import ProcessPoolExecutor

import pandas as pd
from tqdm import tqdm
from loguru import logger
from joblib import Parallel, delayed
from qlib.utils import code_to_fname


class BaseCollector(abc.ABC):
    CACHE_FLAG = "CACHED"
    NORMAL_FLAG = "NORMAL"

    DEFAULT_START_DATETIME_1D = pd.Timestamp("2000-01-01")
    DEFAULT_START_DATETIME_1MIN = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6 - 1)).date()
    DEFAULT_END_DATETIME_1D = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)).date()
    DEFAULT_END_DATETIME_1MIN = DEFAULT_END_DATETIME_1D

    INTERVAL_1min = "1min"
    INTERVAL_1d = "1d"

    def __init__(
        self,
        save_dir: [str, Path],
        start=None,
        end=None,
        interval="1d",
        max_workers=1,
        max_collector_count=2,
        delay=0,
        check_data_length: int = None,
        limit_nums: int = None,
    ):
        """

        Parameters
        ----------
        save_dir: str
            instrument save dir
        max_workers: int
            workers, default 1; Concurrent number, default is 1; when collecting data, it is recommended that max_workers be set to 1
        max_collector_count: int
            default 2
        delay: float
            time.sleep(delay), default 0
        interval: str
            freq, value from [1min, 1d], default 1d
        start: str
            start datetime, default None
        end: str
            end datetime, default None
        check_data_length: int
            check data length, if not None and greater than 0, each symbol will be considered complete if its data length is greater than or equal to this value, otherwise it will be fetched again, the maximum number of fetches being (max_collector_count). By default None.
        limit_nums: int
            using for debug, by default None
        """
        self.save_dir = Path(save_dir).expanduser().resolve()
        self.save_dir.mkdir(parents=True, exist_ok=True)

        self.delay = delay
        self.max_workers = max_workers
        self.max_collector_count = max_collector_count
        self.mini_symbol_map = {}
        self.interval = interval
        self.check_data_length = max(int(check_data_length) if check_data_length is not None else 0, 0)

        self.start_datetime = self.normalize_start_datetime(start)
        self.end_datetime = self.normalize_end_datetime(end)

        self.instrument_list = sorted(set(self.get_instrument_list()))

        if limit_nums is not None:
            try:
                self.instrument_list = self.instrument_list[: int(limit_nums)]
            except Exception as e:
                logger.warning(f"Cannot use limit_nums={limit_nums}, the parameter will be ignored")

    def normalize_start_datetime(self, start_datetime: [str, pd.Timestamp] = None):
        return (
            pd.Timestamp(str(start_datetime))
            if start_datetime
            else getattr(self, f"DEFAULT_START_DATETIME_{self.interval.upper()}")
        )

    def normalize_end_datetime(self, end_datetime: [str, pd.Timestamp] = None):
        return (
            pd.Timestamp(str(end_datetime))
            if end_datetime
            else getattr(self, f"DEFAULT_END_DATETIME_{self.interval.upper()}")
        )

    @abc.abstractmethod
    def get_instrument_list(self):
        raise NotImplementedError("rewrite get_instrument_list")

    @abc.abstractmethod
    def normalize_symbol(self, symbol: str):
        """normalize symbol"""
        raise NotImplementedError("rewrite normalize_symbol")

    @abc.abstractmethod
    def get_data(
        self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp
    ) -> pd.DataFrame:
        """get data with symbol

        Parameters
        ----------
        symbol: str
        interval: str
            value from [1min, 1d]
        start_datetime: pd.Timestamp
        end_datetime: pd.Timestamp

        Returns
        ---------
            pd.DataFrame, "symbol" and "date"in pd.columns

        """
        raise NotImplementedError("rewrite get_timezone")

    def sleep(self):
        time.sleep(self.delay)

    def _simple_collector(self, symbol: str):
        """

        Parameters
        ----------
        symbol: str

        """
        self.sleep()
        df = self.get_data(symbol, self.interval, self.start_datetime, self.end_datetime)
        _result = self.NORMAL_FLAG
        if self.check_data_length > 0:
            _result = self.cache_small_data(symbol, df)
        if _result == self.NORMAL_FLAG:
            self.save_instrument(symbol, df)
        return _result

    def save_instrument(self, symbol, df: pd.DataFrame):
        """save instrument data to file

        Parameters
        ----------
        symbol: str
            instrument code
        df : pd.DataFrame
            df.columns must contain "symbol" and "datetime"
        """
        if df is None or df.empty:
            logger.warning(f"{symbol} is empty")
            return

        symbol = self.normalize_symbol(symbol)
        symbol = code_to_fname(symbol)
        instrument_path = self.save_dir.joinpath(f"{symbol}.csv")
        df["symbol"] = symbol
        if instrument_path.exists():
            _old_df = pd.read_csv(instrument_path)
            df = pd.concat([_old_df, df], sort=False)
        df.to_csv(instrument_path, index=False)

    def cache_small_data(self, symbol, df):
        if len(df) < self.check_data_length:
            logger.warning(f"the number of trading days of {symbol} is less than {self.check_data_length}!")
            _temp = self.mini_symbol_map.setdefault(symbol, [])
            _temp.append(df.copy())
            return self.CACHE_FLAG
        else:
            if symbol in self.mini_symbol_map:
                self.mini_symbol_map.pop(symbol)
            return self.NORMAL_FLAG

    def _collector(self, instrument_list):
        error_symbol = []
        res = Parallel(n_jobs=self.max_workers)(
            delayed(self._simple_collector)(_inst) for _inst in tqdm(instrument_list)
        )
        for _symbol, _result in zip(instrument_list, res):
            if _result != self.NORMAL_FLAG:
                error_symbol.append(_symbol)
        print(error_symbol)
        logger.info(f"error symbol nums: {len(error_symbol)}")
        logger.info(f"current get symbol nums: {len(instrument_list)}")
        error_symbol.extend(self.mini_symbol_map.keys())
        return sorted(set(error_symbol))

    def collector_data(self):
        """collector data"""
        logger.info("start collector data......")
        instrument_list = self.instrument_list
        for i in range(self.max_collector_count):
            if not instrument_list:
                break
            logger.info(f"getting data: {i+1}")
            instrument_list = self._collector(instrument_list)
            logger.info(f"{i+1} finish.")
        for _symbol, _df_list in self.mini_symbol_map.items():
            _df = pd.concat(_df_list, sort=False)
            if not _df.empty:
                self.save_instrument(_symbol, _df.drop_duplicates(["date"]).sort_values(["date"]))
        if self.mini_symbol_map:
            logger.warning(f"less than {self.check_data_length} instrument list: {list(self.mini_symbol_map.keys())}")
        logger.info(f"total {len(self.instrument_list)}, error: {len(set(instrument_list))}")


class BaseNormalize(abc.ABC):
    def __init__(self, date_field_name: str = "date", symbol_field_name: str = "symbol", **kwargs):
        """

        Parameters
        ----------
        date_field_name: str
            date field name, default is date
        symbol_field_name: str
            symbol field name, default is symbol
        """
        self._date_field_name = date_field_name
        self._symbol_field_name = symbol_field_name
        self.kwargs = kwargs
        self._calendar_list = self._get_calendar_list()

    @abc.abstractmethod
    def normalize(self, df: pd.DataFrame) -> pd.DataFrame:
        # normalize
        raise NotImplementedError("")

    @abc.abstractmethod
    def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
        """Get benchmark calendar"""
        raise NotImplementedError("")


class Normalize:
    def __init__(
        self,
        source_dir: [str, Path],
        target_dir: [str, Path],
        normalize_class: Type[BaseNormalize],
        max_workers: int = 16,
        date_field_name: str = "date",
        symbol_field_name: str = "symbol",
        **kwargs,
    ):
        """

        Parameters
        ----------
        source_dir: str or Path
            The directory where the raw data collected from the Internet is saved
        target_dir: str or Path
            Directory for normalize data
        normalize_class: Type[YahooNormalize]
            normalize class
        max_workers: int
            Concurrent number, default is 16
        date_field_name: str
            date field name, default is date
        symbol_field_name: str
            symbol field name, default is symbol
        """
        if not (source_dir and target_dir):
            raise ValueError("source_dir and target_dir cannot be None")
        self._source_dir = Path(source_dir).expanduser()
        self._target_dir = Path(target_dir).expanduser()
        self._target_dir.mkdir(parents=True, exist_ok=True)
        self._date_field_name = date_field_name
        self._symbol_field_name = symbol_field_name
        self._end_date = kwargs.get("end_date", None)
        self._max_workers = max_workers
        self.interval = kwargs.get("interval", "1d")

        self._normalize_obj = normalize_class(
            date_field_name=date_field_name, symbol_field_name=symbol_field_name, **kwargs
        )

    def format_data(self, df: pd.DataFrame):
        if self.interval == "1d":
            try:
                pd.to_datetime(df.iloc[-1]["date"], format="%Y-%m-%d", errors="raise")
            except Exception:
                df = df.iloc[:-1]
        return df

    def _executor(self, file_path: Path):
        file_path = Path(file_path)

        # some symbol_field values such as TRUE, NA are decoded as True(bool), NaN(np.float) by pandas default csv parsing.
        # manually defines dtype and na_values of the symbol_field.
        default_na = pd._libs.parsers.STR_NA_VALUES  # pylint: disable=I1101
        symbol_na = default_na.copy()
        symbol_na.remove("NA")
        columns = pd.read_csv(file_path, nrows=0).columns
        df = pd.read_csv(
            file_path,
            dtype={self._symbol_field_name: str},
            keep_default_na=False,
            na_values={col: symbol_na if col == self._symbol_field_name else default_na for col in columns},
        )
        df = self.format_data(df=df)

        if not df.empty:
            # NOTE: It has been reported that there may be some problems here, and the specific issues will be dealt with when they are identified.
            df = self._normalize_obj.normalize(df)
            if df is not None and not df.empty:
                if self._end_date is not None:
                    _mask = pd.to_datetime(df[self._date_field_name]) <= pd.Timestamp(self._end_date)
                    df = df[_mask]
                df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
        else:
            logger.warning(f"{file_path.stem} source data is empty and will not undergo normalization processing.")

    def normalize(self):
        logger.info("normalize data......")

        with ProcessPoolExecutor(max_workers=self._max_workers) as worker:
            file_list = list(self._source_dir.glob("*.csv"))
            with tqdm(total=len(file_list)) as p_bar:
                for _ in worker.map(self._executor, file_list):
                    p_bar.update()


class BaseRun(abc.ABC):
    def __init__(self, source_dir=None, normalize_dir=None, max_workers=1, interval="1d"):
        """

        Parameters
        ----------
        source_dir: str
            The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source"
        normalize_dir: str
            Directory for normalize data, default "Path(__file__).parent/normalize"
        max_workers: int
            Concurrent number, default is 1; Concurrent number, default is 1; when collecting data, it is recommended that max_workers be set to 1
        interval: str
            freq, value from [1min, 1d], default 1d
        """
        if source_dir is None:
            source_dir = Path(self.default_base_dir).joinpath("source")
        self.source_dir = Path(source_dir).expanduser().resolve()
        self.source_dir.mkdir(parents=True, exist_ok=True)

        if normalize_dir is None:
            normalize_dir = Path(self.default_base_dir).joinpath("normalize")
        self.normalize_dir = Path(normalize_dir).expanduser().resolve()
        self.normalize_dir.mkdir(parents=True, exist_ok=True)

        self._cur_module = importlib.import_module("collector")
        self.max_workers = max_workers
        self.interval = interval

    @property
    @abc.abstractmethod
    def collector_class_name(self):
        raise NotImplementedError("rewrite collector_class_name")

    @property
    @abc.abstractmethod
    def normalize_class_name(self):
        raise NotImplementedError("rewrite normalize_class_name")

    @property
    @abc.abstractmethod
    def default_base_dir(self) -> [Path, str]:
        raise NotImplementedError("rewrite default_base_dir")

    def download_data(
        self,
        max_collector_count=2,
        delay=0,
        start=None,
        end=None,
        check_data_length: int = None,
        limit_nums=None,
        **kwargs,
    ):
        """download data from Internet

        Parameters
        ----------
        max_collector_count: int
            default 2
        delay: float
            time.sleep(delay), default 0
        start: str
            start datetime, default "2000-01-01"
        end: str
            end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))``
        check_data_length: int
            check data length, if not None and greater than 0, each symbol will be considered complete if its data length is greater than or equal to this value, otherwise it will be fetched again, the maximum number of fetches being (max_collector_count). By default None.
        limit_nums: int
            using for debug, by default None

        Examples
        ---------
            # get daily data
            $ python collector.py download_data --source_dir ~/.qlib/instrument_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
            # get 1m data
            $ python collector.py download_data --source_dir ~/.qlib/instrument_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1m
        """

        _class = getattr(self._cur_module, self.collector_class_name)  # type: Type[BaseCollector]
        _class(
            self.source_dir,
            max_workers=self.max_workers,
            max_collector_count=max_collector_count,
            delay=delay,
            start=start,
            end=end,
            interval=self.interval,
            check_data_length=check_data_length,
            limit_nums=limit_nums,
            **kwargs,
        ).collector_data()

    def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol", **kwargs):
        """normalize data

        Parameters
        ----------
        date_field_name: str
            date field name, default date
        symbol_field_name: str
            symbol field name, default symbol

        Examples
        ---------
            $ python collector.py normalize_data --source_dir ~/.qlib/instrument_data/source --normalize_dir ~/.qlib/instrument_data/normalize --region CN --interval 1d
        """
        _class = getattr(self._cur_module, self.normalize_class_name)
        yc = Normalize(
            source_dir=self.source_dir,
            target_dir=self.normalize_dir,
            normalize_class=_class,
            max_workers=self.max_workers,
            date_field_name=date_field_name,
            symbol_field_name=symbol_field_name,
            **kwargs,
        )
        yc.normalize()